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Active Fault-Tolerant Control for Discrete Vehicle Active Suspension Via Reduced-Order Observer
Abstract
In this article, the fault-tolerant control (FTC) problem of vehicle active suspension is concerned in the discrete-time domain, in which the road disturbances and faults in actuator and measurement are considered. The main contribution consists of proposing an active physically realizable fault-tolerant controller based on a reduced-order observer, which makes up an optimal vibration control component and an event-triggered FTC component. More specifically, by discussing a discrete vehicle active suspension subject to road disturbances generated from the output of a designed exosystem, the optimal vibration control component is derived from maximum principle to offset the inevitable vibrations. Meanwhile, based on the real-time system output of vehicle suspension rather than residual error, a reduced-order observer is proposed to cover the physically unrealizable problem for the designed optimal vibration control component. After that, an event-triggered FTC component and an event-triggered restructured system output are designed to compensate the faults in actuator and measurement, respectively. Finally, extensive experiments are conduced to the control performance of vehicle active suspension under the proposed controller, and confirm its effectiveness and superiority over other control schemes.
... 1) Internal Stability Condition: Theorem 1 presents sufficient conditions to ensure the internal stability for system (17) when d k = 0 and f k = 0, for k ∈ Z + , i.e., ...
... 2) Disturbance Attenuation Condition: Theorem 2 presents sufficient conditions to ensure a finite-frequency H ∞ performance for system (17) when f k = 0, for k ∈ Z + , i.e., e k+1 = N (h, ρ)e k + M Dd k , ...
... 3) Fault Sensitivity Condition: Theorem 2 presents sufficient conditions to ensure a finite-frequency H − performance for system (17) when d k = 0, for k ∈ Z + , i.e., ...
This paper presents a new nonlinear observer-based method to detect the faults of both steering and torque actuators of autonomous ground vehicles. To this end, the nonlinear vehicle system is reformulated in a Takagi-Sugeno (TS) fuzzy model with both measured and unmeasured nonlinear consequents, called N-TS fuzzy model. Differently from the classical TS fuzzy technique with linear consequents, this N-TS fuzzy reformulation enables an effective use of differential mean value theorem to deal with unmeasured nonlinearities, which is known as a major challenge in TS fuzzy observer design. Moreover, the N-TS fuzzy form also allows reducing not only the design conservatism but also the numerical complexity of the design conditions as well as the observer structure, which is crucial for real-time vehicle application. To minimize the disturbance effect with an
performance and maximize the fault sensibility with an
performance, the
a priori
information on the disturbance/fault frequency ranges is taken into account in the N-TS fuzzy observer design via the generalized Kalman–Yakubovich–Popov (KYP) lemma. Based on Lyapunov stability theory, the design of the proposed multiobjective
N-TS fuzzy fault detector is recast as an optimization problem with strict linear matrix inequality (LMI) constraints, which can be effectively solved with numerical solvers. Both numerical and experiments are performed under realistic driving conditions to demonstrate the theoretical and practical interests of the new finite-frequency fuzzy fault detection method.
... Among these, model-based fault detection makes full use of the deep knowledge inside the system and determines whether the fault occurs by residual error [2]. Then, to ensure the automatic control system is stable and reduce the impact of faults on the system performance, some targeted FTC strategies are studied [3][4][5][6][7][8][9][10]. Considering that active FTC relies too much on the fault detection module, it is easy to cause delay when reconstructing a controller according to detection results, which affects system performance [4][5][6]. ...
... Among these, model-based fault detection makes full use of the deep knowledge inside the system and determines whether the fault occurs by residual error [2]. Then, to ensure the automatic control system is stable and reduce the impact of faults on the system performance, some targeted FTC strategies are studied [3][4][5][6][7][8][9][10]. Considering that active FTC relies too much on the fault detection module, it is easy to cause delay when reconstructing a controller according to detection results, which affects system performance [4][5][6]. Passive FTC is widely used because of its simple design, easy implementation and good real-time performance [7][8][9][10]. However, due to its conservative design and low tolerance to unknown faults, adaptive FTC can adjust its own characteristic feedback control system in real time and intelligently according to the specific fault affecting the system so that the system can work in the optimal state according to some set standards [11][12][13][14][15][16][17][18][19]. ...
This paper investigated the tracking problem of mixed H∞ and L2−L∞ adaptive fault-tolerant control (FTC) for continuous-time interval type-2 fuzzy systems (IT2FSs). For the membership function mismatch and uncertainty between the modules of the nonlinear system, the IT2 fuzzy model is applied to linearly approximate it. The observer can sensitively estimate the system state, and the adaptive fault estimation functions can estimate adaptively the fault signals, which enables the designed adaptive FTC scheme to ensure the asymptotic stability of the closed-loop control system and achieve the desired mixed H∞ and L2−L∞ tracking performance. The designed adaptive control law can achieve the purpose of dynamic compensation for faults and disturbances, and the introduced lemmas further reduce the design conservatism by adjusting the slack parameters and matrices. Finally, a mass-spring-damping system is used to illustrate the effectiveness of the designed method.
... Experimental results demonstrated superior performance compared to nominal controllers. Han [80] integrated discrete vehicle active suspension system states and fault signals to design an enhanced system. An active fault-tolerant controller was then proposed based on optimal control theory using reduced-order observers. ...
... Classification Machinery [79][80][81][82][83] Fault-tolerant control method based on reconfiguration This method is predicated on fault detection and diagnosis. Once a fault emerges, the controller is shifted to the predesigned corresponding fault-tolerant controller in accordance with the detected fault, ensuring that the system performance remains largely unchanged before and after the fault. ...
Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores fault-tolerant controls for suspension systems, driven by the need to enhance fault tolerance in such scenarios. We examine the dynamic similarities between the semi-active/active suspension systems in road vehicles and the suspension systems in maglev trains, offering a comprehensive summary of fault-tolerant control strategies for both. Our analysis covers the histories, technical characteristics, fundamentals, modeling, mathematical derivations, and control objectives of both systems. The review categorizes fault-tolerant control methods into hardware redundancy, passive fault-tolerant control, and active fault-tolerant control. We evaluate the advantages and disadvantages of these strategies and propose future directions for the development of fault-tolerant control in suspension systems.
... Designing an appropriate switching signal, including multiple Lyapunov functions, common Lyapunov function, average dwell time (ADT), mode-dependent ADT, and state-dependent switching law [14], [15], [16], [17], [18], [19], [20], [21], is a crucial issue for switched systems. Since the potential switched actuator faults of vehicle active suspension systems may originate from the data transmission through the CAN and the external disturbance, the fault-tolerant ability of the control strategy for the vehicle active suspension systems is also an inessential issue to ensure the operation safety [22], [23], [24]. Especially, actuator faults can severely degrade vehicle performance and contribute to safety accidents [25]. ...
... In [23], the non-fragile fault-tolerant control design was proposed for vehicle suspension active systems by taking into account input quantization. In [24], concerned with faults in the actuator and measurement, the fault-tolerant control was developed for the discrete-time vehicle active suspension based on a reduced-order observer. ...
This paper considers the problem of dynamic output feedback fault-tolerant control for switched vehicle active suspension delayed systems with unknown measurement sensitivities. By utilizing a dynamic switched gain, a switched reduced-order state observer is designed to estimate unavailable system states and uncertain system parameters. Subsequently, based on the Nussbaum gain technique, an output feedback fault-tolerant control scheme is proposed to attenuate the influence of the unknown measurement sensitivity on the system output. In addition, an auxiliary switched system is constructed to make up for the effect of the actuator input delays and faults via the backstepping approach. It is proved that all the signals in the closed-loop switched system are semi-globally uniformly ultimate bounded. Meanwhile, the displacement of the vehicle body and the unsprung mass can remain within a small origin neighbourhood under a set of switching signals with average dwell time. Case studies are utilized to illustrate the flexibility and effectiveness of the proposed control approach and indicate that better stabilization of switched vehicle active suspension delayed systems can be achieved through the proposed adaptive fault-tolerant control strategy against the unknown measurement sensitivity.
