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Kalman filter for mobile-robot attitude estimation: Novel optimized and adaptive solutions
Abstract
This paper proposes two novel approaches to estimate accurately mobile robot attitudes based on the fusion of low-cost accelerometers and gyroscopes. The first part of the paper demonstrates the use of a special test bench that both enables simulations of various dynamic behaviors of wheeled robots and measures their real attitude angles along with the raw sensor data. These measurements are applied in a simulation environment and we outline an offline optimization of Kalman filter parameters. The second part of the paper introduces a novel adaptive Kalman filter structure that modifies the noise covariance values according to the system dynamics. The instantaneous dynamics are characterized regarding the magnitudes of both the instantaneous vibration and the external acceleration. The proposed adaptive solution measures these magnitudes and utilizes fuzzy-logic to modify the filter parameters in real time. The results show that the adaptive filter improves the overall filter convergence by a remarkable 10.9% over using the optimized Kalman filter, thereby demonstrating its efficacy as an accurate and robust attitude filter. The proposed filter performances are also benchmarked against other common methods indicating that the flexibility of the developed adaptive filter allowed it to compete and even outperform the benchmark filters.
... Intelligent manufacturing and logistics have driven significant advancements in mobile robot technology in recent years. The demand for autonomous mobile robots in smart factories has grown substantially, necessitating robots that are flexible, adaptable, and safe to navigate complex work environments [1][2][3][4][5]. ...
... Based on the DH parameters the forward kinematics can be calculated through equation (1): (1) where T denotes the homogenous transformation from frame i to frame j and ...
... Sensor fusion is among the most commonly studied techniques for the acquisition of accurate information using a set of measurements from different sources [14][15][16]. Filtering-based applications either making use of Kalman filters [17,18] or particle filters [19] can be counted among the widely accepted methods used for sensor fusion. Specifically, Extended Kalman Filter (EKF) has been shown to combine multiple sensor data yielding reliable measurements [20] or for pairing a machine learning-based method with another estimation technique in an accurate way [21][22][23]. ...
... The identity given in (18) will yield an exponentially decaying error dynamics by appropriate selection of the coefficients (i.e., K 1 > 0 & K 2 > 0). Substituting the error in (17) to the dynamics in (18), one can extract the desired acceleration as follows: ...
Mecanum wheeled robots can exhibit serious slippage problems because of the discontinuous contact between the wheels and the ground which negatively influences the overall navigation quality. Addressing this problem, the aim of this paper is to demonstrate how a learning-based method can be used for the estimation of the drifting error from multiple sensors with distinct measurement types. Here, a recurrent neural network (RNN)-based drift compensation algorithm is proposed for the estimation of the positioning drift. In order to improve the positioning performance in dead reckoning the estimated drift is used within the real-time control loop for proper modification of the motion trajectory. During the training phase, the data acquired from the acceleration sensors attached to the robot chassis and the encoders of the wheels of the robot are used as the main features to train a gated recurrent unit-based RNN. The drift estimator is trained using the computer-generated reference position data, and the response position data which is measured using an optoelectronic motion tracking device. The performance of the proposed learning-based drift estimation and control algorithm is validated through a series of experiments. The responses obtained from the experiments are graphically illustrated and the improvements in the positioning performances are numerically evaluated. The results obtained from the experiments illustrate the effective performance of the proposed algorithm by considerably decreasing the positioning errors.
... To quantify vibration magnitudes, the peak or RMS of the translational or rotational velocity or acceleration spectrum could be used. In the case of walking robots, it is worth considering the highest of the three axis vibration magnitudes (see the detailed derivation in [33]). ...
... In Equations (8) and (9) v xb ( f ), v yb ( f ) and v zb ( f ) are the spectrums of translational or rotational velocities or accelerations of the robot body in the x, y and z directions as a function of frequency ( f ), respectively. The determination of these components is discussed in [33]. ...
This paper presents a novel model-based structural optimization approach for the efficient electromechanical development of hexapod robots. First, a hexapod-design-related analysis of both optimization objectives and relevant parameters is conducted based on the derived dynamical model of the robot. A multi-objective optimization goal is proposed, which minimizes energy consumption, unwanted body motion and differences between joint torques. Then, an optimization framework is established, which utilizes a sophisticated strategy to handle the optimization problems characterized by a large set of parameters. As a result, a satisfactory result is efficiently obtained with fewer iterations. The research determines the optimal parameter set for hexapod robots, contributing to significant increases in a robot’s walking range, suppressed robot body vibrations, and both balanced and appropriate motor loads. The modular design of the proposed simulation model also offers flexibility, allowing for the optimization of other electromechanical properties of hexapod robots. The presented research focuses on the mechatronic design of the Szabad(ka)-III hexapod robot and is based on the previously validated Szabad(ka)-II hexapod robot model.
... While gyroscopes are prone to drift (being rate sensors), they perform best at high angular rates where accelerometer and magnetometer readings might lag or yield noisy measurements. This is exploited in modern estimation methods through sensor fusion techniques such as high-speed gradient-descent [10], high-accuracy Kalman filtering [11,13], and magnetic-disturbance rejecting extended complementary filtering [12,14]. The addition of gyroscopes as common sensors in biologging tags therefore offers opportunities to improve orientation accuracy estimates, which can not only enhance dead-reckoning, but has ramifications for gait and behavioral dynamics analyses as well. ...
... 13: Results from the uncertainty analysis experiment. TOP: Linear regression results for all four turbines, with best-fit lines (black) of the raw data (shape markers) flanked by 95% confidence interval bounds (shaded regions). ...
Marine mammal monitoring has seen improvements in the last few decades with advances made to both the monitoring hardware and post-processing computation methods. The addition of tag-based hydrophones, Fastloc GPS units, and an ever-increasing array of IMU sensors, coupled with the use of energetics proxies such as Overall Dynamic Body Acceleration (ODBA), has led to new insights into marine mammal swimming behavior that would not be possible using traditional secondary-observer methods. However, these advances have primarily been focused on and implemented in wild animal tracking, with less attention paid to the managed environment. This is a particularly important gap, as the cooperative nature of managed animals allows for research on swimming kinematics and energetics behavior with an intricacy that is difficult to achieve in the wild. While proxy-based methods are useful for relative inter-or-intra-animal comparisons, they are not robust enough for absolute energetics estimates for the animals, which can limit our understanding of their metabolic patterns. Proxies such as ODBA are based on filtered on-animal IMU data, and measure the aggregate high-pass acceleration as an estimate for the magnitude of the animal’s activity level at a given point in time. Depending on its body structure and locomotive gait, tag placement on the animal and the specific filtering techniques used can significantly impact the results. Any relation made to energetics is then strictly a mapping: a relation that may apply well to an individual or group under specific experimental conditions, but is not generalizable. To address this gap, this dissertation presents new tag-based hardware and data processing methods for persistently estimating cetacean swimming kinematics and energetics, which are functional in both managed and wild settings. Unfortunately, localization techniques for managed environments have not been thoroughly explored, so a new animal tracking method is required to spatially contextualize information on swimming behavior. State-of-the-art wild cetacean localization operates via sparse GPS updates upon animal surfacings, and can be paired with biologging-tag-based odometry for a continuous track. Such an approach is hindered by the structure and scale of managed environments: GPS suffers from increased error near and within buildings, and current odometry methods are insufficiently precise for habitat scales where locations of interest might be separated by meters, rather than kilometers (such as in the wild). There is then a need for a tracking method that uses an alternate source of absolute animal locations that can achieve the high precision necessary for meaningful results given the spatial scale. To this end, this dissertation presents a novel animal localization framework, based on tracking and sensor filtering techniques from the field of robotics that have been tailored for use in this setting. Overall, this research targets two main gaps: 1) the lack of persistent, absolute estimates of animal swimming energetics and kinematics, and 2) the lack of a robust, precise localization method for managed cetaceans. To address these gaps, the hardware and animal tracking methods developed to enable the rest of the dissertation are first defined. Next, a physics-based approach to directly monitor cetacean swimming energetics is both presented and implemented to study animal propulsion patterns under varying effort conditions. Finally, a high-fidelity 3D monitoring framework is introduced for tracking institutionally-managed cetaceans, and is applied alongside the energetics estimation method to provide a first look at the potential of spatially-contextualized animal monitoring.
... It is worth mentioning that angular velocity is observable only given the knowledge of attitude. In turn, in order to obtain attitude information it is sufficient to acquire at least two vectorial measurements at the rigid-body using, for example, an IMU module [2,6,[13][14][15]]. An early solution presented a full state observer for rigid-body motion [1]. ...
... Q is the unit-quaternion error, Q d is the desired unit-quaternion, Q a = [q a0 , q a ] is the auxiliary unit-quaternion,Q a = [q a0 ,q a ] = Q −1 a Q c , RQ c is the attitude control error, RQ a is the attitude auxiliary error, e a = 1 −q a0 , E a = 1 2 ln δ a +ea/ξa δa−ea/ξa is the transformed error, W c and β a are the correction factors, andΩ d is the derivative of the desired angular velocity. Additionally, ξ a is the PPF defined in (14) with ξ 0 a > e a (0), and k w , k c , k β , and δ a = δ a > e a (0) are positive constants. ...
This paper proposes a novel unit-quaternion observer-based controller for attitude tracking (attitude and angular velocity) with guaranteed transient and steady-state performance. The proposed approach is computationally cheap and can operate based on measurements provided, for instance by a typical low-cost inertial measurement unit (IMU) or magnetic, angular rate, and gravity (MARG) sensor without the knowledge of angular velocity. First, an observer evolved on is developed guaranteeing asymptotic stability of the closed loop error signals starting from any initial condition. Afterwards, the observer is combined with the proposed controller such that the observer-based controller ensures asymptotic stability of the closed loop error signals starting from any initial condition. Simulation performed in discrete form at low sampling rate reveals the robustness and effectiveness of the proposed approach. Keywords: Observer-based controller, attitude, estimation, control, MARG, IMU, asymptotic stability.
... It is worth mentioning that angular velocity is observable only given the knowledge of attitude. In turn, in order to obtain attitude information it is sufficient to acquire at least two vectorial measurements at the rigid-body using, for example, an IMU module [2,6,[13][14][15]]. An early solution presented a full state observer for rigid-body motion [1]. ...
... Q is the unit-quaternion error, Q d is the desired unit-quaternion, Q a = [q a0 , q a ] is the auxiliary unit-quaternion,Q a = [q a0 ,q a ] = Q −1 a Q c , RQ c is the attitude control error, RQ a is the attitude auxiliary error, e a = 1 −q a0 , E a = 1 2 ln δ a +ea/ξa δa−ea/ξa is the transformed error, W c and β a are the correction factors, andΩ d is the derivative of the desired angular velocity. Additionally, ξ a is the PPF defined in (14) with ξ 0 a > e a (0), and k w , k c , k β , and δ a = δ a > e a (0) are positive constants. ...
