Neural network-based finite-time adaptive tracking control of nonstrict-feedback nonlinear systems with actuator failures

Abstract

In this paper, the finite-time adaptive fault-tolerant tracking control for nonstrict-feedback nonlinear systems (NSFNSs) is investigated. To avoid the problem of “complexity explosion”, a novel finite-time command filter is introduced to generate command signals and their derivatives. The fractional order error compensation mechanism (ECM) is designed to quickly compensate the effect of filter error. By combining the approximation abilities of neural networks and command filtered backstepping (CFB) approach, a finite-time adaptive control strategy is established. It guarantees that the output tracking error approaches to a sufficiently small region of the original point within finite-time, and all signals of the closed-loop system are finite-time semi-globally uniformly ultimately bounded (SGUUB). Finally, two simulation examples are supplied to verify the effectiveness of the proposed control method.