No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A framework of frequency-domain flight dynamics modeling for multi-rotor aerial vehicles
Abstract
In this paper, a frequency-domain modeling methodology that can be applied to various multi-rotor aerial vehicles is introduced. The primary contribution of this work is a systematic integration of the first-principles modeling and system identification approaches to generate flight dynamics models with good accuracy. The first-principles modeling and model linearization are conducted to obtain an appropriate baseline model for the subsequent system identification. Next, a four-step parameter identification process, which consists of: (1) baseline model determination; (2) data collection and preprocessing; (3) mode-wise parameter identification; and (4) model fidelity validation, is performed in the frequency domain to identify the uncertain parameters. Our method has been applied to two custom-built multi-rotor aircraft (one X-type quadcopter and one QU4D quadcopter) for efficiency demonstration.
... In this sense, a new control allocation method was proposed to consider aerodynamic effects, most notably those regarding blade flapping and inflow effects as described in [10], based on a function fitting neural network to replace the classic mixing matrix. This method allows us to improve the controller performance without the need for directly using aerodynamic effects equations in the control algorithm or the allocation problem. ...
... As aforementioned, rotor flapping causes the rotor plane to tilt and hence the thrust force will also be tilted and will have a horizontal component. The total thrust that appears in (4) results from the sum of the thrusts produced by each of the four propellers [10] ...
... where is called the thrust coefficient, and are detailed in Table II, as described by Riether [10]. ...
This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks. Thus, the system performance can be improved by replacing the classic allocation matrix, without using the aerodynamic inflow equations directly. The network training is performed offline, which requires low computational power. The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm. Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work, because of its small size. We compared the mechanical torques commanded by the flight controller, i.e., the control input, to those actually generated by the actuators and established at the aircraft. It was observed that the proposed neural network was able to closely match them, while the classic allocation matrix could not achieve that. The allocation error was also determined in both cases. Furthermore, the closed-loop performance also improved with the use of the proposed neural network control allocation, as well as the quality of the thrust and torque signals, in which we perceived a much less noisy behavior.
... Flight test data collection is an important phase to be accomplished before any application on the real-time system identification. During the experiment, manual frequency sweep signals with quasi-sinusoidal are injected to quadcopter as pilot input (Adiprawita et al., 2007;Cai et al., 2016;Sakulthong et al., 2018). The test pilot will manually excite the frequency sweep as an input signal to the quadcopter. ...
... Then, the pilot will reduce the sweep signal until the quadcopter enters the trim state. At one time, the pilot typically issued the frequency sweep into one input channel only, while another input channels will have uncorrelated and minimum signals to retain the quadcopter in hovering flight (Alexis et al., 2016;Cai et al., 2016). Normalise pilot control input and state output response were collected at 50 Hz sampling rate with noises cut off frequency at 10 Hz. ...
In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN's performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy.
... Flight test data collection is an important phase to be accomplished before any application on the real-time system identification. During the experiment, manual frequency sweep signals with quasi-sinusoidal are injected to quadcopter as pilot input (Adiprawita et al., 2007;Cai et al., 2016;Sakulthong et al., 2018). The test pilot will manually excite the frequency sweep as an input signal to the quadcopter. ...
... Then, the pilot will reduce the sweep signal until the quadcopter enters the trim state. At one time, the pilot typically issued the frequency sweep into one input channel only, while another input channels will have uncorrelated and minimum signals to retain the quadcopter in hovering flight (Alexis et al., 2016;Cai et al., 2016). Normalise pilot control input and state output response were collected at 50 Hz sampling rate with noises cut off frequency at 10 Hz. ...
In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’sperformance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy.
... There is a large volume of published studies [13], [16], [37] on system identification based on the frequency domain. Most of the aforementioned researchers used Comprehensive Identification from Frequency Responses (CIFER) software as an identification tool to implement frequency domain identification. ...
... Frequency domain result could be inaccurate and inconsistent because of the insufficient data in very low bandwidth frequency and the removal effect of some dynamic components. Even with the satisfactory identification results from the frequency domain, G. Cai et al. [16] suggested that more focus is needed in data collection and model architecture reconstruction in order to improve the model accuracy further. A complete comparison between frequency and timedomain system identification can be referred to in [20]. ...
A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.
... There is a large volume of published studies [13], [16], [37] on system identification based on the frequency domain. Most of the aforementioned researchers used Comprehensive Identification from Frequency Responses (CIFER) software as an identification tool to implement frequency domain identification. ...
... Frequency domain result could be inaccurate and inconsistent because of the insufficient data in very low bandwidth frequency and the removal effect of some dynamic components. Even with the satisfactory identification results from the frequency domain, G. Cai et al. [16] suggested that more focus is needed in data collection and model architecture reconstruction in order to improve the model accuracy further. A complete comparison between frequency and timedomain system identification can be referred to in [20]. ...
A quadcopter is a rotorcraft with simple mechanical construction. It has the same hovering capability, similar to the traditional helicopter, but it is easier to maintain. The quadcopter is very difficult to control due to its unstable system with highly coupled and non-linear dynamics. To design robust control algorithms, it is crucial to obtain precise quadrotor flight dynamics through system identification, which is a new method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter, the first principle modeling, system identification of quadcopter, and implementation of NN based system identification in quadcopter platform.
... A real time estimator determining the inertia tensor of a three degrees of freedom quadcopter hover platform is designed in [15]. System identification of quadcopters is accomplished also by employing frequency domain methods commonly applied to large scale rotorcrafts and fixed wing aircraft [16], [17], [18]. Also, bare airframe dynamics is extracted in hover flight condition [19]. ...
Development of a reliable high-performance quadcopter requires an accurate and practical model of the vehicle dynamics. Generally, the key physical parameters of an object with six degrees of freedom (6-DOF) are mass and moments of inertia, where mass is easily obtainable while it is difficult to identify moments of inertia considering that it is not always measurable by static tests. In this paper, a precise and fast system identification technique in the frequency domain is proposed, which is directly applicable to the control system design. This technique has been experimentally implemented to two quadcopters. Transfer functions are extracted, and the moments of inertia are identified. The process has been firstly applied in a captive form to a quadcopter attached to the three degrees of freedom laboratory test stand. In addition, the tests are repeated for F450 quadcopter and the extracted dynamic models are verified. Moreover, delays of the rotors are also identified.
... Due to the inherent strongly coupled and nonlinear dynamics of the models, complex control techniques are utilized or linearization and certain assumptions are used to obtain the model linear counterparts prior to designing the controllers [4][5][6]. Although firstprinciple models provide physical insight into the dynamics of quadcopters, they lack appropriate evaluation of their prediction accuracy [7]. As an alternative to models derived from first principles, system identification provides powerful algorithms to obtain quadcopter dynamic models [8,9]. ...
This paper presents a method to precisely model a four rotor unmanned aerial vehicle, widely known as quadcopter autopilot system. Common system identification methods limit quadcopter models into first or second order systems, and do not count for noise characteristics. This leads to poor prediction accuracy of its longitudinal and lateral motion dynamics that ultimately affects the aircraft stabilization during flight and landing. To improve the quality of the estimated models, we utilized a statistically suitable discrete-time linear Box–Jenkins structure to model the plant and noise characteristics of the horizontal subsystems of a quadcopter autopilot system. The models were estimated using flight data acquired when the system were provided with pseudo-random binary sequence input. In this proposed method, by employing the prediction error method and least squares approach, the aircraft dynamics could be modeled up until the fifth order. The normalized root mean square fitness value showed that the predicted model output matches the experimental flight data by 94.72% in the one-step-ahead prediction test, and 84.52% in the infinite-step-ahead prediction test. These prediction results demonstrated an improvement of 52.8% when compared with a first and second order model structures proposed in previous works for the same quadcopter model. The output from this research works confirmed the effectiveness of the proposed method to adequately capture the autopilot dynamics and accurately predict the quadcopter outputs. These would greatly assist in designing robust flight controllers for the autopilot system.