... The suspension maintenances with distributed computation benefit more effectively from the supplemented information. The modeling, analysis, and control of networked vehicle active suspension systems have received considerable attention in the last two decades [5][6][7][8]. ...
... Based on the mechanical dynamic of the sprung and unsprung masses of vehicle active suspension in [1], the system state x of vehicle suspension is defined as x = [x 1 x 2 x 3 x 4 ] T , which involves the suspension deflection x 1 = z s − z u , the tire deflection x 2 = z u − z r , the velocity x 3 =ż s of sprung component, and the velocity x 4 =ż u of unsprung component, respectively. By setting the sample period as T in an ideal CAN without network-induced time delay and packet dropout, the normal form of networked vehicle active suspension in a discrete-time domain is described as in [7]: ...
Ride comfort and driving safety are highly vulnerable to the undesirable excessive vibrations caused by road surface irregularities and the imperfect in-vehicle network (IVN). The main contribution of this paper consists of proposing a near-optimal vibration control approach for networked vehicle active suspension under irregular road excitations in a discrete-time domain, in which the uncertain time delay and packet dropout in CAN are taken into consideration. More specially, by virtue of two buffers of the sensor-to-controller network channel and the controller-to-actuator network channel in CAN, by introducing a designed state-transformation-based method, the original vibration control problem under the constraints of the irregular road excitations and imperfect CAN is transformed into a two-point boundary value (TPBV) problem without advanced and delayed items. After that, the near-optimal vibration control approach is presented to isolate the vehicle body from the road excitations and compensate the time delay and packet dropout from CAN synchronously. The stability condition of the networked vehicle active suspension under the proposed vibration controller is obtained based on the Lyapunov function. In numerous scenarios with different road roughnesses and network-induced time delays and packet dropouts, the simulation results illustrate the effectiveness and superiority of the proposed near-optimal vibration controller.
... The traffic light control system is put into use with the emergence of advanced measured technologies. In this system, accurate information of traffic flow provides the fundamental information for devising sound control strategies [11,12]. While applying the physical devices to detect the traffic flow, the detection range is limited by the complicated traffic network and even by the huge cost of extending it [13]. ...
... Following the output q i of FNT in Section 3, by integrating the duration adjustment for the signal cycle in (8)- (10), and the proposed elastic utilization-based adaptive scheduling for phase green light in (12), the Algorithm 3 for the adaptation scheduling of urban traffic lights is described as follows. ...
By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow is designed to obtain the variance tendency of traffic load. After that, a strategy for adjusting the duration of traffic signal cycle is designed to tackle the problem of overload or lightweight traffic flow in the next-time frame. While predetermining the duration of signal cycle in the next-time frame, from a utilization perspective, an elastic-adaption strategy for scheduling the separate phase’s green traffic lights is derived from the analytical solution, which is obtained from a designed trade-off scheduling optimization problem to increase the adaptability for the upcoming traffic flow. The experiment results show that the proposed framework can effectively reduce the delay and stopping rate of vehicles, and improves the adaptability for the upcoming traffic flow.
... But it is weak in suppressing the mismatched disturbance, such as the load disturbance of PMSM in non-cascade control. The disturbance rejection technique based on disturbance observer becomes an effective method to deal with the disturbance, such as disturbance observer [16], extended disturbance observer [17,18], finite time disturbance observer [19], nonlinear disturbance observer [20], sliding mode observer [21], reduced-order observer [22]. ...
... In conclusion, the fast reaching law has faster convergence speed. Combined (22) and (24), the sliding mode control law can be derived as ...
Electric vehicle has become the main trend of the development of automobile industry. As a highly symmetrical system, the characteristics of drive motor will have a great impact on the driving comfort. Aiming at the control regulation of permanent magnet synchronous motor (PMSM) drive, a model-free and non-cascade sliding mode control with a fast-reaching law is proposed in this paper. Firstly, the mathematical model of PMSM is constructed as an ultra-local model without considering any motor parameters. Then, to improve the response speed, an integral sliding mode method with a fast reaching law is proposed, and the fast convergence can be realized. The controller system adopts single loop non-cascade control, which greatly simplifies the system structure. In order to further improve the anti-disturbance performance of the system, the dual disturbance observers are used to compensate the disturbance through feed-forward control. The stability of the system is proved by Lyapunov. Finally, the experimental results show that the proposed method has faster convergence speed and stronger anti-disturbance ability.
