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

The problem of fault-tolerant tracking control is studied for a class of uncertain nonlinear systems with actuator failures. An adaptive neural networks (NNs) fault-tolerant tracking control strategy is proposed by combing backstepping with NNs, the implicit function theory, the mean value theorem and the dynamic surface control (DSC) technique. It is proven that it can guarantee all signals in the closed-loop system are uniformly ultimately bounded in the presence of actuator failures and the tracking error converges to a small neighborhood around zero by suitable choice of design parameters. Finally, simulation results are presented to illustrate the effective of this approach.

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