Prescribed performance fault tolerant control for uncertain nonlinear systems with input saturation

This paper investigates the problem of adaptive fault tolerant tracking control for uncertain nonlinear systems in the event of external disturbances, actuator faults and input saturation. In order to guarantee the transient and steady-state control performance, the prescribed performance function is employed to impose the anticipant performance criterion on the output tracking error. By using the error transformation, the original inequality error constraint is converted to an unconstrained issue. For the conversion systems, the negative effect resulting from the external disturbances is restrained via the adaptive control approach. The radial basis function neural network (RBFNN) is adopted to handle the unknown nonlinear functions. The Nussbaum gain technique is utilised to deal with the nonlinear terms arising from the actuator faults and input saturation. Combining with the backstepping control strategy, an adaptive neural fault tolerant control (FTC) scheme is developed for the uncertain nonlinear systems. It is rigorously proved that the proposed controller is capable of ensuring the boundness of all closed-loop signals as well as the specified tracking performance. Simulation results on two examples are given to illustrate the effectiveness of the presented control approach.