... (Mrazgua et al., 2019) proposed a fuzzy H∞ fault-tolerant control (FTC) problem for T-S fuzzy model-based active suspension systems with actuator faults. (Han et al., 2021) introduced an active, physical, and realizable fault-tolerant controller based on a reduced order observer for vehicle suspension in discrete time domain. explained active fault-tolerant control and fault estimation for discrete time systems in (Amin & Mahmood-ul-Hasan, 2021;Benosman, 2010;Jin & He, 2017;Tao, 2014; (Abbaspour et al., 2020;Ding et al., 2021;Fourlas & Karras, 2021;Hagh et al., 2021;Mrazgua et al., 2019;Rudin et al., 2020;B. ...
Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted.
... These uncertainties could cause fragility of the controller and further lead to the degradation of the closed-loop system performance. Hence, much effort was devoted to deal with the controller fragility issue for suspension control systems, such as safety assessment method [22], faulttolerant [23] method, and non-fragile method [24]. Among them, the non-fragile methods that can resist interference have attracted scholars' attention. ...
The vehicle active suspension has attracted considerable attention owing to its great contributions to the vertical dynamics of vehicle. This paper investigates the non-fragile output feedback control problem of the uncertain vehicle active suspension with stochastic network-induced delay. Firstly, taking the variation of sprung and unsprung masses into consideration, an interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is introduced to describe the nonlinear characteristics of active suspension systems (ASSs). Secondly, to ensure that the control strategy is practicable when some states are unmeasured, a novel output feedback method is proposed by employing a variable substitution approach. Meanwhile, in order to approximate the real physical conditions of the control system, the gain perturbations are taken into account. Thirdly, with regard to the complexity of signal transmission delay in network control process, a more generalized lumped delay form is employed to represent the network-induced delay. Moreover, to describe the stochasticity of lumped delay, a Markovian process is introduced. Finally, both numerical simulations and experimental tests are carried out to examine the effectiveness and practicability of the proposed controller.
... Output may be harmed by defective parts, but overall system reliability is assured. 9,10 An FDI unit can be built using several approaches. For example in Amin and Mahmood-ul-Hasan, 2 Kalman Filter (KF) is used and in general, the KF is not an ideal estimator. ...
Internal Combustion (IC) engines are commonly used in the process industry, and proper Air-Fuel Ratio (AFR) regulation in their fuel system is essential for improved engine performance, fuel efficiency, and environmental protection. Since faults in the sensors of the AFR system cause the engine to shut down, fault tolerance is essentially required for them. In this paper, an advanced Active Fault-Tolerant Control System (AFTCS) for AFR control of an IC engine is proposed to prevent the engine shutdown. For analytical redundancy, the proposed AFTCS uses the Genetic Algorithm (GA) based observer model in the Fault Detection and Isolation (FDI) unit to generate estimated value for the faulty sensors using the values of other healthy sensors. The proposed framework was simulated in the MATLAB/Simulink environment to verify its effectiveness. In comparison to previous literature works, the results show that the AFR control system has superior fault tolerance output for sensor faults, especially for the MAP sensor, in terms of less oscillatory response. Finally, a comparison of the proposed GA-based AFTCS was carried out with the existing literature works and its superior performance was elaborated.
... e authors in [14] and [15] proposed a set of novel features and used a support vector machine (SVM) with only a RBF kernel to classify real and pseudo pre-microRNAs and proposed Triplet-SVM and PremipreD While different kernels have different characteristics, a RBF kernel could not adequately map the pre-microRNAs to the appropriate feature spaces. When the features of input data contain heterogeneous information [16,17] or the data are nonflat in the high-dimensional feature space [18], it is not reasonable to use a single simple kernel to map all the input data. e authors in [19] used a random forest classifier to find shallow features and only got 91.29% accuracy and proposed MipIe. ...
