Adaptive decentralized fault-tolerant tracking control of a class of uncertain large-scale nonlinear systems with actuator faults

We are concerned with the fault-tolerant tracking control affair for a class of large-scale multi-input and multi-output (MIMO) nonlinear systems suffering from actuator failures. Taking advantage of the mean-value theory and the implicit function theorem, the non-affine subsystems are transformed into affine forms. Neural networks (NNs) are utilized to approximate unknown virtual control signals, and then an adaptive NN-based decentralized tracking control strategy is exploited recursively by combining backstepping methods as well as the dynamic surface control (DSC) methodology. In theory, the stability of the resulting whole system is rigorously analysed, where it is proven that all signals remain uniformly ultimately bounded (UUB) and the designed strategy can guarantee the convergence of tracking errors via a suitable choice of control parameters. Finally, two simulation examples, both practical and numerical examples, are illustrated to verify the feasibility of the theoretical claims.