Neural Network-Based Fault-Tolerant Adaptive Synchronization for High-Order Multi-Agent Systems

This chapter is concerned with the fault-tolerant adaptive synchronization issue for high-order multi-agent systems (MASs). Since the considered MASs are in discrete-time forms and involve nonlinear dynamics, a novel backstepping-based framework is introduced for implementing the controller. More specifically, under the proposed novel framework, the virtual controller designs in the intermediate steps are avoided. Therefore, the corresponding NN approximations are released. For each agent, only two NNs, an action NN and a critic NN, are needed. The first NN is employed for the implementation of the controller while the second NN is utilized to estimate the cost function. By utilizing the Lyapunov difference approach, the stability of the MASs is rigorously proved. Finally, the effectiveness of the proposed strategy is validated via the simulation studies.