This paper proposes a novel unit-quaternion observer-based controller for attitude tracking (attitude and angular velocity) with guaranteed transient and steady-state performance. The proposed approach is computationally cheap and can operate based on measurements provided, for instance by a typical low-cost inertial measurement unit (IMU) or magnetic, angular rate, and gravity (MARG) sensor without the knowledge of angular velocity. First, an observer evolved on S 3 × R 3 is developed guaranteeing asymptotic stability of the closed loop error signals starting from any initial condition. Afterwords, the observer is combined with the proposed controller such that the observer-based controller ensures asymptotic stability of the closed loop error signals starting from any initial condition. Simulation performed in discrete form at low sampling rate reveals the robustness and effectiveness of the proposed approach.
... As commented throughout this work, the Kalman filter has been combined with other techniques such as optimization and fuzzy logic. The study in [62] tests both combinations. First, the authors apply particle swarm optimization to the covariance values for the KF noise using mean square error as a target function in order to obtain an optimized performance of the filter. ...
... Mobile robot[62]. ...
Due to its widespread application in the robotics field, the Kalman filter has received increased attention from researchers. This work reviews some of the modifications conducted on to this algorithm over the last years. Problems such as the consistency, convergence, and accuracy of the filter are also dealt with. Sixty years after its creation, the Kalman filter is still used in autonomous navigation processes, robot control, and trajectory tracking, among other activities. The filter is not only restricted to robotics but is also present in different fields, such as economics and medicine. In addition, the characteristics of each modification on this filter are analyzed and compared.
... The accelerometer readings are very sensitive to external noise [16], [17]. Disturbance forces acting on a quadcopter can therefore greatly affect the integrity of the data from the accelerometer. ...
... The prediction can be written as (12) and (13). The update part entails the computation of the measurement residual (14), the Kalman gain (15), the updated state estimate (16), and the updated error covariance (17). The predictionx − k predicts the current state using the state estimate from the previous time step and the current input u k−1 . ...
... Therefore, it is necessary to study and develop a novel mechanism for correct attitude control, as described earlier, as well as attach direct sensors suitable for agricultural machinery. Studies on attitude control are being actively conducted in various fields, such as in the fields of automobiles, drones (Guo, Jiang & Zhang, 2017), mobile robots (Odry, Fuller, Rudas & Odry, 2018), telemetry and satellites (Du, Li & Qian, 2011;Kim, Ju, Kim & Son, 2019b). Substantial efforts have been made to control attitude accurately with the required attitude adopting various sensors (Zhu et al. (2019) ;Jiang, Hu & Friswell (2016); Zhou & Zhou (2020)). ...
... For Kalman filter system modeling, we referred to the 1 algorithm presented in (Odry, Fuller, Rudas & Odry, 2018). ...
Korean cabbage harvesting lacks mechanization and depends on human power; thus, conducting research on Korean cabbage harvesters is of immense importance. Although these harvesters have been developed in various forms, they have not yet attained commercialization. Most Korean cabbage fields have slopes; thus there are several challenges, that can prevent accurate harvesting. Therefore, to address these challenges at the site, we adopt two cylinders in this study, develop a mechanism that enables attitude control of the cutting device, not driving platform body, to cope with slopes. By maintaining the level, angle, height of cutting, we can reduce loss and improve harvest performance. It is difficult to find examples where these mechanisms have been applied. For basic research, sensor fusion has been carried out based on the Kalman filter, which is commonly utilized for attitude control. The hydraulic cylinder was controlled using the data obtained for maintaining the attitude. Furthermore, field tests were conducted to validate this system, and the root mean square error (RMSE) was obtained and verified to quantitatively assess the presence or absence of attitude control. Therefore, the purpose of this study is to suggest a development direction for Korean cabbage harvesters via the proposed attitude control system.
... To integrate the aforementioned sensor data, Kalman-type filters are widely employed in navigation due to their stochastic framework and ability to handle noisy measurements [25]- [28]. The Kalman Filter (KF) provides a maximum likelihood estimate of the system's state vector based on available measurement data; however, it operates optimally only within linear systems. ...
This paper addresses the challenge of estimating the orientation, position, and velocity of a vehicle operating in three-dimensional (3D) space with six degrees of freedom (6-DoF). A Deep Learning-based Adaptation Mechanism (DLAM) is proposed to adaptively tune the noise covariance matrices of Kalman-type filters for the Visual-Inertial Navigation (VIN) problem, leveraging IMU-Vision-Net. Subsequently, an adaptively tuned Deep Learning Unscented Kalman Filter for 3D VIN (DeepUKF-VIN) is introduced to utilize the proposed DLAM, thereby robustly estimating key navigation components, including orientation, position, and linear velocity. The proposed DeepUKF-VIN integrates data from onboard sensors, specifically an inertial measurement unit (IMU) and visual feature points extracted from a camera, and is applicable for GPS-denied navigation. Its quaternion-based design effectively captures navigation nonlinearities and avoids the singularities commonly encountered with Euler-angle-based filters. Implemented in discrete space, the DeepUKF-VIN facilitates practical filter deployment. The filter's performance is evaluated using real-world data collected from an IMU and a stereo camera at low sampling rates. The results demonstrate filter stability and rapid attenuation of estimation errors, highlighting its high estimation accuracy. Furthermore, comparative testing against the standard Unscented Kalman Filter (UKF) in two scenarios consistently shows superior performance across all navigation components, thereby validating the efficacy and robustness of the proposed DeepUKF-VIN. Keywords: Deep Learning, Unscented Kalman Filter, Adaptive tuning, Estimation, Navigation, Unmanned Aerial Vehicle, Sensor-fusion.
... To integrate the aforementioned sensor data, Kalman-type filters are widely employed in navigation due to their stochastic framework and ability to handle noisy measurements [25]- [28]. The Kalman Filter (KF) provides a maximum likelihood estimate of the system's state vector based on available measurement data; however, it operates optimally only within linear systems. ...
This paper addresses the challenge of estimating the orientation, position, and velocity of a vehicle operating in three-dimensional (3D) space with six degrees of freedom (6-DoF). A Deep Learning-based Adaptation Mechanism (DLAM) is proposed to adaptively tune the noise covariance matrices of Kalman-type filters for the Visual-Inertial Navigation (VIN) problem, leveraging IMU-Vision-Net. Subsequently, an adaptively tuned Deep Learning Unscented Kalman Filter for 3D VIN (DeepUKF-VIN) is introduced to utilize the proposed DLAM, thereby robustly estimating key navigation components, including orientation, position, and linear velocity. The proposed DeepUKF-VIN integrates data from onboard sensors, specifically an inertial measurement unit (IMU) and visual feature points extracted from a camera, and is applicable for GPS-denied navigation. Its quaternion-based design effectively captures navigation non-linearities and avoids the singularities commonly encountered with Euler-angle-based filters. Implemented in discrete space, the DeepUKF-VIN facilitates practical filter deployment. The filter's performance is evaluated using real-world data collected from an IMU and a stereo camera at low sampling rates. The results demonstrate filter stability and rapid attenuation of estimation errors, highlighting its high estimation accuracy. Furthermore, comparative testing against the standard Unscented Kalman Filter (UKF) in two scenarios consistently shows superior performance across all navigation components, thereby validating the efficacy and robustness of the proposed DeepUKF-VIN.
... Since the parameter setting of the two covariance matrices involves finding a set of values that minimize the performance indicators of Kalman filter, the process of selecting the model error covariance matrix Q and the observation error covariance matrix R are optimization problems in essence. Odry et al. [11] used the Particle Swarm Optimization (PSO) algorithm to optimize the model error covariance matrix Q and the observation error covariance matrix R of the Kalman Filter (KF) and used the improved KF for the state estimation of mobile robots. Kaba et al. [12] have used an evolutionary algorithm to optimize the model error covariance matrix Q and the observation error covariance matrix R of the KF, and their results indicate that the optimized KF is effective for the state estimation of aircraft. ...
The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values.
... Stochastic navigation estimators can be linear and nonlinear. Linear-type stochastic estimators include Kalman filters (KFs) [85][86][87]. Nonlinear-type stochastic navigation estimators include extended Kalman filters (EKFs) [88] which require linearization around nominal point, unscented Kalman filters (UKFs), Particle filters (PFs) [89,90], and Lyapunov-based nonlinear complementary stochastic filters which use Stochastic Differential Equations (SDEs) and adopt Ito's or Stratonovich's integrals to mitigate noise stochasticity and address navigation nonlinearity [36,22,1]. Ito's approach addresses white noise, while Stratonovich's approach is applicable for colored noise [91,92]. ...
Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identification protocols, novel energy solutions, multi-sensor accurate perception and autonomous navigation, precise path planning, that guarantees collision avoidance, reliable trajectory control, and efficient data transfer within the UAV system. Moreover, special consideration must be given to electronic warfare threats prevention, detection, and mitigation, and the regulatory framework associated with UAV operations. This review presents the role and taxonomy of each UAV avionics system while covering shortcomings and benefits of available alternatives within each system. UAV communication systems, antennas, and location communication tracking are surveyed. Identification systems that respond to air-to-air or air-to-ground interrogating signals are presented. UAV classical and more innovative power sources are discussed. The rapid development of perception systems improves UAV autonomous navigation and control capabilities. The paper reviews common perception systems, navigation techniques, path planning approaches, obstacle avoidance methods, and tracking control. Modern electronic warfare uses advanced techniques and has to be counteracted by equally advanced methods to keep the public safe. Consequently, this work presents a detailed overview of common electronic warfare threats and state-of-the-art countermeasures and defensive aids. Furthermore, UAV safety occurrences are analyzed in the context of national regulatory framework and the certification process. Lastly, databus communication and standards for UAVs are reviewed as they enable efficient and fast real-time data transfer. Index Terms—Avionics systems, Unmanned Aerial Vehicles, navigation and control, regulation and certification, communication and energy, electronic warfare and identification.
... La demanda de robots móviles autónomos en fábricas inteligentes ha crecido sustancialmente, lo que requiere robots que sean flexibles, adaptables y seguros para navegar en entornos de trabajo complejos. [1][2][3][4][5] Para abordar los requisitos de tales entornos, la elección de ruedas para robots móviles es de suma importancia. Las ruedas omnidireccionales, equipadas con rodillos en sus bordes, resuelven las limitaciones de las ruedas tradicionales [6]. ...
... The soft machine is a typical flexible multibody system that usually undergoes Z. Liu zhuyongliu@sjtu.edu.cn 1 large overall motion and large structural deformation [2,3]. Flexible legged robots with good traversal and exploration capabilities in the field environment have attracted extensive attention in recent years [4][5][6][7]. Compared to traditional wheeled and crawler mobile robots, legged robots have discontinuous support during motion, as well as higher environmental adaptability and flexible mobility. ...
