Adaptive fault-tolerant control for a class of uncertain nonlinear MISO discrete-time systems in triangular forms with actuator failures

This paper investigates the adaptive actuator failure compensation control for a class of uncertain multi input single out (MISO) discrete time systems with triangular forms. The systems contain the actuator faults of both loss of effectiveness and lock-in-place. With the help of radial basis function neural networks (RBFNN) to approximate the unknown nonlinear functions, an adaptive RBFNN fault-tolerant control (FTC) scheme is designed. Compared with some exist result in which solving linear matrix inequality (LMI) is required, we introduce the backstepping technique to achieve the FTC task. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are bounded and that the output can successfully track a reference signal in the presence of the actuator failures. Finally, simulation results are provided to confirm the effectiveness of the control approach.