MicroRNAs are a group of noncoding RNAs that are about 20–24 nucleotides in length. They are involved in the physiological processes of many diseases and regulate transcriptional and post-transcriptional gene expression. Therefore, the prediction of microRNAs is of great significance for basic biological research and disease treatment. MicroRNA precursors are the necessary stage of microRNA formation. RBF kernel support vector machines (RBF-SVMs) and shallow multiple kernel support vector machines (MK-SVMs) are often used in microRNA precursors prediction. However, the RBF-SVMs could not represent the richer sample features, and the MK-SVMs just use a simply convex combination of few base kernels. This paper proposed a localized multiple kernel learning model with a nonlinear synthetic kernel (LMKL-D). The nonlinear synthetic kernel was trained by a three-layer deep multiple kernel learning model. The LMKL-D model was tested on 2241 pre-microRNAs and 8494 pseudo hairpin sequences. The experiments showed that the LMKL-D model achieved 93.06% sensitivity, 99.27% specificity, and 98.03% accuracy on the test set. The results showed that the LMKL-D model can increase the complexity of kernels and better predict microRNA precursors. Our LMKL-D model can better predict microRNA precursors compared with the existing methods in specificity and accuracy. The LMKL-D model provides a reference for further validation of potential microRNA precursors.
... It should be noticed that Pan et al. [15] designed a tracking control method with extended state observer (ESO) to estimate the unknown dynamics and external disturbances. In [17], by taking the irregular road disturbances into consideration, an active fault-tolerant control for active suspension systems was studied. In [20] and [21], taking the external load disturbance and unknown nonsymmetric input saturation into full consideration, Vahidi-Moghaddam et al. designed several disturbanceobserver-based terminal sliding-mode controllers to achieve superior performance. ...
This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.
... Through estimating the lumped disturbance in the system, the estimated disturbance is used for the feedforward compensation control. The disturbance estimation and attenuation techniques for PMSM drives have been summarized in [9], such as, extended sliding mode disturbance observer [15,18], extended state observer [16,21] and disturbance observer [19,20,32,33]. ...
This paper concerns the speed regulation problem for permanent magnet synchronous motor (PMSM) drive system with matched and mismatched disturbance. A novel single-loop speed and current control strategy with continuous integral-type terminal sliding mode control (ITSMC) and disturbance observer (DOB) is proposed. Firstly, the nonlinear motor model including the lumped disturbance is constructed. Then, by introducing a virtual control, a continuous integral-type sliding mode controller, which has the non-cascade structure, is developed based on terminal sliding mode surface, and it has the globally asymptotic stability based on Lyapunov criterion. However, when the drive system has large disturbance, especially for unmatched external disturbance, the performance of the whole system will be influenced. To further improve the anti-disturbance performance, a disturbance observer is introduced to estimate both the matched and mismatched disturbance in the system, and it is used for the feed-forward compensation, and the stability of the system is proved. Finally, the novel scheme is tested on a surface-mounted PMSM by experiment, and the comparative results in different operation conditions verify that the proposed method has the fast transient response and the strong robustness for the disturbance.
... The controller is then reconfigured to meet new operational requirements. Degradation of performance can occur due to defective components in AFTCS, but system stability is guaranteed [8]- [10]. The AFTCS working can be demonstrated in state-space in the analysis by Wang et al. [11] to explain the architecture of observer: ...
Fault tolerant control systems can be used in the process machines such as Internal Combustion (IC) engines to achieve greater reliability and stability in the fault conditions. Thus, costly loss of production due to the unusual and unexpected shutdown of these machines can be avoided. The Air Fuel Ratio (AFR) control system is an important system in IC engines and faults in the sensors of this system will cause its shutdown creating costly production loss, therefore, fault tolerance is necessary for them. In this paper, an Active Fault Tolerant Control System (AFTCS) based on Artificial Neural Networks (ANN) has been proposed for the AFR control system of a Spark Ignition (SI) IC engine to increase its reliability. In the proposed AFTCS, a nonlinear ANN-based observer is used in the Fault Detection and Isolation (FDI) unit for the highly nonlinear sensors of the AFR system for analytical redundancy. The Lyapunov stability analysis has been utilized to design a stable system in normal and faulty conditions. The system has been implemented in MATLAB/Simulink environment to test its performance. The simulation experimental results demonstrate that the suggested system stays reliable maintaining the stability well in the fault conditions of sensors with little degradation in AFR. A comparison with the existing works demonstrates the superior performance of the proposed AFTCS for the highly nonlinear sensors of the AFR control system. The technique suggested is very effective in terms of fault robustness and is more specifically based on the nonlinear behavior of the MAP sensor compared to the existing works.