The purpose of this research is to present an alternative multibody dynamic model for soft robots and to analyze the intrinsic mechanism of motion. It is difficult to directly apply traditional robot modeling methods due to the large structural deformation of soft walking robots. This paper establishes the dynamic modeling of a soft robot system with contact/impact based on the corotational formulation of the special Euclidean group SE(2). The experiments are designed to verify the dynamic model of the robot. The history of the marked points on the robot prototype is measured in real time by an ARAMIS Adjustable Camera System. Based on the dynamic model, we conducted an in-depth analysis of the entire process through which the robot achieves directional walking utilizing complex friction characteristics. Notably, the robot’s kick-up phenomenon attracted our attention, and an analytical model for predicting the critical drive acceleration is proposed. The conditions and mechanisms of the robot’s kick-up are analyzed, and effective direction is provided for designing new drive laws. Finally, several sets of key parameters affecting the walking efficiency are analyzed using the multibody model, which can provide scientific guidance for the material selection and optimization of the robot. The presented dynamic modeling approach can be freely extended to other soft robots, which will provide valuable references for the design and analysis of soft robots.
... Fundamentally, the ZED 2i depth camera is a passive stereo camera without an active ranging appliance. This stereo device utilizes a binocular camera to generate 3D scene data, retrieves the disparity of the object and scene using a stereo matching algorithm, and in the end determinates the depth map according to the sensor parameters in millimeters (mm) [36][37][38][39][40]. ...
This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step in a series of robotic operations, where the intent is to detect and extract the charging socket on the vehicle’s body surface. The ZED 2i depth sensor was utilized for scene recording with artificial illumination. Later, the socket detection and extraction were accomplished using both simple image processing and morphological operations in an object extraction algorithm with tilt angles and centroid coordinates determination of the charging socket itself. The aim was to use well-known, simple, and proven image processing techniques in the proposed method to ensure both reliable and smooth functioning of the robot’s vision system in an industrial environment. The experiments demonstrated that the deployed algorithm both extracts the charging socket and determines the slope angles and socket coordinates successfully under various depth assessment conditions, with a detection rate of 94%.
... The heuristic particle swarm optimization (PSO) algorithm is proposed for the problem, since it does not rely on gradient information, the search is effectively guided for nonlinear stochastic systems, and has demonstrated better performance than other approaches [31], [32]. The setup of the algorithm is comprehensively discussed in our earlier works [33]. ...
... By continually combining a learning module with a prediction algorithm, this approach may be utilized to modify its performance. The authors of [25] present two unique Kalman filter-based techniques for reliably estimating mobile robot attitudes. The first approach optimizes Kalman filter parameters using the measurements in a simulation environment based on lowcost accelerometers and gyroscopes. ...
Clinical trials have been made transparent and accessible because of the widespread adoption of blockchain technology. Its distinctive characteristics, such as data immutability and transparency, could increase public trust in a fair and transparent manner among all stakeholders. However, blockchain systems cannot handle the requirement of processing huge volumes of data in real time. Scalability becomes a severe issue when implementing decentralized applications for clinical studies. With an abrupt expansion in the number of transaction exchanges happening consistently and the capital associated with those exchanges, there is an urgent demand for developers and users to know blockchain systems' performance limits to determine if requirements can be fulfilled; however, little is known about the prediction of blockchain system behaviors. This paper shows the feasibility of using machine learning technologies to predict the transaction throughput of blockchain-based systems in clinical trials. A learning to prediction model is proposed, in which the Kalman filter is used to predict the transaction throughput, and the Artificial Neural Network (ANN) is utilized to enhance the Kalman filter's prediction accuracy. A real dataset generated from a clinical trial testbed using Hyperledger Fabric is utilized to demonstrate the feasibility of the proposed approach. Moreover, we compare the Kalman filter with other learning modules, and the results indicate that the ANN performs best. Furthermore, we apply the proposed approach to different blockchain platforms, and the experiment results indicate the efficiency and universality of the designed approach.
... Attitude control studies are being actively conducted in various fields, such as automobiles, drones (Guo et al., 2017;Lotufo et al., 2019;Kim et al., 2019), mobile robots (Odry et al., 2018), satellites (Du et al., 2011), and the attitude stabilization control of spacecraft (Jiang et al., 2016). In addition, a study on multi-sensor-based attitude prediction was also conducted to accurately measure the attitude of agricultural vehicles on uneven terrain (Zhu et al., 2019). ...
Highlights
Developed the attitude control system of the cutting device to improve the accuracy of Korean cabbage harvesting.
The cutting device provides attitude control, allowing precise harvesting.
The attitude control algorithm was designed based on sensor fusion.
Field experiments were conducted to compare attitude maintenance and harvest success rates as the attitude control system of cutting device was with/without.
Abstract. Harvesting Korean cabbage requires precise cutting since the cutting method determines its quality. However, Korean cabbage fields have uneven surfaces and severe slopes, making it challenging to automatically harvest the crops. This unstructured environment hindered precise cutting, delaying the commercialization of the Korean cabbage harvester. Therefore, we designed an attitude control system for the cutting machine to improve the accuracy of cabbage harvesting. The proposed system controls the cutting level, angle, and height according to the field to improve the harvest performance. This study applied the Kalman filter-based sensor fusion to measure the cutting angle and height for pose estimation and attitude control. The attitude of the cutting device is estimated even in soil and mud by fusing a gyroscope, accelerometer, and linear potentiometer. Subsequently, the attitude is controlled by two hydraulic cylinders so that the cutting machine has the desired cutting angle and height. For the validation of the attitude control, the root mean square error (RMSE) of the cutting angle and height was calculated through experiments in a sloped environment. As a result, the proposed system reduced the RMSE for the cutting angle and height by 92% and 72%, respectively. Furthermore, field tests were performed on farmland with a Korean cabbage harvester to evaluate the harvesting performance of the attitude control system. The harvesting performance was quantitatively computed by scoring the cutting surface of the cabbage, and the performance improved from 56 to 89 points when the attitude control was applied to the cutting machine. Keywords: Attitude control, Automatic cabbage harvesting, Kalman filter, Korean cabbage harvester, Sensor fusion.
... Therefore, it is essential to develop algorithms capable of accurately predicting head orientation in VR and AR systems. In addition to AR and VR, orientation prediction plays a vital role in various practical applications, such as unmanned aerial vehicles [10], robotics [11], [12], and navigation systems [13]- [15]. ...
We propose an orientation prediction algorithm based on Kalman-like error compensation for virtual reality (VR) and augmented reality (AR) devices using measurements of an inertial measurement unit (IMU), which includes a tri-axial gyroscope and a tri-axial accelerometer. First, the initial prediction of the orientation is estimated by assuming linear movement. Then, to improve the prediction accuracy, the accuracies of previous predictions are taken into account by computing the orientation difference between the current orientation and previous prediction. Finally, we define a weight matrix to determine the optimal adjustments for predictions corresponding to a given orientation, which is obtained by minimizing the estimation errors based on the minimum mean square error (MMSE) criterion using Kalman-like error compensation. Experimental results demonstrate that the proposed algorithm exhibits higher orientation prediction accuracy compared with conventional algorithms on several open datasets.
... Robots with artificial intelligence are being utilized in a variety of sectors. The introduction of such novel intelligent instruments has permanently altered people's old working modes, substantially increasing human output and lowering production hazards (Odry et al. 2018). Mobile robots seem to be the most common type of robots in which sensors are used to assess their Abstract The mobile robot seems to be highly significant in the field of robotics. ...
The mobile robot seems to be highly significant in the field of robotics. In actuality, the efficiency of a mobile robot is necessary to be larger and larger. To fulfill the specified
performance objectives, the mobile robot must be able to adapt to various complicated situations by using its neural network. The Kalman filter is utilized to fuse multiple sensor
data, and the online motion planning approach in robot navigation based on enhanced by the construction of generalized VORONOI graph, as opposed to standard path planning
using sensor data fusion technique. The robot performs optimum path planning as well as obstacle avoidance in a complicated unknown environment through the construction
of the generalized VORONOI technique. In this research, a unique online motion planner for robot navigation is created in real-time applications to methodically explore unexpected
surroundings. The program uses sensory input by the sensor data fusion to determine an obstacle-free path from start to destination. It accomplishes this by computing the
Generalized Voronoi Graph (GVG) with free-space life and combining depth-first as well as breadth-first searching onthe GVG. Thus, the proposed construction of the VORNOI graph enables online motion planning for robot navigation.
... Consequently, these advantages make UGVs suitable for indoor 4,5 and outdoor 6,7 applications. However, the ground vehicles can be perturbed due to uncertainties in models, and description of the context of operation 8 , skidding and slipping 9 , noisy sensor's measurements, 10 , and failures in electromechanical components 11 , making the navigation and control of the platform a challenging task 12,13 . ...
This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.
... Additionally, between one and two hidden layers were tested, and finally, the number of neurons was varied according to the performance shown by the network. The network performance was measured by root-mean-square error (RMSE) which is described by Equation (4) and is a typical quality index for characterization of the performance [43,44]. The starting point was a minimum number determined by the geometric pyramid rule, and the number of neurons was gradually increased. ...
The construction of urban and transport indicators aims for a better diagnosis that enables technical and precise decision-making for the public administration or private investment. Therefore, it is common to make comparisons and observe which has better diagnosis results in a diversity of indexes and models. The present study made a comparative analysis of spatial models using artificial intelligence to estimate transport demand. To achieve this goal, the audit field was recollected in specific urban corridors to measure the indicators. A study case in Querétaro, an emergent city in the Mexican region known as El Bajío, is conducted. Two similar urban avenues in width and length and close to each other were selected to apply a group of spatial models, evaluating the avenues by segments and predicting the public transport demand. The resulting database was analyzed using Artificial Neural Networks. It displays specific indicators that have around 80% of correlations. The results facilitate the localization of the avenue segments with the most volume of activity, supporting interventions in urban renewal and sustainable transportation projects.
... More complex pendulums can be created, such as the 3D pendulum [1,2], -link pendulums [3], the Kapitza pendulum [4], or the Wilberforce pendulum [5]. Pendulums are crucial in modeling gait in both robotics [6][7][8] and biomechanics [9][10][11]. The simple pendulum is easy to fabricate, but it exhibits complex dynamics because of its nonlinear equations of motion. ...
Adaptive oscillators are a type of nonlinear oscillator that are capable of learning and storing information in plastic states. Here, a typical mechanical pendulum is modified to have an adjustable rod length to create a pendulum adaptive frequency oscillator. Since the resonance frequency of the pendulum is a function of the rod length, this allows the pendulum to learn and encode frequency information from an external source. An experimental pendulum adaptive frequency oscillator is designed and constructed, and its performance is compared to numerical simulations. This nonlinear pendulum was approximated as a Duffing oscillator through the method of multiple scales to determine the physical constants of the experiment by using a curve fit. Utilizing the pendulum adaptive frequency oscillator’s dynamics, this system is able to learn a resonance condition and store this information in the rod length. This causes the system to seek resonance, even with considerable nonlinearity. As pendulums can be used to harvest energy, this type of adaptation could be used to further exploit vibratory energy sources.
... Various factors measure the performance of a network. However, the mean square error (MSE) and the root mean square error (RMSE) are the most frequent statistical indicator used [49,50]. The measured performance depends mainly on the chosen network architecture. ...
