Performance guaranteed tracking control of nonlinear systems under anomaly actuation: A neuro-adaptive fault-tolerant approach

In this work, we investigate the performance guaranteed tracking control problem of a class of multi-input multi-output (MIMO) nonlinear systems with anomaly actuation. By introducing new forms of parameter estimation error together with Lyapunov function, and using neural network approach, the obstacles caused by anomaly actuation can be handled gracefully and the assumptions on control gain matrices in existing results are significantly relaxed. Furthermore, a strictly increasing function is introduced to form a scaling speed transformation, which directly impacts both the controller law and the adaptive law, accelerating the learning rate and thus enhancing the tracking performance. It is shown that all the closed-loop signals are uniformly bounded and the tracking error converges to an adjustable residual set with a pre-assignable decay rate during the tracking process. Simulation results are presented to illustrate the effectiveness of the proposed scheme.