An event-triggered integral sliding mode control (ETISMC) approach for the networked nonlinear active suspension systems (ASSs) via interval type-2 (IT-2) T-S fuzzy logic is investigated in this paper. Firstly, in order to deal with the uncertainty in system masses, the nonlinearity of the suspension spring stiffness and damping coefficient, and the unknown actuator nonlinearity simultaneously, an ASSs model based on the IT-2 T-S fuzzy method is constructed. Secondly, a discrete event-triggered method is designed to process the sampled data and reduce the usage of vehicle CAN network communication resources. Thirdly, the H infinity robust method is used to handle the road disturbance and the sliding mode control (SMC) is adopted to deal with the actuator input uncertainty. To better integrate the SMC and event-triggered control (ETC), an ETISMC approach is explored for the ASSs. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed strategy in this work.
A novel control method of addressing coupling and disturbance influences for finite-time energy-saving robust control of active suspension systems (ASSs) is investigated. By elaborately constructing coupling and disturbance effect indicators, the pros and cons of coupling and disturbance influences on ASSs are discussed, and then a finite-time coupling and disturbance effects-triggered control method is designed via a second-order sliding mode control technique. Importantly, the good/bad coupling effects are assessed through a well-designed nonlinear function. By means of determining if the sign of disturbances conforms to the expected motion or not, the addition of beneficial disturbance effects or removal of detrimental disturbance effects is implemented. Noticeably, by employing a bioinspired nonlinear reference model, beneficial nonlinear stiffness and damping effects are thus utilized, leading to the possibility of energy-saving performance. As a result, the proposed control method exhibits a unique feature, i.e., fully employing potential contribution from the coupling and disturbance effects, and presents a totally new coupling and disturbance effects-triggered control framework, leading to obvious performance improvement. Benchmark experimental conclusions are devoted to distinguishing the advantages and effectiveness of the designed tracking method.
This article focuses on the problem of designing the reduced-order dynamic output feedback (DOF) controller for discrete-time T–S fuzzy plants. Differing from the existing methodologies, the reduced-order approximation technique is applied to simplify the pregiven high-order DOF controller. The key point is to construct a reduced-order closed-loop model to approximate the original high-order closed-loop system. First, a new error auxiliary system between the high-order closed-loop system and the reduced-order closed-loop model is obtained. The sufficient conditions to guarantee that the corresponding error system is asymptotically stable with a prescribed
performance index are developed. Then, the parameters of the desired reduced-order controller are derived by utilizing the projection lemma and the cone complementary linearization algorithm. Finally, the advantages of the proposed technique are illustrated by a series of simulation analysis.
In this article, we develop a disturbance observer-based event-triggered control strategy for uncertain active suspension systems (ASSs), while guaranteeing the transient behavior and reducing the communication burden over the controller area network (CAN). In order to improve the transient and steady-state performances of ASSs, a novel global finite-time prescribed performance function is proposed. Unlike the existing vehicle suspension results on prescribed performance control, the proposed scheme is able to relax the restriction that the initial value depends on the prescribed function. Meanwhile, the proposed approach ensures that the tracking error is stabilized in a prescribed zone in a finite time independent of the initial condition. Then, a performance function dependent event-triggered mechanism is suggested to alleviate the bandwidth occupancy on the CAN while obtaining satisfactory tracking performance. In addition, a disturbance observer is employed to estimate the lumped uncertainty in ASSs. All signals in the resulting close-loop ASSs are ensured bounded. Finally, the effectiveness of the developed methodology are demonstrated in simulation results.
In this article, an expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) is proposed to realize the decision making for swarm robots. Multiagent deep deterministic policy gradient (MADDPG) is a multiagent reinforcement learning algorithm proposed to utilize a centralized critic within the actor–critic learning framework, which can reduce policy gradient variance. However, it is difficult to apply traditional MADDPG to swarm robots directly as it is time consuming during the path planning, rendering it necessary to propose a faster method to gather the trajectories. Besides, the trajectories obtained by the MADDPG are continuous by straight lines, which is not smooth and will be difficult for the swarm robots to track. This article aims to solve these problems by closing the above gaps. First, the ESB-MADDPG method is proposed to improve the training speed. The smooth processing of the trajectory is designed in the ESB-MADDPG. Furthermore, the expert system also provides us with many trained offline trajectories, which avoid the retraining each time we use the swarm robots. Considering the gathered trajectories, the model predictive control (MPC) algorithm is introduced to realize the optimal tracking of the offline trajectories. Simulation results show that combining ESB-MADDPG and MPC can realize swarm robot decision making efficiently.
The life test of a complex electromechanical system (CEMS) is restricted by many factors, such as test time, test cost, test environment, test site, and test conditions. It is difficult to realize system reliability synthesis and prediction of a CEMS which consists of units with different life distributions. Aiming at the problems, a numerical analysis method based on the computer simulation and the Monte Carlo (MC) method is proposed. First, the unit’s life simulation values are simulated using the MC method with the given each unit’s life distribution and its distribution parameter point estimation. Next, using the unit’s life simulation values, the CEMS life simulation value can be obtained based on the CEMS reliability model. A simulation test is realized instead of the life test of the CEMS when there are enough simulation values of the CEMS life. Then, simulation data are analyzed, and the distribution of the CEMS life is deduced. The goodness-of-fit test, point estimation and confidence interval of the parameters, and reliability measure are estimated. Finally, as a test example of the wind turbine, the practicability and effectiveness of the method proposed in this paper are verified.
Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy. However, some of them are not able to make a good balance between computational cost and precision to meet the needs of deployment in real world. To address these issues, a lightweight and efficient wheat ear detector with Shuffle Polarized Self-Attention (SPSA) is proposed in this paper. Specifically, we first utilize a lightweight backbone network with asymmetric convolution for effective feature extraction. Next, SPSA attention is given to adaptively select focused positions and produce a more discriminative representation of the features. This strategy introduces polarized self-attention to spatial dimension and channel dimension and adopts Shuffle Units to combine those two types of attention mechanisms effectively. Finally, the TanhExp activation function is adopted to accelerate the inference speed and reduce the training time, and CIOU loss is used as the border regression loss function to enhance the detection ability of occlusion and overlaps between targets. Experimental results on the Global Wheat Head Detection dataset show that our method achieves superior detection performance compared with other state-of-the-art approaches.
In this paper, we propose the bionic tracking-containment control based on smooth transition in communication, which is inspired by wolf hunting. To realize the hunting target process, we concentrate on the transition from tracking control to containment control of the swarm system by using backstepping method. The smooth transition of switching communication topology is introduced to realize bionic switching. We design a smooth switching function, which can avoid the negative influence of instant switch. Due to the input saturation and smooth switching function, it is challenging to design the tracking-containment controller with nonlinear dynamics and external disturbances. To overcome these difficulties, we design the tracking-containment control based on the adaptive backstepping method, which includes n command filters and one adaptive neural network to improve the control performance. The swarm system is proved stable under the Lyapunov stability criterion. Finally, simulation is carried out to verify the feasibility of the proposed method.
In this paper, an optimal-performance-supervised vibration control strategy is developed for a class of discrete-time nonlinear systems subject to delayed input and sinusoidal disturbance, which makes up the optimal-trajectory-based vibration controller (OTVC) and the optimal-performance-supervised iterative algorithm (OPSIA). More specifically, by employing the reference optimal trajectories obtained from a closed-loop augmented system under the typical optimal state feedback controller, the original vibration control problem is reconstructed as an optimal-trajectory-guided tracking control (OTGTC) problem. After that, OTVC is derived from a sequence of nonhomogeneous linear two-point boundary value (TPBV) problem, which consists of the feedback term with system state, the feedforward terms with system states of sinusoidal disturbance and reference closed-loop system, and the compensation term with an infinity vector sequence for nonlinear dynamic and delayed input. Meanwhile, by defining the terminal condition involving with the desired minimum performance value, OPSIA is proposed to realize the computability of OTVC. Finally, the effectiveness of the proposed strategy is verified by employing a simple nonlinear discrete-time vehicle active suspension under different scenarios.