... Reference [11] argues to provide a generic model but limits the multirotor architecture to "+" or "×" configurations. On [12] a more generalized model is presented. It even includes blade-flapping effects, but fails to address attitude planning and control allocation. ...
This paper presents an architecture-independent framework for multirotor simulation based on a dynamic model for non-reversible rotors, a quaternion-based attitude planning, and null-space-based control allocation algorithm. This approach can be of use when
investigating different multirotor architectures' characteristics and control algorithms for them, avoiding spending effort on attitude planning and control allocation for each different case. The framework is compared to current solutions by pointing out the advantages of the approach used here. Simulation results of three different architectures
are presented aiming to explore the algorithm's robustness, without change, to arbitrary and non-symmetric rotor positions; under, fully or over-actuated architectures; and differently-sized rotors.
... The friction between the quadrotor body and the bench setup, and 15 the restricted quadrotor movement alter attitude dynamics and hence affect the identification results. Comprehensive Identification from FrEquency Responses (CIFER) were used by a few groups to identify quadrotor attitude dynamics in-flight [6,7,8,9]. In all cases, the proposed system identification methods requires a lot of time, expertise, and are computationally extensive which makes 20 them impractical for online identification. ...
Designing high performance controllers for multirotors is a rigorous task that is often solved by trial and error approach. Trial and error tuning usually results in non-optimal controller parameters. Tuning controllers based on the existing quadrotor models would result in poor performance of quadrotors due to simplifications and inaccuracies in the underlying models. In this paper optimal tuning rules for quadrotor attitude dynamics are designed, which guarantees near-optimal performance and robustness. A single in-flight run of the Modified Relay Feedback Test that takes only few seconds with guaranteed stability is enough to have near-optimal tuning of the controller. The designed tuning rule is tested experimentally in-flight on a custom-built quadrotor. The results showed significant advantages in performance and robustness due to the proposed approach.
... It determines the accuracy of the dynamic model. In the previous studies for multirotor UAVs, the input variables of the filter are usually chosen as the control inputs, 24,25 while the output variables are chosen as the angular rates, accelerations, and so on. However, the dynamic model established by the above input-output variables cannot directly reflect the characteristics of the forces and moments. ...
The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.
This work presents a nonparametric identification method applied to study the nonlinear response of an MEMS resonator. The MEMS resonator is a clamped-clamped microbeam fabricated for out-of-plane motion accounting for geometric nonlinearities due to midplane stretching while actuated by electrostatic forces. Experimental measurements show hardening behavior in the frequency-response of the first symmetric mode of the resonator due to the dominant cubic nonlinearity. Modeling the dynamics of the microbeam using a nonparametric identification method is shown to be very effective in capturing the characteristics of the complex nonlinear system at electrostatic AC and DC voltages.KeywordsNonparametric IdentificationMEMS resonatorNonlinear Dynamics
This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN's performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.
While much research has been carried out on propellers for full-scale aircraft, not much data exists on propellers applicable to the ever growing number of UAVs. Many of these UAVs use propellers that must operate in the low Reynolds number range of 50,000 to 100,000 based on the propeller chord at the 75% propeller-blade station. Tests were performed at the University of Illinois at Urbana-Champaign (UIUC) to quantify the propeller efficiency at these conditions. In total, 79 pro-pellers were tested and the majority fit in the 9-to 11-in diameter range. During the tests, the propeller speed (RPM) was fixed while changing the wind-tunnel speed to sweep over a range of advance ratios until reaching the windmill state (zero thrust). To examine Reynolds number effects, typically four RPM's were tested in the range 1,500 to 7,500 RPM depending on the propeller diameter. Propeller efficiencies varied greatly from a peak near 0.65 (for an efficient propeller) to near 0.28 (for an exceptionally poor propeller). Thus, these results indicate that proper propeller selection for UAVs can have a dramatic effect on aircraft performance.
In this paper, we present a comprehensive design for a fully functional unmanned rotorcraft system: GremLion. GremLion is a new small-scale unmanned aerial vehicle (UAV) concept using two contra-rotating rotors and one cyclic swash-plate. It can fit within a rucksack and be easily carried by a single person. GremLion is developed with all necessary avionics and a ground control station. It has been employed to participate in the 2012 UAVForge competition. The proposed design of GremLion consists of hardware construction, software devel-opment, dynamics modeling and flight control design, as well as mission algorithm investigation. A novel computer-aided technique is presented to optimize the hardware construction of GremLion to realize robust and efficient flight behavior. Based on the above hardware platform, a real-time flight control software and a ground control station (GCS) software have been developed to achieve the onboard processing capability and the ground monitoring capability respectively. A GremLion mathematical model has been derived for hover and near hover flight conditions and identified from experimental data collected in flight tests. We have combined H 1 technique, a robust and perfect tracking (RPT) approach, and custom-defined flight scheduling to design a comprehensive nonlinear flight control law for GremLion and successfully realized the automatic control which includes take-off, hovering, and a variety of essential flight motions. In addition, advanced mission algorithms have been presented in the paper, including obstacle detection and avoidance, as well as target following. Both ground and flight experiments of the complete system have been conducted including autonomous hovering, waypoint flight, etc. The test results have been presented in this paper to verify the proposed design methodology.
We study models of a quadrotor UAV. These models include various aerodynamic effects of the propellers and their interactions with the motion of the rigid body of the UAV. Two main assumptions are formulated: the twisting of the propellers is such that the local angle of attack is constant along the blades in stationary flight, and, secondly, that the local induced velocity is also constant along the blades. Under these assumptions (which correspond to an optimal hovering rotor), we conclude that the flexibility of the propellers plays an important role in the dynamics of the UAV (including the effects of the vertical location of the center of gravity), and that it is interesting to account for it when designing a closed loop controller.
Quadrotor helicopters are emerging as a popular platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their construction and maintenance, their ability to hover, and their vertical take off and landing (VTOL) capability. Current designs have often considered only nominal operating conditions for vehicle control design. This work seeks to address issues that arise when deviating significantly from the hover flight regime. Aided by well established research for helicopter flight control, three separate aerodynamic effects are investigated as they pertain to quadrotor flight, due to vehicular velocity, angle of attack, and airframe design. They cause moments that affect attitude control, and thrust variation that affects altitude control. Where possible, a theoretical development is first presented, and is then validated through both thrust test stand measurements and vehicle flight tests using the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) quadrotor helicopter. The results enabled improved controller performance.
This paper outlines progress towards realising practical quad-rotor robot helicopters and, in particular, the Australian National University's 'X-4 Flyer' platform. Two challenges facing the X-4 are generating sufficient thrust and man-aging unstable dynamic behaviour. We ad-dress these issues with a rotor design technique for maximising thrust and the application of a novel rotor mast configuration. An aero-elastic blade design is described and its performance results are presented. A sprung teetering rotor hub that allows adjustment of the blade flap-ping characteristics and a quad-rotor dynamic model with blade flapping are introduced. The use of inverted rotors is shown to produce fa-vorable stability properties for the Mark II X-4 Flyer.
Quadrotor helicopters have become increasingly important in recent years as platforms for both research and commercial unmanned aerial vehicle applications. This paper extends previous work on several important aerodynamic effects impacting quadrotor flight in regimes beyond nominal hover conditions. The implications of these effects on quadrotor performance are investigated and control techniques are presented that compensate for them accordingly. The analysis and control systems are validated on the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control quadrotor helicopter testbed by performing the quadrotor equivalent of the stall turn aerobatic maneuver. Flight results demonstrate the accuracy of the aerodynamic models and improved control performance with the proposed control schemes.