Urban renewal projects worldwide focus mainly on resolving motorized, personal, and low occupancy problems instead of sustainable mobility. As part of the process, traditional field audits have a high cost in time and resources. This paper reviews a spatial model of accessibility and habitability of the streets, oriented to the location of the volume of people moving sustainably out of an extensive street network. The exercise site is in the Monterrey Metropolitan Area, the second largest in Mexico. Here, the population that moves sustainably as the collective (public and enterprise transportation) and the active (cycling, walking, and others) represents a considerable portion (49%) of travelers, thus, confirming the need for intervention. The spatial model is elaborated in a Geographical Information System (GIS), and the main results are compared with the actual public transport demand using a neural networks process. The results of the tool as a predictor have a 91% efficiency, making it possible to determine the location of urban renewal projects related to the volume of people moving sustainably.
... Pose estimation refers to the estimation of the position and attitude of the target to be tested through the detection and tracking of key points. Combined with deep learning, 3D pose estimation can be realized without the interference of background and color [6]. However, traditional human motion pose reconstruction methods are susceptible to image noise when contour information is extracted. ...
The study is aimed at solving the problem of large measurement errors caused by the binocular camera in traditional 3D art design, which leads to inaccurate 3D information of the target. The contour information extraction in the process of human motion pose reconstruction is easily affected by the noise in the image. Therefore, a binocular stereo vision system is built first and it integrates image acquisition, camera calibration, and image processing. The dedistortion method is used to process the image because it can reduce errors. Second, a three-dimensional human motion pose reconstruction model is implemented, the Gaussian template is used to remove the noise in the image frame, and the change detection template (CDM) is used to solve the problem of background “exposure” and “occlusion.” Finally, simulation experiments are designed to verify the system and model designed. Since the research on the application of pose estimation based on visual sensing technology in art design is still blank, such research has great significance and provides a reference for the research in the field. The literature analysis is used to expound and analyze the application of pose estimation based on visual sensing technology in visual communication design and environmental art design: (1) although the binocular stereo vision system causes some errors in the measurement, the overall error is controlled within 2% and the accuracy is high, which proves that it can be applied to the acquisition of three-dimensional information of the target in art design; (2) there is a high degree of fitting between the video sequence data created by the three-dimensional human motion pose reconstruction model designed and the real motion data, which indicates that this method has high accuracy in processing image sequences and the feasibility of applying it to human pose reconstruction in three-dimensional art design is high; (3) through the analysis of the existing literature, it is found that most of the current visual-based attitude assessment studies are carried out by using network cameras combined with computers, and the quality of the obtained images is low. The combination of binocular stereo sensor and attitude estimation technology can be applied to the design of advertising, animation, games, and packaging, making the behavior of virtual characters in animation and games more vivid. The combination provides convenience for the collection of environmental spatial information and object attitude information, the formulation of a design scheme, and real-time monitoring of construction in environmental art design. The purpose of this study is to provide an important theoretical basis for the technical upgrading of art design.
1. Introduction
Art belongs to the social superstructure, and it is an important part of people’s spiritual life after their needs of material life are met. If the economy is more prosperous, the higher living standards are and the greater demand for art is becoming [1]. The art design is a process in which artists express their inspiration, experience, and feelings through artworks and communicate with the public. The traditional art design process needs to go through tedious design steps and takes a lot of time, which cannot meet the practical needs of today’s society. In response to the problem, the research of art design combined with science and technology attracts more and more attention of people [2]. No matter what type of art design, it needs to be conveyed through vision, such as color, pattern, and text on clothing; the quality and 3D effect of animation; the composition of product packaging; and the shape and size of landscape [3]. With the development of society, people have higher and higher requirements for artistic products. The quality of 2D photos or video images taken by ordinary cameras is poor, and they are unable to meet practical needs. The equipment specially used for shooting ultra-high-quality films, television dramas, and animations are generally bulky, expensive, and not suitable for people to use in artistic design work.
Visual sensor based on visual sensing technology has a series of advantages, such as small size, low price, and long life. It can draw the surface signal of the object after computer processing and present it in front of the researchers. For example, the most popular binocular stereo vision sensor at present is widely used in three-dimensional modeling, three-dimensional measurement, intelligent monitoring, and other research fields [4]. Visual sensors have great advantages in image processing compared with ordinary cameras, but there are also some shortcomings, such as being vulnerable to complex background and color, occlusion, and irregular movement of the target [5]. Pose estimation refers to the estimation of the position and attitude of the target to be tested through the detection and tracking of key points. Combined with deep learning, 3D pose estimation can be realized without the interference of background and color [6]. However, traditional human motion pose reconstruction methods are susceptible to image noise when contour information is extracted. After the literature is reviewed, it is found that the current research on pose assessment based on visual sensing technology mainly focuses on human pose assessment and UAV (Unmanned Aerial Vehicle) pose estimation, but there is little literature on its application in art design.
Based on the above problems, a binocular stereo vision system and a 3D human motion pose reconstruction model are built first. Second, simulation experiments are designed to verify the system and model designed. Finally, the application of pose estimation based on visual sensing technology in visual communication design and environmental art design is analyzed. The purpose of this study is to provide an important theoretical basis for the technical upgrading of art design.
2. Materials and Methods
2.1. Analysis of Visual Sensing Technology
Vision is the most important feeling of human beings. Through vision, the size, color, and action of objects can be perceived to obtain information about the surrounding environment. However, human visual perception is vulnerable to emotional, physical, and light, and it has certain restrictions [7]. In recent years, with the development of science and technology, visual sensors gradually replace human beings and are used in various fields, solving the problems existing in the human visual. Here, product detection is taken as an example to compare the human vision and visual sensing technology, as shown in Table 1.
Human vision
Visual sensing technology
Accuracy
Need magnifying glass or microscope assistance and the accuracy is low
Accuracy can reach one-thousandth of an inch without physical constraints
Reproducibility
Easy to fatigue and has some subtle differences when testing identical products. The repeatability is poor
Using the same method and parameters to test the product. It will not fatigue and has good repeatability
Speed
Slow detection speed, especially when the product has a certain speed displacement
Faster and better detection of high-speed moving objects on production lines to improve production efficiency
Objectivity
The subjective judgment which is easily influenced by emotion when testing products
The test results are objective and reliable
Cost
Fatigue, illness, or other subjective factors may lead to lower productivity and higher costs
No illness, no need to stop, high efficiency, production efficiency, cost savings
... Consequently, these advantages make UGVs suitable for indoor 4,5 and outdoor 6,7 applications. However, the ground vehicles can be perturbed due to uncertainties in models, and description of the context of operation 8 , skidding and slipping 9 , noisy sensor's measurements, 10 , and failures in electromechanical components 11 , making the navigation and control of the platform a challenging task 12,13 . Motion planning and control are the two crucial factors for a vehicle to complete an assigned task safely and effectively 14 . ...
This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.
... A set of measurements is required for the estimation process. The vehicle's orientation can be estimated using for instance, inertial measurement unit (IMU) [5][6][7][8][9], while vehicle's pose (orientation and position) can be estimated using IMU and vision unit [10]. Recently, other potential solutions emerged to estimate the vehicle's pose as well as map the unknown environment such as nonlinear deterministic filter for simultaneous localization and mapping (SLAM) [11,12] and nonlinear stochastic filter for SLAM [1]. ...
Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been known that the true navigation dynamics are highly nonlinear modeled on the Lie Group of SE2(3). In this paper, a nonlinear filter for inertial navigation is proposed. The filter ensures systematic convergence of the error components starting from almost any initial condition. Also, the errors converge asymptotically to the origin. Experimental results validates the robustness of the proposed filter.
... A set of measurements is required for the estimation process. The vehicle's orientation can be estimated using for instance, inertial measurement unit (IMU) [5][6][7][8][9], while vehicle's pose (orientation and position) can be estimated using IMU and vision unit [10]. Recently, other potential solutions emerged to estimate the vehicle's pose as well as map the unknown environment such as nonlinear deterministic filter for simultaneous localization and mapping (SLAM) [11,12] and nonlinear stochastic filter for SLAM [1]. ...
Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been known that the true navigation dynamics are highly nonlinear modeled on the Lie Group of . In this paper, a nonlinear filter for inertial navigation is proposed. The filter ensures systematic convergence of the error components starting from almost any initial condition. Also, the errors converge asymptotically to the origin. Experimental results validates the robustness of the proposed filter.
... Angular velocity measurements are given by [5,[21][22][23]: ...
A robust nonlinear stochastic observer for simultaneous localization and mapping (SLAM) is proposed using the available uncertain measurements of angular velocity, translational velocity, and features. The proposed observer is posed on the Lie Group of to mimic the true stochastic SLAM dynamics. The proposed approach considers the velocity measurements to be attached with an unknown bias and an unknown Gaussian noise. The proposed SLAM observer ensures that the closed loop error signals are semi-globally uniformly ultimately bounded. Simulation results demonstrates the efficiency and robustness of the proposed approach, revealing its ability to localize the unknown vehicle, as well as mapping the unknown environment given measurements obtained from low-cost units.
... The fit quality is determined by the model parameters. The heuristic PSO algorithm was employed in the parameter extraction process, since it has demonstrated great efficiency (in both robustness and fast convergence) in multidimensional problems in earlier works [32]- [34]. This algorithm guides the search in the search space based on the fitness function (5), which quantifies the BIS model quality. ...
As material analysis method, the bioimpedance
spectroscopy (BIS) is an effective technique to obtain material
characteristics in many biological applications. Among the BIS
models, the single-dispersion RC model and the Cole-Cole model
constitute the fundamental mathematical descriptions for BIS
measurements. These models enable both the evaluation of
impedance spectrum and derivation of core parameters that
describe biological processes. This paper presents the application
of the particle swarm optimization (PSO), as a flexible heuristic
optimization approach, to both derive the model parameters and
characterize biological media based on BIS measurements. First,
the fundamental modeling approaches are addressed and the
core parameters are discussed. Moreover, the employed electrical
impedance measuring instrument and conducted experiments are
presented. Then, a multi-objective fitness function is established
for the usage of the PSO algorithm for model parameter
optimization. The paper demonstrates two case studies, namely,
BIS measurements are performed to monitor i) liver fat and
characterize its state with an optimized Cole-Cole model and ii)
cell culture growth and characterize its state with an optimized
single-dispersion RC model. It is shown with experimental results
that PSO is an effective and robust tool to fit these biological
models to BIS measurements. Additionally, the experimental
results also highlight that the cell culture growth process cannot
be modeled properly with the single-dispersion RC model in high
frequency ranges. This experimental observation establishes the
demand for the derivation of a more sophisticated mathematical
model for the comprehensive characterization of cell culture
growth in wide frequency spectrum.
