Prescribed Performance Fault-Tolerant Control for Synchronization of Heterogeneous Nonlinear MASs Using Reinforcement Learning.

In this article, a novel approach of prescribed performance synchronization control is developed for heterogeneous nonlinear multiagent systems (MASs) subject to unknown actuator faults. Considering that not all followers are able to access the information of the leader, a distributed auxiliary perception system is proposed to estimate the state information of the leader to guarantee that the estimation errors converge to zero within fixed time. Then, based on the estimated states, a prescribed performance fault-tolerant control (FTC) approach is proposed, which achieves the user-defined performance specifications even in the presence of system faults. Moreover, as accurate system dynamic models are perhaps hard to acquire in practical engineering, a data-based method is proposed by using the reinforcement learning (RL) algorithm to design the fault-tolerant controller, which only needs the off-policy online data and is independent of the model dynamics of followers. The stability and synchronization with the prescribed behavior are guaranteed through the Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the developed controller.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Hybrid event-triggered tracking control for unmanned autonomous helicopter under disturbance and deception attacks.

This paper studies the hybrid event-triggered tracking control based on an unmanned autonomous helicopter (UAH) and an unmanned ground vehicle (UGV) under unmeasurable states, transmission delay, external disturbance, and deception attacks. By designing a switching condition, a hybrid event-triggered scheme (ETS) is proposed to realize an elegant trade-off between less data transmissions and control performance. Then, based on the designs of the observer and tracking controller, an augmented closed-loop system is established, and a sufficient condition on uniform ultimate boundedness is presented. Moreover, a co-design method of the hybrid ETS, observer, and controller is obtained via linear matrix inequalities (LMIs), which is further extended by utilizing an improved adaptive hybrid ETS. Finally, some simulation and comparisons are exploited to illustrate the proposed method.

Distributed Adaptive Fault-Tolerant Formation-Containment Control With Prescribed Performance for Heterogeneous Multiagent Systems.

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

Adaptive Fault-Tolerant Formation Control of Heterogeneous Multi-Agent Systems under Directed Communication Topology.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

Incipient Fault Diagnosis for High-Speed Train Traction Systems via Stacked Generalization.

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

Sliding mode observer based incipient sensor fault detection with application to high-speed railway traction device.

This paper considers incipient sensor fault detection issue for a class of nonlinear systems with "observer unmatched" uncertainties. A particular fault detection sliding mode observer is designed for the augmented system formed by the original system and incipient sensor faults. The designed parameters are obtained using LMI and line filter techniques to guarantee that the generated residuals are robust to uncertainties and that sliding motion is not destroyed by faults. Then, three levels of novel adaptive thresholds are proposed based on the reduced order sliding mode dynamics, which effectively improve incipient sensor faults detectability. Case study of on the traction system in China Railway High-speed is presented to demonstrate the effectiveness of the proposed incipient senor faults detection schemes.