This paper introduces the development of flight control system for
rotorcraft-based unmanned aerial vehicle (RUAV). In this research, the
linear time-invariant model valid for hover is sought. The system
response data is acquired in carefully devised experiment procedure and
then a linear time-invariant system model is obtained using the
time-domain analysis method. The acquired model is used to design
feedback controller consisting of inner-loop attitude feedback control,
mid-loop velocity feedback control and the outer-loop position control.
The proposed controller is implemented on an on-board digital computer
and tested in a Berkeley RUAV and shows outstanding hovering performance
In this paper, we consider the problem of controlling multiple quadrotor robots that cooperatively grasp and transport a payload in three dimensions.We model the quadrotors both individually and as a group rigidly attached to a payload. We propose individual robot control laws defined with respect to the payload that stabilize the payload along three-dimensional trajectories. We detail the design of a gripping mechanism attached to each quadrotor that permits autonomous grasping of the payload. An experimental study with teams of quadrotors cooperatively grasping, stabilizing, and transporting payloads along desired three-dimensional trajectories is presented with performance analysis over many trials for different payload configurations.
The advance in robotics has boosted the application of autonomous vehicles to perform tedious and risky tasks or to be cost-effective substitutes for their - man counterparts. Based on their working environment, a rough classi cation of the autonomous vehicles would include unmanned aerial vehicles (UAVs), - manned ground vehicles (UGVs), autonomous underwater vehicles (AUVs), and autonomous surface vehicles (ASVs). UAVs, UGVs, AUVs, and ASVs are called UVs (unmanned vehicles) nowadays. In recent decades, the development of - manned autonomous vehicles have been of great interest, and different kinds of autonomous vehicles have been studied and developed all over the world. In part- ular, UAVs have many applications in emergency situations; humans often cannot come close to a dangerous natural disaster such as an earthquake, a ood, an active volcano, or a nuclear disaster. Since the development of the rst UAVs, research efforts have been focused on military applications. Recently, however, demand has arisen for UAVs such as aero-robotsand ying robotsthat can be used in emergency situations and in industrial applications. Among the wide variety of UAVs that have been developed, small-scale HUAVs (helicopter-based UAVs) have the ability to take off and land vertically as well as the ability to cruise in ight, but their most importantcapability is hovering. Hoveringat a point enables us to make more eff- tive observations of a target. Furthermore, small-scale HUAVs offer the advantages of low cost and easy operation.
In Chap. 6, we present a comprehensive modeling process to obtain a highly accurate nonlinear dynamical model for our unmanned systems, SheLion (also applicable to HeLion). We first derive a minimum-complexity model structure, which covers all the important dynamic features necessary for flight control law design. Based on this structured model, we develop a five-step procedure, a systematic combination of the first-principles and system identification approaches, to determine all the associated model parameters. We then carry out a thorough validation process to verify the fidelity of the flight dynamics model in the wide flight envelope. Finally, we proceed to determine the flight envelope of the obtained flight dynamics model, which is essential before proceeding to conduct flight control law design and flight experiments.
Time optimal path planning and trajectory management algorithms for air vehicles with limited on-board computing resources require an efficient approach to satisfy flight dynamic constraints needed to guarantee paths are feasible. B-spline curves enable compact definition of feasible airplane trajectories that are suited for on-board real-time computation. The design of a trajectory definition and management algorithm suited for a multi-agent persistent surveillance application is described. The proposed solution post-processes the output of a point-by-point path planner and converts it into a minimal representation. Key design requirements include minimization of mission execution time, ability to seamlessly redirect agents based on information acquired by sensor feedback, and robust adherence to mission and vehicle motion constraints. A simple coordinated aerial surveillance scenario is described and demonstrated using the algorithms presented.
This paper describes a practical system identification procedure for small, low-cost, fixed-wing unmanned aircraft. Physical size and cost restrictions limit the sensing capabilities of these vehicles. The procedure is demonstrated on an Ultra Stick 25e, therefore emphasizing a minimum complexity approach compatible with a low-cost inertial sensor. A linear model, obtained from the generic nonlinear equations of motion for aircraft, is used as a basis for system identification. This model is populated with results from a first principles analysis to form a baseline model. Flight experiments are designed using the baseline model and operational constraints to collect informative data. Parameters of the linear model are identified by fitting the model to frequency responses extracted from the data. The parameters are integrated into the nonlinear equations of motion, and both linear and nonlinear models are validated in the time domain. Verification of model accuracy is extended with a sensitivity and residual analysis.
Unmanned Rotorcraft Systems explores the research and development of fully-functional miniature UAV (unmanned aerial vehicle) rotorcraft, and provides a complete treatment of the design of autonomous miniature rotorcraft UAVs. The unmanned system is an integration of advanced technologies developed in communications, computing, and control areas, and is an excellent testing ground for trialing and implementing modern control techniques. Included are detailed expositions of systematic hardware construction, software systems integration, aerodynamic modeling; and automatic flight control system design.Emphasis is placed on the cooperative control and flight formation of multiple UAVs, vision-based ground target tracking, and landing on moving platforms. Other issues such as the development of GPS-less indoor micro aerial vehicles and vision-based navigation are also discussed in depth: utilizing the vision-based system for accomplishing ground target tracking, attacking and landing, cooperative control and flight formation of multiple unmanned rotorcraft; and future research directions on the related areas.
In this paper, a micro quadrotor aircraft with approximately 40 g gross weight and 8 min flight endurance is developed. System development of this micro aerial vehicle is discussed in terms of the design of the mechanical structure and electrical systems. Mechanical structure is designed based on the proposed weight budget, propulsion system efficiency as well as results of structural analysis. Mechanical parts are designed and analyzed with 3D simulation software then 3D printed. Development of the electrical systems includes integration of processor, inertia measurement unit, and communications with commercial off-the-shelf electronics. Finally, a mathematical model of the quadrotor is derived based on the quadrotor X-configuration working principle. Parameters involved are further identified with experimental setups or simulations. The controller is first simulated with software simulator and then tuned with real flight tests.
This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle (UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or off-line computational power, while the mapping is done off-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.
Remote sensing has traditionally be done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) can bridge the gap for personal remote sensing for scientific data. Precision aerial imagery and sensor data requires an accurate dynamics model of the vehicle for controller development. One method of developing a dynamics model is system identification (system ID). The purpose of this paper is to provide a survey and categorization of current methods and applications of system ID for small low-cost UAVs. This paper also provides background information on the process of system ID with in-depth discussion on practical implementation for UAVs. This survey divides the summaries of system ID research into five UAV groups: helicopter, fixed-wing, multirotor, flapping-wing, and lighter-than-air. The research literature is tabulated into five corresponding UAV groups for further research.
We describe a prototype 75 g micro quadrotor with onboard attitude estimation and control that operates autonomously with an external localization system. The motivation for designing quadrotors at this scale comes from two observations. First, the agility of the robot increases with a reduction in size, a fact that is supported by experimental results in this paper. Second, smaller robots are able to operate in tight formations in constrained, indoor environments. We describe the hardware and software used to operate the vehicle as well our dynamic model. We also discuss the aerodynamics of vertical flight and the contribution of ground effect to the vehicle performance. Finally, we discuss architecture and algorithms to coordinate a team of these quadrotors, and provide experimental results for a team of 20 micro quadrotors.
