Neural-Networks-Based Adaptive Fault-Tolerant Control of Nonlinear Systems With Actuator Faults and Input Quantization

In this work, the neural networks-based adaptive fault-tolerant control problem for nonlinear systems with actuator faults and input quantization is investigated. To approximate the nonlinear functions in the control system, radial basis function neural networks (RBFNN) are introduced. Additionally, an adaptive fault-tolerant controller is presented for nonlinear systems to compensate for the effects of input quantization and actuator fault using the backstepping approach and Lyapunov stability theory. It is demonstrated that with the proposed control strategy, all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an arbitrarily small area of origin. The simulation results of an electromechanical system are shown to verify the validity of the control approach.