Magnetic, angular rate, and gravity (MARG) sensor-based orientation estimation is commonly implemented using extended Kalman filter (EKF) frameworks, where robustness against dynamic motions and magnetic disturbances is ensured through online EKF covariance updates. This paper proposes an alternative solution involving a neural network (NN) with an appropriate topology, which is trained to provide MARG correction signals to the EKF, thereby delivering disturbance-suppressed measurements for improved orientation estimation. First, the EKF-based orientation estimation is revisited, and the main sources of error are highlighted. Then, a universal laboratory framework is introduced, which performs various motions with a calibrated UR5 cobot and records the raw measurements from a MARG sensor attached to its end-effector, along with ground truth pose data. Using a comprehensive database consisting of 16 different scenarios, the derivation of target signals for error-corrected MARG data is presented. Next, three shallow NN architectures—feed forward NN (FFNN), cascade forward NN (CFNN), and focused time-delay NN (FTDNN)—are trained for these targets, and their performances are evaluated using Pearson’s correlation metrics as a function of the number of hidden neurons and input channel combinations. Finally, each NN-EKF combination is assessed, and it is found that the FFNN offers the most suitable topology in terms of both performance and computational cost. The proposed FFNN-EKF approach enhances orientation estimation quality by a significant 44.5% based on the mean squared quaternion error metrics, even in highly disturbed environments. The performance of the FFNN-EKF is also compared to other common methods, demonstrating that the proposed approach outperforms the benchmark filters.
Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identification protocols, novel energy solutions, multi-sensor accurate perception and autonomous navigation, precise path planning, that guarantees collision avoidance, reliable trajectory control, and efficient data transfer within the UAV system. Moreover, special consideration must be given to electronic warfare threats prevention, detection, and mitigation, and the regulatory framework associated with UAV operations. This review presents the role and taxonomy of each UAV avionics system while covering shortcomings and benefits of available alternatives within each system. UAV communication systems, antennas, and location communication tracking are surveyed. Identification systems that respond to air-to-air or air-to-ground interrogating signals are presented. UAV classical and more innovative power sources are discussed. The rapid development of perception systems improves UAV autonomous navigation and control capabilities. The paper reviews common perception systems, navigation techniques, path planning approaches, obstacle avoidance methods, and tracking control. Modern electronic warfare uses advanced techniques and has to be counteracted by equally advanced methods to keep the public safe. Consequently, this work presents a detailed overview of common electronic warfare threats and state-of-the-art countermeasures and defensive aids. UAV safety occurrences are analyzed in the context of national regulatory framework and the certification process. Databus communication and standards for UAVs are reviewed as they enable efficient and fast real-time data transfer.
This brief presents a novel optimal parametric estimation method of improving the reconstructing accuracy for biased sinusoidal signals. First, a second-order generalized integrator (SOGI) is used to construct a linear regression equation, whose unknown coefficient vector is a combination with the bias and frequency of the input signal. Next, the dynamic regression extension and mixing technique is employed to estimate the unknown parameters by solving this equation. Then, the particle swarm optimization algorithm simultaneously optimizes the parameters of SOGI and adaptive control gains. Finally, a comparison of the accuracy of frequency estimation and reconstruction ability illustrates the superiority of the proposed strategy.
Background
Robotic puncture system (RPS) consists of an optical tracking system (OTS) and a robotic arm gripping the puncture needle. Typically, the RPS requires hand‐eye calibration before the surgery in order to obtain the relative position between the OTS and the robotic arm. However, if there is any displacement or angular deviation in either the robotic arm or the OTS, the calibration results become invalid, necessitating recalibration.
Methods
We propose an uncalibrated robotic puncture method that does not rely on the hand‐eye relationship of the RPS. By constructing angle and position graph jacobian matrices respectively, and employing Square Root Cubature Kalman Filter for online estimation. This enables obtaining control variables for the robot to perform puncture operations.
Results
In simulation experiments, our method achieves an average error of 1.3495 mm and an average time consumption of 39.331 s.
Conclusions
Experimental results indicate that our method possesses high accuracy, low time consumption, and strong robustness.
Fusion schemes used for estimating roll and pitch angles from inertial measurement unit (IMU) data typically suffer from one or more of the following limitations: sensitivity to inaccurate initial estimate, computational burden, and sensitivity to inaccurate noise model. This article introduces a new fusion algorithm called the linear parameter varying (LPV)
filter, which aims to effectively address these limitations and strike a balance between them. The proposed LPV
filter is a weighted sum of a number of local linear
filters. Each local filter corresponds to a specific set of parameters, allowing for adaptability to different situations. By combining these local filters with their validity weights, the overall filter achieves robustness in the estimation process. The local filters and the overall filter are designed to be stable with fast dynamics, making the scheme less sensitive to inaccurate initial estimates. Moreover, the synthesis of the local filter gains is performed offline (beforehand), which reduces the computational burden during online implementation, making the proposed scheme more efficient and practical for real-time applications. To evaluate the effectiveness of the proposed scheme, a comparative study is conducted against two benchmark and most popular fusion schemes, namely, the highly accurate but computationally demanding fusion scheme of extended Kalman filter (EKF) as well as the adequately accurate yet computationally simple algorithm of explicit complementary filter (ECF). Through this comparative study, the performance of the LPV
filter, using synthetic and experimental data, is assessed in terms of estimation accuracy/robustness against varying noise characteristics, computational efficiency, and sensitivity to inaccurate initial estimate.
Implicit polynomial (IP) fitting is an effective method to quickly represent two-dimensional (2D) image boundary contour in the form of mathematical function. Under the same maximum degree, the fractional implicit polynomial (FIP) can express more curve details than IP and has obvious advantages for the representation of complex boundary contours. In existing studies, algebraic distance is mainly used as the fitting objective of the polynomial. Although the time cost is reduced, there are problems of low fitting accuracy and spurious zero set. In this paper, we propose a two-stage neural network with differentiable geometric distance, which uses FIP to achieve mathematical representation, called TSEncoder. In the first stage, the continuity constraint is used to obtain a rough outline of the fitting target. In the second stage, differentiable geometric distance is gradually added to fine-tune the polynomial coefficients to obtain a contour representation with higher accuracy. Experimental results show that TSEncoder can achieve mathematical representation of 2D image boundary contour with high accuracy.
Wheels are very important for a mobile robot platform. A mobile robot with Mecanum wheels can move in any direction and any degree rotation without a steering mechanism, which is playing a significant role in transportation, picking up fruits, and other services in a limited small operating space. In this paper, a mathematic model of Mecanum wheels and the controller of the platform are proposed and simulated. The mathematic model is analyzed according to the relations between the angular velocity of the Mecanum wheels and the speed of the moving platform. The transfer function of the system consisted of Mecanum wheels and motors is introduced. Then, a PID controller is designed. To improve the control performance, a fuzzy neural network controller (FNNC) is designed. The simulation shows that compared to the traditional PID control, FNNC gets a shorter rising time while remains the platform in stable status, which overperforms the PID controller.KeywordsMobile robot platformMecanum wheelPIDFNNC
The precise positioning of an unmanned vibratory roller is the key to the compaction quality. Currently, many earth-rockfill dams are constructed in deep narrow valleys. Ultra-wide band (UWB) is widely used to overcome the partially denied problem of the global navigation satellite system. However, due to the complex noisy interference on construction sites, the accuracy of UWB is insufficient. To ensure the precise positioning of the unmanned vibratory roller, we propose an innovation gain-adaptive Kalman filter (IG-AKF) algorithm. The IG-AKF can adjust the noise covariance based on the relationship between the historical state statistics and positioning deviation. In addition, an attention decay mechanism that associates more attention to recent states is employed. Moreover, a diversity Harris hawks optimisation is proposed to optimise hyper-parameters in the IG-AKF. The proposed method was applied in Lianghekou project. Compared with UWB and the standard KF, the positioning accuracy of the IG-AKF was significantly improved.
Due to the wide application of robots, multi-robot formation plays an increasingly important role in autonomous transportation and service. This paper studies a formation control problem of a multi-robot system based on leader-followers mode. Firstly, according to the formation constraint, a kinematic model is established such that the formation problem is transformed into a trajectory-tracking problem of the robots. Based on this model, a backstepping sliding mode (BSM) control algorithm is proposed to ensure the convergence of follower robots’ tracking errors. In addition, the stability of the whole control system is substantiated in the sense of Lyapunov. Finally, the performance of the designed BSM controller is tested by simulation in circular and curvilinear formation scenarios. The simulation results demonstrate that our presented BSM control law possesses high tracking accuracy.
Kalman filter is one of the most important estimation algorithms, which estimates certain unknown variables given the measurements observed over time subject to a dynamic system, for many applications in science and engineering including environmental science, ecometrics, robotics, financial analysis, data mining, etc. It is often necessary to characterize multiple relationships among various kinds of signals/data in tensor form. The conventional Kalman filter paradigm is based on the low-dimensional state-space representation, which is restricted by the state-transition, observation-model, process-noise covariance, and observation-noise covariance matrices. However, we often need to express some or all of them in terms of tensors in practice. Very lately, the aforementioned Kalman filter in tensor form was tackled using tensor decomposition but the exact estimator has never been established so far. In this work, we propose a new generalized Kalman filter framework consisting of state, state-transition model, observation-model, process-noise covariance, and observation-noise covariance tensors of arbitrary orders by applying the ShermanMorrisonWoodbury identity and block tensor inverse, which we call "Tensor Kalman Filter" (TKF). Our proposed new approach can produce the exact Kalman filter estimator without any need of tensor decomposition (approximation). The pertinent computational- and memory-complexity studies are also provided in this paper.
The inertial reference unit (IRU) serves as a master reference for acquisition, tracking and pointing (ATP) system due to its ability to maintain stability with respect to inertial frame. However, the bandwidth of IRU system is usually limited by the dynamics of mounted inertial sensors and structural resonances. In this paper, magnetohydrodynamics (MHD) angular rate sensor (ARS) with extremely low noise at high frequencies (>3Hz) was combined with a conventional gyro serving as inertial sensing unit of the developed IRU. This sensing unit was verified experimentally to exhibit a nearly perfect transfer function with “unity” gain and “zero” phase error. A central hinge that allows only rotation about the actuating axes was used as the supporting structure. A structural parameter design approach based on analytical stiffness model was developed for the central hinge. The relative errors between theoretical stiffness values, finite element analysis (FEA) simulations and experimental data were found to be within ±13%. A double-T network notch filter with optimal parameters was implemented to suppress the low-frequency mechanical resonance. With the notch filter in the forward path, the open-loop dynamic model of the IRU was measured with the results indicating a completely damped resonant peak and an identification error less than ±0.5 dB in magnitude and ±5° in phase. The developed IRU was ultimately certified to achieve more than ±6 mrad as a drive range and more than 120 Hz as a closed-loop bandwidth under a digital controller designed with frequency shaping technique.
Aiming at the problems of low precision and poor adaptability of traditional in-motion coarse alignment methods for vehicle strapdown inertial navigation system (SINS), an optimal indirect in-motion coarse alignment method aided by global navigation satellite system (GNSS) is proposed. The attitude matrix problem is transformed into Wahba problem to make full use of the observation vectors information. In the process of observation vectors construction, the integral interval of the observation vectors is shortened by the sliding interval method to weaken the accumulation of constant error of inertial devices. The Wahba problem is solved by singular value decomposition (SVD) algorithm. Combined with the output information of GNSS and SINS, the equivalent rotation vector and constant bias of gyroscope are used as state variables to construct the process model and measurement model. The type-2 fuzzy adaptive Kalman filter algorithm is proposed to solve the problem of inaccurate measurement noise in the process of state variables estimation, and the transform matrix of carrier frame is continuously modified to obtain a more accurate attitude conversion matrix. Digital simulation and vehicle experiments show that the proposed algorithm can significantly improve the alignment accuracy of inertial navigation system on moving base.
A robust nonlinear stochastic observer for simultaneous localization and mapping (SLAM) is proposed using the available uncertain measurements of angular velocity, translational velocity, and features. The proposed observer is posed on the Lie Group of SLAMn (3) to mimic the true stochastic SLAM dynamics. The proposed approach considers the velocity measurements to be attached with unknown bias and Gaussian noise. The proposed SLAM observer ensures that the closed loop error signals are semi-globally uniformly ultimately bounded. Simulation results demonstrates the efficiency and robustness of the proposed approach revealing its ability to localize the unknown vehicle as well as mapping the unknown environment given measurements obtained from low-cost units.
Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this paper, a mode-switching moving average with variable time period is proposed to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving average and the classical Kalman filter is proposed. Calibration routines for the adaptive Kalman filter take the measured motor current noise and errors in the speed data from the individual joints into account. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach were performed on the Universal Robot 5 manipulator with differing unexpected end effector loads. The combined force and torque sensing method led to a reduction of the estimation errors and response time in comparison with the pioneering method, and the effect is further improved as the payload rises. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available and consequently the cost will be reduced dramatically from methods that require load cells.
This paper describes the design and optimization results of a cascade fuzzy control structure developed and applied for the stabilization of an underactuated two-wheeled mobile pendulum system. The proposed fuzzy control strategy applies three fuzzy logic controllers to both provide the planar motion of the plant and reduce the inner body oscillations. Among these controllers, one is a special PI-type fuzzy logic controller designed to simultaneously ensure the linear speed and prevent high current peaks in the motor drive system. The input-output ranges and membership functions of the controllers are initially selected based on earlier studies. A complex fitness function is formulated for the quantification of the overall control performance. In this fitness function, the quality of reference tracking related to the planar motion, the efficiency of the suppression of inner body oscillations as well as the magnitude of the resulting current peaks in the driving mechanism are considered. Using the defined fitness function, the optimization of the parameters of fuzzy logic controllers is realized with the aid of particle swarm optimization, yielding the optimal possible control performance. Results demonstrate that the optimized fuzzy control strategy provides satisfying overall control quality with both fast closed loop behavior and small current peaks in the driving mechanism of the plant. The flexibility of the proposed fuzzy control strategy allows to protect the plant’s electro-mechanical parts against jerks and vibrations along with smaller energy consumption. At the end of the paper, a look-up table based implementation technique of fuzzy logic controllers is described, which requires small computational time and is suitable for small embedded processors.
For linear systems, the original Kalman filter under the minimum mean square error (MMSE) criterion is an optimal filter under a Gaussian assumption. However, when the signals follow non-Gaussian distributions, the performance of this filter deteriorates significantly. An efficient way to solve this problem is to use the maximum correntropy criterion (MCC) instead of the MMSE criterion to develop the filters. In a recent work, the maximum correntropy Kalman filter (MCKF) was derived. The MCKF performs very well in filtering heavy-tailed non-Gaussian noise, and its performance can be further improved when some prior information about the system is available (e.g., the system states satisfy some equality constraints). In this paper, to address the problem of state estimation under equality constraints, we develop a new filter, called the maximum correntropy Kalman filter with state constraints (MCKF-SC), which combines the advantages of the MCC and constrained estimation technology. The performance of the new algorithm is confirmed with two illustrative examples.
This paper investigates the performance of an optimized fuzzy control structure developed for the stabilization of a naturally unstable mechatronic system. The mechatronic system is a so-called mobile wheeled pendulum consisting of two actuated coaxial wheels and an inner body which oscillates (as a pendulum) around the wheel axis during planar motion. This motion is controlled in closed loop ensuring both the stabilization of the inner body as well as the planar motion of the wheels. The control structure comprises three fuzzy logic controllers whose input-output ranges and membership functions had been defined heuristically in an earlier study. This paper analyzes the achievable control performance through the formulation of a complex performance index and application of the particle swam optimization on the parameters of the control structure. The complex performance index took into account the reference tracking errors and the extent of inner body oscillation. The optimized fuzzy logic controllers showed a remarkable 29% overall performance improvement in the closed loop dynamics compared to the performance of the initial fuzzy control parameters. The simulation results proved that the optimized closed loop behavior protects more the electro-mechanical structure of the plant since the fast reference tracking performance was achieved along with effectively limited inner body oscillations.
Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.
In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.
This paper describes an iterative closed-loop procedure for the performance optimization of an Inertial Navigation System (INS) based on Fiber Optic Gyro (FOG) technology for airborne applications. The proposed approach focuses on the tuning of the Indirect Kalman Filter (IKF) covariance matrices using inertial sensor errors budgets gathered by the analysis of Allan Variance in a reliable calibration environment. The proposed method identifies a base metrics for predicting the actual filter performance, selecting a suitable combinations of IKF tuning parameters in order to satisfy the system specifications for a particular application. The engineering optimization process spans from the sensor raw data acquisition up to the performance test on the real target HW platform, carrying out the various intermediate steps of algorithm design, simulation runs, code porting and deployment on the embedded INS, lab and on-field testing for performance verification and final comparison of the acquired data output with simulation results to feed the successive tuning iteration. The matching effectiveness between simulated and real data is presented to highlight the beneficial features of this approach.
In the last few years, many closed-loop control systems have been introduced in the automotive field to increase the level of safety and driving automation. For the integration of such systems, it is critical to estimate motion states and parameters of the vehicle that are not exactly known or that change over time. This paper presents a model-based observer to assess online key motion and mass properties. It uses common onboard sensors, i.e. a gyroscope and an accelerometer, and it aims to work during normal vehicle manoeuvres, such as turning motion and passing. First, basic lateral dynamics of the vehicle is discussed. Then, a parameter estimation framework is presented based on an Extended Kalman filter. Results are included to demonstrate the effectiveness of the estimation approach and its potential benefit towards the implementation of adaptive driving assistance systems or to automatically adjust the parameters of onboard controllers.
This paper studies the control performances of modern and soft-computing based control solutions. Namely, the stabilization of a naturally unstable mechatronic system will be elaborated using linear-quadratic-Gaussian and cascade-connected fuzzy control schemes. The mechatronic system is a special mobile robot (so called two-wheeled mobile pendulum system) that has only two contact points with the supporting surface and its center of mass is located under the wheel axis. Due to this mechanical structure, the inner body (which acts as a pendulum between the wheels) tends to oscillate during the translational motion of the robot, thus the application of feedback control is essential in order to stabilize the dynamical system. In the first part of the paper, the mechatronic system and the corresponding mathematical model are introduced, while in the second part the aforementioned control solutions are designed for the plant. The achieved control performances are analyzed both in simulation environment and on the real mechatronic system. At the end of the paper, a performance assessment of the elaborated control solutions is given based on transient response and error integral measurements.
In this paper, the linear-quadratic-Gaussian control of a mechatronic system will be studied. The mechatronic
system is a special mobile robot (called two-wheeled mobile pendulum) having two-wheels, two contact points with the supporting surface and its center of mass is located under the wheel axis. Due to the mechanical structure, the inner body (which acts as a pendulum between the wheels) tends to oscillate during the translational motion of the robot, thus the application of modern control methods is essential in order to
stabilize the dynamical system. In the first part of the paper, the mechatronic system and the corresponding mathematical model are introduced, while in the second part different controllers are designed for the plant. The achieved control performances are analyzed based on simulation and implementation results.
The two-wheeled mobile pendulum system is a special mobile robot having two-wheels, two contact
points with the supporting surface and its center of mass is located under the wheel axis. Due to the mechanical structure, the intermediate body tends to oscillate during the translation motion of the robot thus the application of modern control methods is essential in order to stabilize the dynamical system. In this paper, we introduce the mechatronics construction of the robot and derive its corresponding nonlinear mathematical model as well.
To provide a long-time reliable orientation, sensor fusion technologies are widely used to integrate available inertial sensors for the low-cost orientation estimation. In this paper, a novel dual-linear Kalman filter was designed for a multi-sensor system integrating MEMS gyros, an accelerometer, and a magnetometer. The proposed filter precludes the impacts of magnetic disturbances on the pitch and roll which the heading is subjected to. The filter can achieve robust orientation estimation for different statistical models of the sensors. The root mean square errors (RMSE) of the estimated attitude angles are reduced by 30.6% under magnetic disturbances. Owing to the reduction of system complexity achieved by smaller matrix operations, the mean total time consumption is reduced by 23.8%. Meanwhile, the separated filter offers greater flexibility for the system configuration, as it is possible to switch on or off the second stage filter to include or exclude the magnetometer compensation for the heading. Online experiments were performed on the homemade miniature orientation determination system (MODS) with the turntable. The average RMSE of estimated orientation are less than 0.4_ and 1_ during the static and low-dynamic tests, respectively. More realistic tests on two-wheel self-balancing vehicle driving and indoor pedestrian walking were carried out to evaluate the performance of the designed MODS when high accelerations and angular rates were introduced. Test results demonstrate that the MODS is applicable for the orientation estimation under various dynamic conditions. This paper provides a feasible alternative for low-cost orientation determination.
This paper introduces a method of design and implementation of a two-wheeled inverted pendulum (TWIP) robot with friction compensation. Friction in the drive mechanism is a critical factor of robot self-balancing and affects its performance. The friction parameters are identified based on the dynamic model of the drive mechanism. The dynamics of the whole robot system are obtained by the Lagrangian function method and take the robot drive mechanism friction into account. Sliding mode controllers for self-balancing and yaw motion are designed independently, although the TWIP robot is coupled as a nonlinear system. A low cost but low accuracy gyro and accelerator in consumer electronics grade is adopted to estimate the pitch angle and pitch rate. Using the sensor data from the gyro and accelerator fused with the help of a Kalman filter, the robot body pitch angle is obtained precisely, and these are the key state variables for TWIP control. The effect on the sensor installation location is analysed and corrected with innovation, and the precision of the pose estimation is improved accordingly. Several physical experiments are conducted, and the results demonstrate that the proposed method is effective.
Over the years, smartphones have become tools for scientific and clinical research. They can, for instance, be used to assess range of motion and joint angle measurement. In this paper, our aim was to determine if smartphones are reliable and accurate enough for clinical motion research. This work proposes an evaluation of different smartphone sensors performance and different manufacturer algorithm performances with the comparison to the gold standard, an industrial robotic arm with an actual standard use inertial motion unit in clinical measurement, an Xsens product. Both dynamic and static protocols were used to perform these comparisons. Root Mean Square (RMS) mean values results for static protocol are under 0.3° for the different smartphones. RMS mean values results for dynamic protocol are more prone to bias induced by Euler angle representation. Statistical results prove that there are no filter effect on results for both protocols and no
Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.
In this paper, a Kalman filter with proportional gain and multiintegral (PMI) gains is proposed to inertial navigation system (INS) and global positioning system (GPS) integration. A generalized fault is first introduced to represent the unexpected inertial sensors' biases resulted by environment condition changes or performance degradation of INS. Then, a linear time-varying system subject to fault is given to describe the dynamics of the INS/GPS integrated system. To achieve simultaneous estimation of navigation state and inertial sensors' biases, a PMI Kalman filter is developed in the framework of active fault tolerant filtering. Furthermore, a flight experiment is also given to illustrate the effectiveness of the proposed method. It is shown from the experimental results that the designed PMI Kalman filter can estimate biases more accurately than the traditional Kalman filter.
The Szabad(ka)-II 18 DOF walking robot and its simulation
model is suitable for research into hexapod walking
algorithm and motion control. The complete dynamic
model has already been built, and is used as a black box for
walking optimization in this research. First, optimal
straight line walking was chosen as our objective, since the
robot mainly moves in this mode. This case can be tested
and validated as well on the current version of our robot.
An ellipse-based leg trajectory has been generated for this
low-cost straight line walking. Currently a simple new
Fuzzy-PI controller with three input variables is being
constructed and compared with an previously used PI
controller. The purpose of defining the rules and its
optimization are to obtain a controller that provides
walking with higher quality. Both the compared controllers
have been optimized together with the parameters of the
leg trajectory. The particle swarm optimization (PSO)
method was chosen from several methods with our
benchmark-based selection research and the help of
specific test functions; moreover the previous research (the
comparison of genetic algorithm (GA) and PSO) also led to
this conclusion.
The processes of attitude estimation, involving estimation of a vehicle's orientation from body measurements and filtering of noisy measurements is presented. Another way is to use the kinematics model propagated with three axis rate integrating gyros, the rates measured by gyros drift over time. Therefore, to determine the drift, the altitude state vector is supplemented by three or more states. The multiple extended Kalman filter is a simple and flexible tool for a variety of measurements, increasing powerful processors promote increased spacecraft autonomy. The orthogonal altitude filters are nonlinear filters and the predictive filter is used for point-by-point estimation. These filters require the probability density function to be Gaussian and creative ways are used to increase in the computation power to be useful for the future spacecraft applications.
This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a classical local search and three popular metaheuristics in terms of quality of the final solution for the problem considered in this paper. A novel simple stator-flux-oriented sliding mode (SFO-SM) control scheme is online used in conjunction with the optimized DSKF-based algorithm to improve the robustness of the sensorless IM drive at low speed. The SFO-SM control scheme has closed loops of torque and stator-flux linkage without proportional-plus-integral controllers so that a minimum number of gains has to be tuned.
This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.
Human motion is an important issue in various medical analyses. In this paper, the application of interest is estimating the orientation of the wrist in indoor and outdoor activities using low cost Inertial Measurement Sensor IMU. We present an orientation estimation using Kalman filter based on Correntropy Criterion (KFCC). The proposed method uses only the acceleration data in the aim to reduce the computing load with accurate orientation estimation. Optical measurement system has been used to benchmark the performance of the proposed method. Moreover, the KFCC is evaluated in indoor and outdoor activities with static and dynamic motion conditions. The proposed method proves its efficiency and reliability against existing methods.
This paper explores performance of various methods for state estimation of radar tracking models. A coordinated turn case study of maneuvering target in the presence of non-Gaussian noise is of particular interest. We aim at evaluating the estimation potential of recently presented filters grounded in the Maximum Correntropy Criterion (MCC). Various investigations confirm the outstanding performance of such filters for treating stochastic systems disturbed with impulsive (shot) and mixed-Gaussian noises. However, those filters are intended for linear models, and the success of the MCC-based state estimation in a nonlinear continuous-time stochastic environment, which often underlies radar tracking modeling, is still debatable. First, we extend the MCC-based filters, which are designed presently for linear discrete-time stochastic models, to nonlinear continuous-discrete systems. We devise the conventional (non-square-root) filtering and its square-root version as well. Second, we fulfil a comprehensive examination of these new methods in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, in the presence of both impulsive (shot) and mixed-Gaussian noises. In addition, the novel MCC-based filters are compared to various contemporary extended, cubature and unscented Kalman-like state estimators.
Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposedto restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.
This paper presents a novel approach to monitoring rotor blade flap, lead-lag and pitch using an embedded gyroscope and symmetrically mounted MEMS accelerometers. The central hypothesis is that differential accelerometer measurements are proportional only to blade motion; fuselage acceleration and blade bending are inherently compensated for. The inverse kinematic relationships (from blade position to acceleration and angular rate) are derived and simulated to validate this hypothesis. An algorithm to solve the forward kinematic relationships (from sensor measurement to blade position) is developed using these simulation results. This algorithm is experimentally validated using a prototype device. The experimental results justify continued development of this kinematic estimation approach.
Recent developments in the realm of state estimation of stochastic dynamic systems in the presence of non-Gaussian noise have induced a new methodology called the maximum correntropy filtering. The filters designed under the maximum correntropy criterion (MCC) utilize a similarity measure (or correntropy) between two random variables as a cost function. They are shown to improve the estimators’ robustness against outliers or impulsive noises. In this paper we explore the numerical stability of linear filtering technique proposed recently under the MCC approach. The resulted estimator is called the maximum correntropy criterion Kalman filter (MCC-KF). The purpose of this study is two-fold. First, the previously derived MCC-KF equations are revised and the related Kalman-like equality conditions are proved. Based on this theoretical finding, we improve the MCC-KF technique in the sense that the new method possesses a better estimation quality with the reduced computational cost compared with the previously proposed MCC-KF variant. Second, we devise some square-root implementations for the newly-designed improved estimator. The square-root algorithms are well known to be inherently more stable than the conventional Kalman-like implementations, which process the full error covariance matrix in each iteration step of the filter. Additionally, following the latest achievements in the KF community, all square-root algorithms are formulated here in the so-called array form. It implies the use of orthogonal transformations for recursive update of the required filtering quantities and, thereby, no loss of accuracy is incurred. Apart from the numerical stability benefits, the array form also makes the modern Kalman-like filters better suited to parallel implementation and to very large scale integration (VLSI) implementation. All the MCC-KF variants developed in this paper are demonstrated to outperform the previously proposed MCC-KF version in two numerical examples.
Contemporary mobile devices can be used as navigation aids. The embedded gyroscope, accelerometer and magnetometer used together may form a reliable AHRS (Attitude and Heading Reference System), which estimates the orientation of the device with respect to the global reference frame. However, a question arises: which framework to use in order to integrate the noisy data under the tight computing power and energy limitations of a mobile device? While the Extended Kalman Filter (EKF) is considered the standard framework to solve estimation problems in navigation, in practice the much simpler Complementary Filter is often applied in systems of limited resources. In this paper we compare the strengths and drawbacks of both frameworks in the application context of Android-based mobile devices. The comparison is focused on the assessment of accuracy and reliability in several real-world motion scenarios.
In this paper, the fuzzy control of a mechatronic system will be studied. The mechatronic system is a special mobile robot (called two-wheeled mobile pendulum) that has two contact points with the supporting surface and its center of mass is located under the wheel axis. Due to the mechanical structure, the inner body (which acts as a pendulum between the wheels) tends to oscillate during the translational motion of the robot, thus the application of complex control solutions is essential in order to stabilize the dynamical system. In the first part of the paper the mechatronic system and the corresponding mathematical model are introduced, while in the second part the design and implementation of different fuzzy controllers are elaborated for the plant. The achieved control performances are analyzed based on simulation and implementation results.
This paper addresses the position control of valve-controlled cylinder system employed in hydraulic excavator. Nonlinearities such as dead zone, saturation, discharge coefficient and friction existed in the system are highlighted during the mathematical modeling. On this basis, simulation model is established and then validated against experiments. Aim for achieving excellent position control performances, an improved particle swarm optimization (PSO) algorithm is presented to search for the optimal proportional-integral-derivative (PID) controller gains for the nonlinear hydraulic system. The proposed algorithm is a hybrid based on the standard PSO algorithm but with the addition of selection and crossover operators from genetic algorithm in order to enhance the searching efficiency. Furthermore, a nonlinear decreasing scheme for the inertia weight of the improved PSO algorithm is adopted to balance global exploration and local exploration abilities of particles. Then a co-simulation platform combining the simulation model with the improved PSO tuning based PID controller is developed. Comparisons of the improved PSO, standard PSO and Phase Margin (PM) tuning methods are carried out with three position references as step signal, ramp signal and sinusoidal wave using the co-simulation platform. The results demonstrated that the improved PSO algorithm can perform well in PID control for positioning of nonlinear hydraulic system.
Our complete dynamical simulation-model realistically describes the real low-cost hexapod walker robot Szabad(ka)-II within prescribed tolerances under nominal load conditions. This validated model is novel, described in detail, for it includes in a single study: (a) digital controllers, (b) gearheads and DC motors, (c) 3D kinematics and dynamics of 18 Degree of Freedom (DOF) structure, (d) ground contact for even ground, (e) sensors and battery model. In our model validation: (a) kinematical-, dynamical- and digital controller variables were simultaneously compared, (b) differences of measured and simulated curves were quantified and qualified, (c) unknown model parameters were estimated by comparing real measurements with simulation results and applying adequate optimization procedures. The model validation helps identifying both model’s and real robot’s imperfections: (a) gearlash of the joints, (b) imperfection of approximate ground contact model, (c) lack of gearhead’s internal non-linear friction in the model. Modeling and model validation resulted in more stable robot which performed better than its predecessors in terms of locomotion.
Nonlinear Kalman filtering methods are the most popular algorithms for integration of a MEMS-based inertial measurement unit (MEMS-IMU) with a global positioning system (GPS). Despite their accuracy, these nonlinear algorithms present a challenge in terms of the computational efficiency for portable wearable devices. We introduce a cascaded Kalman filter for GPS/MEMS-IMU integration for the purpose of trajectory determination in sports applications. The proposed algorithm uses a novel orientation filter, cascaded with a position/velocity filter. By using cascaded linear Kalman filtering, this method avoids the need to propagate additional states, resulting in the covariance propagation to become more computationally efficient for ambulatory human motion tracking. Additionally, the use of this separate orientation filter helps to retain the orientation accuracy during GPS outage. Results of the field experiments reveal that the proposed algorithm is computationally much faster compared to the available non-linear approaches and demonstrates improved trajectory tracking during GPS outages.
In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum correntropy (KRMC), is derived in kernel space and under the maximum correntropy criterion (MCC). The proposed algorithm is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive noises. The superior performance of KRMC is confirmed by simulation results about short-term chaotic time series prediction in alpha-stable noise environments.