This paper presents the Yamaha R-MAX helicopter hover and forward-flight state-space models computed from flight test data using a frequency-domain system identification method. The derived identified models show good agreement with the flight test data in both the frequency and time domains. The coupled rotor/stabilizer/fuselage dynamics are identified using a hybrid equation formulation, which is summarized in this paper. The vehicle response is dominated by the lightly damped coupled rotor/stabilizer/fuselage modes that were identified in the 10-25 rad/sec range. The identified state-space models presented herein are useful for flight dynamics, control, and simulation studies of rotorcraft unmanned aerial vehicles based on the Yamaha R-MAX.
This paper describes the development of a parameterized model for a small-scale unmanned helicopter (Yamaha R-50 with 10 ft rotor diameter) and its identification using a frequency domain identification technique. The model explicitly accounts for the stabilizer bar, which has a strong influence on the flight dynamics characteristics. The accuracy of the identified model is verified by comparing the model-predicted responses with the responses collected during flight experiments. Furthermore, the values of key identified parameters are compared with the values predicted by helicopter theory to show that the model has a physically meaningful parameterization. Both hover and cruise flight conditions are examined.
A nonlinear flight dynamics model that can be adopted by small-scale unmanned aerial vehicle (UAV) helicopters is presented. To minimize structural complexity, the proposed nonlinear model contains only four essential components, i.e., kinematics, rigid-body dynamics, main rotor flapping dynamics, and yaw rate feedback controller dynamics. A five-step parameter determination procedure is proposed to estimate the unknown parameters of the flight dynamics model. Based on the time-domain evaluations conducted, the nonlinear model is proven to be accurate in capturing the flight dynamics of our UAV helicopter platform over a wide envelope.
In this paper, we consider the problem of controlling multiple quadrotor robots that cooperatively grasp and transport a payload in three dimensions. We model the quadro-tors both individually and as a group rigidly attached to a payload. We propose individ-ual robot control laws defined with respect to the payload that stabilize the payload along three-dimensional trajectories. We detail the design of a gripping mechanism attached to each quadrotor that permits autonomous grasping of the payload. An experimental study with teams of quadrotors cooperatively grasping, stabilizing, and transporting payloads along de-sired three-dimensional trajectories is presented with performance analysis over many trials for different payload configurations.
- Sadeghzadeh I
Payload drop application using an unmanned quadrotor helicopter based on gain-scheduled PID and model predictive control
- I Sadeghzadeh
- Abdolhosseini M Zhang
Sadeghzadeh I, Abdolhosseini M and Zhang Y.
Payload drop application using an unmanned quadrotor helicopter based on gain-scheduled PID and model
predictive control. Unmanned Syst 2014; 2(1): 39-52.
Control system development and flight testing of the Tiger Moth UAV
- B T Fujizawa
- M B Tischler
- P E Arlton
Fujizawa BT, Tischler MB, Arlton PE, et al. Control
system development and flight testing of the Tiger Moth
UAV, Fort Worth, TX, USA, 1-3 May 2012, pp.2973-2983. Alexandria, Virginia, USA: American Helicopter
Society International.
UIUC propeller database
- J B Brandt
- R W Deters
- G K Ananda
Brandt JB, Deters RW, Ananda GK, et al. UIUC
propeller database, http://m-selig.ae.illinois.edu/props/
propDB.html (2008, accessed 15 February 2011).
Nonlinear dynamic modeling for high performance control of a quadrotor
- M Bangura
- R Mahony
Bangura M and Mahony R. Nonlinear dynamic modeling for high performance control of a quadrotor.
In: Carnegie D, Browne W and Hollitt C (eds)
Australasian conference on robotics and automation,
Wellington, New Zealand, 3-5 December 2012, pp.1-10. Sydney, Australia: Australian Robotics and
Automation Association.
Control system design for rotorcraft-based unmanned aerial vehicles using time-domain system identification
- D H Shim
- Kim Hj
- S Sastry