Adaptive neural control for nonlinear systems with actuator faults and unknown control directions via command filter

This article studies the issue of command filter-based adaptive fault-tolerant control for a class of nonlinear systems subject to unknown control directions and disturbance. First, the neural network is employed to deal with the nonlinear functions, and the explosion of the complexity problem is handled by the command filter approach. Second, the bound estimation method and the Nussbaum function are utilized to compensate for the influence of the actuator faults and the unknown directions, respectively. Finally, the tracking error signals are guaranteed to converge into bounded compact sets around the origin, and all closed-loop signals are bounded. The effectiveness of the proposed method is illustrated by three simulations.