Consumer electronics mobile devices, such as smartphones or tablets, are rapidly growing in computing power and are equipped with an increasing number of sensors. This enables to use a present-day mobile device as a viable platform for computation-intensive, real-time applications in navigation and guidance. In this paper, we present a study on the performance of the orientation estimation based on the data acquired by the accelerometer, magnetometer, and gyroscope in a mobile device. Reliable orientation estimation based on the readouts from inertial sensors may be used in more complex systems, e.g., to correct the orientation error of a visual odometry system. We present a rigorous derivation of the mathematical estimation model, and we thoroughly evaluate the performance of the orientation estimation mechanism available in the Android OS, and the proposed alternative solutions on an unique dataset gathered using an actual smartphone. From the experimental results, we draw the conclusions as to the best performing algorithm, and then we evaluate its execution time on Android-based devices to demonstrate the possibility of real-time usage. The Android code for the proposed orientation estimation system is made publicly available for scientific and commercial applications.
This report presents a novel orientation filter applicable to IMUs consisting of tri-axis gyroscopes and accelerometers, and MARG sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion and gyroscope bias drift compensation. The filter uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient-descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. The benefits of the filter include: (1) computationally inexpensive; requiring 109 (IMU) or 277 (MARG) scalar arithmetic operations each filter update, (2) effective at low sampling rates; e.g. 10 Hz, and (3) contains 1 (IMU) or 2 (MARG) adjustable parameters defined by observable system characteristics. Performance was evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. A simple calibration method is presented for the use of the optical measurement equipment in this application. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the filter achieves levels of accuracy exceeding that of the Kalman-based algorithm; < 0.6 • static RMS error, < 0.8 • dynamic RMS error. The implications of the low computational load and ability to operate at low sampling rates open new opportunities for the use of IMU and MARG sensor arrays in real-time applications of limited power or processing resources or applications that demand extremely high sampling rates.
This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization which includes the presentation of numerous algorithms, examples, and illustrations, designed to improve the reader’s intuition and develop the analytical skills needed to identify optimization problems, classify the structure of a model, and determine whether a solution fulfills optimality conditions.
This paper presents a novel implementation of a Takagi-Sugeno-type fuzzy logic controller (FLC) on a two-wheeled mobile robot (2WMR), which consists of two wheels in parallel and an inverse pendulum. The control objective of the 2WMR is to achieve position control of the wheels while keeping the pendulum around the upright position that is an unstable equilibrium. The novelties of this work lie in three aspects. First, the FLC is a synthesized design which utilizes both heuristic knowledge and model information of the 2WMR system. The FLC structure, including the fuzzy labels, membership functions, and inference, is chosen based on heuristic knowledge about the 2WMR. The output parameters of the FLC are determined by comparing the output of the FLC with that of a linear controller at certain operating points, which avoids the difficulty and tediousness in manual tuning. The linear controller is designed based on a linearized model of the 2WMR system. Second, the proposed FLC is a simple and realizable design for real implementation. Only two fuzzy labels are adopted for each fuzzy variable. Sixteen fuzzy rules are used with eight output parameters and four range parameters for the membership functions to be determined. Third, the proposed FLC is successfully implemented on a real-time 2WMR for regulation and setpoint control tasks. Satisfactory responses are achieved when the 2WMR travels not only on a flat surface but also on an inclined surface. Through comprehensive experiment-based investigations, the effectiveness of the proposed FLC is validated, and the FLC shows superior performance than the existing methods.
To improve the computational efficiency and dynamic performance of low cost Inertial Measurement Unit (IMU)/magnetometer integrated Attitude and Heading Reference Systems (AHRS), this paper has proposed an effective Adaptive Kalman Filter (AKF) with linear models; the filter gain is adaptively tuned according to the dynamic scale sensed by accelerometers. This proposed approach does not need to model the system angular motions, avoids the non-linear problem which is inherent in the existing methods, and considers the impact of the dynamic acceleration on the filter. The experimental results with real data have demonstrated that the proposed algorithm can maintain an accurate estimation of orientation, even under various dynamic operating conditions.
This article presents balancing and navigation control of the balancing robot called MIPS. MIPS is a mobile inverted pendulum system whose structure is a combination of a wheeled mobile robot and an inverted pendulum system. MIPS can navigate on the horizontal plane while balancing the pendulum body. Control performance relies upon the accuracy of sensors to measure a tilted angle. Low cost gyro and tilt sensors are used and fused to detect a balancing angle. Digital filters are selectively designed for sensors to measure an inclined angle accurately with respect to different frequencies. Performances of balancing and navigation of the MIPS are tested by experimental studies through remote control.
A method of tuning a Kalman filter by means of the downhill simplex numerical optimization algorithm is presented. The problem is defined by a brief description of the Kalman filter and the extended Kalman filter and the sensitivity of filter performance to process noise and measurement noise covariance matrices Q and R. The filter tuning problem for a system processing simulated data is then formulated as a numerical optimization problem by defining a performance index based on state estimate errors. The resulting performance index is then minimized using the downhill simplex algorithm. The technique is then applied to three numerical examples of increasing complexity to demonstrate its practical utility.
An important problem in engineering is the unknown parameters estimation in nonlinear systems. In this paper, a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem. This work considers two new aspects, namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method. These mechanisms are employed to enhance global search ability and to increase accuracy. First, three well-known benchmark functions namely Griewank, Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum. The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA), in terms of parameter accuracy and convergence speed. It is confirmed that the proposed APSO is more successful than other aforementioned algorithms. Finally, the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy.
This paper introduces the design and implementation of a two-wheel inverted pendulum (TWIP) system with a fuzzy control scheme and the system-on-a-programmable-chip (SoPC) technology. The control scheme includes three kinds of fuzzy controls which are the fuzzy balanced standing control (FBSC), the fuzzy traveling and position control (FTPC), and the fuzzy yaw steering control (FYSC). Based on the Takagi-Sugeno fuzzy model of the TWIP, the FBSC is a structure of a parallel distributed compensator solved by the linear matrix inequality approach. Based on the motion characteristic of the TWIP, the FTPC and the FYSC are designed with Mamdani architecture if-then rules. Furthermore, the fuzzy control scheme for the real TWIP is implemented into an SoPC development board with an embedded reduced-instruction-set-computer soft-core processor and user intellectual property modules. Both the computer simulations and practical experiments demonstrate the effectiveness of the proposed control scheme.
A problem of accelerometer and gyroscope signals' filtering is discussed in the paper. Triple-axis accelerometer and three single-axis gyroscopes are the elements of strapdown system measuring head. Effective noise filtration impacts on measured signal reliability and the computation precision of moving object position and orientation. The investigations were carried out to apply Kalman filter in a real-time application of acceleration and angular rate signals filtering. The filter parameter adjusting is the most important task of the investigation, because of unknown accuracy of the measuring head and unavailability of precisely known model of the system and the measurement. Results of calculations presented in the paper describe relation between filter parameters and two assumed criterions of filtering quality: output signal noise level and filter response rate. The aim of investigation was to achieve and find values of the parameters which make Kalman filter useful in the real-time application of acceleration and angular rate signals filtering.
Non-linear hysteresis is a complicated phenomenon associated with magnetorheological (MR) fluid dampers. A new model for MR dampers is proposed in this paper. For this, computationally-tractable algebraic expressions are suggested here in contrast to the commonly-used Bouc–Wen model, which involves internal dynamics represented by a non-linear differential equation. In addition, the model parameters can be explicitly related to the hysteretic phenomenon. To identify the model parameters, a particle swarm optimization (PSO) algorithm is employed using experimental force–velocity data obtained from various operating conditions. In our algorithm, it is possible to relax the need for a priori knowledge on the parameters and to reduce the algorithmic complexity. Here, the PSO algorithm is enhanced by introducing a termination criterion, based on the statistical hypothesis testing to guarantee a user-specified confidence level in stopping the algorithm. Parameter identification results are included to demonstrate the accuracy of the model and the effectiveness of the identification process.
This paper reports on the prediction of the expected positioning errors of robot manipulators due to the errors in their geometric parameters. A Swarm Intelligence (SI) based algorithm, which is known as Particle Swarm Optimization (PSO), has been used to generate error estimation functions. The experimental system used is a Motoman SK120 manipulator. The error estimation functions are based on the robot position data provided by a high precision laser measurement system. The functions have been verified for three test trajectories, which contain various configurations of the manipulator. The experimental results demonstrate that the positioning errors of robot manipulators can be effectively predicted using some constant coefficient polynomials whose coefficients are determined by employing the PSO algorithm. It must be emphasized that once the estimation functions are obtained, there may be no need of any further experimental data in order to determine the expected positioning errors for a subsequent use in the error correction process.
Development, calibration and alignment of a miniature magnetic and inertial measurement unit, which is used as an attitude and heading reference system, are presented. Several guidelines were followed during the design process to make the magnetic and inertial measurement unit suitable for various kinds of applications, thus the system is designed both as small as possible but still modular, consisting of three inertial sensor units, a magnetic sensor unit and a control unit.Complete calibration and alignment procedure is described and an adaptive Kalman filter concept for fusing various sensors’ attitude and heading data is introduced and discussed. The characteristics of the magnetic and inertial measurement unit as an attitude and heading reference system are evaluated. The algorithm showed remarkable performance in the orientation determination as the average root mean square error was less than 1.2° over the entire appliable operating range.
Many of the proposed approaches for non-linear systems control are developed under the assumption that all involved parameters are known in advance. Unfortunately, their estimation is not so simple because the nature of the non-linear behaviors is very complex in the most part of the cases.In view of this complication, parameters identification of non-linear oscillators has attracted increasing interests in various research fields: from a pure mathematical point-of-view, parameters identification can be formalized as a multi-dimensional optimization problem, typically over real bounded domains. In doing this, the use of the so-called non-classical methods based on soft computing theories seems to be promising because they do not require a priori information and the robustness of the identification against the noise contamination is satisfactory. However, further studies are required to evaluate the general effectiveness of these methodologies. In this sense, the paper addresses the consistency of two classes of soft computing based methods for the identification of Van der Pol–Duffing oscillators. A large numerical investigation has been conducted to evaluate the performances of six differential evolution algorithms (including a modified differential evolution algorithm proposed by the authors) and four swarm intelligence based algorithms (including a chaotic particle swarm optimization algorithm). Single well, double well and double-hump oscillators are identified and noisy system responses are considered in order to evaluate the robustness of the identification processes. The investigated soft computing techniques behave very well and thus they are suitable for practical applications.
This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification.
In this study, we introduce the design methodology of an optimized fuzzy controller with the aid of particle swarm optimization (PSO) for ball and beam system.The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball. This system exhibits a number of interesting and challenging properties when being considered from the control perspective. The ball and beam system determines the position of ball through the control of a servo motor. The displacement change of the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor.The fixed membership function design of type-1 based fuzzy logic controller (FLC) leads to the difficulty of rule-based control design when representing linguistic nature of knowledge. In type-2 FLC as the expanded type of type-1 FL, we can effectively improve the control characteristic by using the footprint of uncertainty (FOU) of the membership functions. Type-2 FLC exhibits some robustness when compared with type-1 FLC.Through computer simulation as well as real-world experiment, we apply optimized type-2 fuzzy cascade controllers based on PSO to ball and beam system. To evaluate performance of each controller, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time, and a steady-state error. In the sequel, the optimized fuzzy cascade controller is realized and also experimented with through running two detailed comparative studies including type-1/type-2 fuzzy controller and genetic algorithms/particle swarm optimization.