Adaptive Fault-tolerant Control for High-order Nonlinear Systems with Supervisory Controllers and Command Filters

In this paper, the tracking control problem is studied for a class of nonlinear high-order systems with actuator fault, and a new command-filter-based adaptive neural supervisory backstepping control strategy is proposed, where command filters are applied to overcome the "explosion of complexity " occurred in conventional backstepping technique and to remove that condition that tracking reference signal and its the first n derivatives should be known. In the strategy, supervisory control is applied to ensure that all the variables at each backstepping belong to a compact set, which is necessary in function approximation theory, and to further ensure that the unknown function derived at each step can be approximated by neural networks in a the set, which guarantees the strictness of the theoretical analysis and overcomes some the shortcoming in the existing results of backstepping control. Finally, simulation results are shown the validity of the control strategy in this paper.