In this paper, the design of an event-triggered fault-tolerant control for leader-following consensus in multi-agent systems subject to actuator faults is presented. The problem under consideration is to reduce the control update rate and to guarantee that all the agents follow the trajectories of a leader when one or all the agents are subject to actuators faults. The proposed fault-tolerant strategy is based on distributed virtual actuators without re-tuning the nominal leader-following control. Linear matrix inequalities-based conditions are synthesized to guarantee the stability of the synchronization error and estimation error. The effectiveness of the event-triggered fault-tolerant strategy is illustrated through numerical examples 

Event-triggered fault-tolerant leader-following control for homogeneous multi-agent systems

Cooperative fault‐tolerant control for heterogeneous nonlinear multiagent systems via distributed output regulation

New results on robust passivity analysis of neural networks with interval nondifferentiable and distributed time-varying delays are investigated. It is assumed that the parameter uncertainties are norm-bounded. By construction an appropriate Lyapunov-Krasovskii containing single, double, triple and quadruple integrals, which fully utilize information of the neuron activation function and use refined Jensen's inequality for checking the passivity of the addressed neural networks are established in linear matrix inequalities (LMIs). This result is less conservative than the existing results in literature. It can be checked numerically using the effective LMI toolbox in MATLAB. Three numerical examples are provided to demonstrate the effectiveness and the merits of the proposed methods.

Robust passivity analysis of mixed delayed neural networks with interval nondifferentiable time-varying delay based on multiple integral approach

In this paper, a distributed adaptive consensus law with fault compensation for a heterogeneous multi-robot system (MRS) is proposed. The design paradigm adopted in this work involves a leader-following cooperative algorithm featuring two distinct adaptive coupling gains to compensate for multiple additive time-varying faults. Exacerbating the situation, the follower robots commissioned in the leader-following mission are non-identical due to their dynamic characteristic as normally exist in a physical setup. The capability of the proposed scheme is investigated and compared with the other two recent works in two facets; one is to gauge how the algorithm is able to mitigate faults of varying nature in the presence of heterogeneous robot(s) while maintaining the platoon formation during the leader-following task; two is the ability to cope with subsequent topology reconfiguration. The stability and the robustness of the proposed scheme against bounded time-varying faults are proven using rigorous Lyapunov analysis. The proposed control strategy exempts the use of an observer or estimator, thereby simplifies the synthesis and implementation on mobile robots. The simulation results of the proposed adaptive consensus law demonstrate the best performance as compared to the other two recent works in the presence of multiple faulty robots.

/pdf/distributed-adaptive-cooperative-control-with-fault-1u6sv08dk4.pdf

Distributed Adaptive Cooperative Control With Fault Compensation Mechanism for Heterogeneous Multi-Robot System

This paper focuses on the adaptive neural network (NN) control problem for nonlinear quarter active suspension systems with actuator failure. By using Butterworth low-pass filter (LPF), the second order active suspension system is converted to a fourth order system, which solves the problem of zero dynamics analysis in the second order system. Based on the adaptive backstepping technique, considering the actuator fault of vehicle, the corresponding fault tolerant controller is designed. At the same time, the unknown smooth functions are estimated by the NN. It is proved by stability analysis that all states in active suspension system are bounded. Finally, a simulation example is given to verify the effectiveness of the proposed method in a quarter active suspension system.

Adaptive NN control based on Butterworth low-pass filter for quarter active suspension systems with actuator failure

This paper studies the event-triggered fault-tolerant control problem of nonlinear multi-agent systems. The goal is to ensure the stability of event-based sampling multi-agent systems when the actuator faults occurs. The neural networks approximate property is used to approximate unknown ideal control parameters, which can reduce the exact requirements of control parameters. Based on the states information of neighboring agents, a distributed fault-tolerant consensus controller is designed for the leaderless multi-agent systems. Moreover, an event-triggered mechanism with special definition of event-triggered error is applied to reduce the amount of communications. In addition, the Zeno behaviour is avoided. By using Lyapunov stability theory, it is proved that all signals are bounded in the closed-loop systems. Finally, a numerical simulation result is presented to prove the effectiveness of the proposed method.

Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems

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Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems