Predefined-time neural adaptive control for strict-feedback nonlinear systems with actuator faults and output hysteresis

This work investigates the predefined-time adaptive control problem for a class of nonlinear systems subject to actuator faults and output hysteresis. The nonlinear hysteresis behavior at the system output is characterized using an inverse Bouc–Wen hysteresis model. To handle the unknown, time-varying, and potentially sign-changing control gain caused by hysteresis, a Nussbaum-type function is employed, enabling robust compensation without precise knowledge of the hysteresis parameters. The control law is designed using a backstepping approach integrated with predefined-time stability theory. Unlike conventional methods, the adaptive law is formulated as a nonlinear differential equation, providing enhanced design flexibility. Lyapunov-based analysis ensures that all closed-loop signals remain uniformly bounded, and the tracking error converges to an arbitrarily small neighborhood of the desired trajectory within the predefined time. Simulation studies and comparative results demonstrate the effectiveness and robustness of the proposed scheme under various challenging scenarios, including actuator faults and severe output hysteresis.