This paper is concerned with trajectory tracking control for an underactuated unmanned surface vehicle (USV) subject to external disturbance and error constraints. Firstly, a disturbance observer is constructed to observe the unknown external disturbance induced by wind, waves, and currents. Secondly, in order to avoid the “explosion of complexity” problem caused by virtual control variable differentiation, a first order filter and a time-varying tan-type barrier Lyapunov function (BLF) are developed. Then, by introducing the backstepping and dynamic surface control techniques, feedback control laws ensuring trajectory tracking are designed such that the errors of position and heading angle between the unmanned surface vehicle and a virtual leader converge to a bounded region. Finally, the effectiveness of the designed control scheme is verified by a simulation example.
In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller.
Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting. However the huge computational complexity and massive storage requirements make it a great challenge to deploy them in real-time applications, especially on devices with limited resources. In this study, a rapid and highly efficient lightweight CNN based on visual attention, namely SeedSortNet, is proposed for seed sorting. First, a dual-branch lightweight feature extraction module Shield-block is elaborately designed by performing identity mapping, spatial transformation at higher dimensions and different receptive field modeling, and thus it can alleviate information loss and effectively characterize the multi-scale feature while utilizing fewer parameters and lower computational complexity. In the down-sampling layer, the traditional MaxPool is replaced as MaxBlurPool to improve the shift-invariant of the network. Also, an extremely lightweight sub-feature space attention module (SFSAM) is presented to selectively emphasize fine-grained features and suppress the interference of complex backgrounds. Experimental results show that SeedSortNet achieves the accuracy rates of 97.33% and 99.56% on the maize seed dataset and sunflower seed dataset, respectively, and outperforms the mainstream lightweight networks (MobileNetv2, ShuffleNetv2, etc.) at similar computational costs, with only 0.400M parameters (vs. 4.06M, 5.40M).
This paper presents an investigation of the event-triggered control problem for an active suspension system featuring a networked communication architecture and Dynamic Output Feedback Controller (DOFCs) while taking into account the possibility of failure in actuators. The event-triggering condition determines when it is necessary to transmit the observed variables of the plant to the controller, in order not to saturate the network. A Dynamic Output Feedback Controller is synthesized under LMI restrictions to guarantee the system stability with the criteria, using the Lyapunov–Krasovskii functional approach. As the failures in actuators affect the state space model of the system, a novel polytopic model is employed to approach the plant function. In order to prove a practical feasibility, control performance characteristics for vibration suppression are evaluated under various road conditions.
In this article, a fuzzy floating centroids method (FFCM) is proposed, which uses a fuzzy strategy and the concept of floating centroids to enhance the performance of the neural-network classifier. The decision boundaries in the traditional floating centroids neural-network (FCM) classifier are “hard.” These hard boundaries force a point, such as noisy or boundary point, to be assigned to a class exclusively, thereby frequently resulting in misclassification and influencing the performance of optimization methods to train the neural network. A fuzzy strategy combined with floating centroids is introduced to produce “soft” boundaries to handle noisy and boundary points, which increases the chance of discovering the optimal neural network during optimization. In addition, the FFCM adopts a weighted target function to correct the preference to majority classes for imbalanced data. The performance of FFCM is compared with ten classification methods on 32 benchmark datasets by using indicators: average
F
-measure (Avg.FM) and generalization accuracy. Also, the proposed FFCM is applied to nondestructively estimate the strength grade of cement specimens based on microstructural images. In the experimental results, FFCM achieves the optimal generalization accuracy and Avg.FM on 17 datasets and 21 datasets, respectively; FFCM balances precision and recall better than its competitors for the estimation of cement strength grade.
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