Adaptive neural dynamic surface control for high-order nonstrict-feedback nonlinear systems with unknown backlash-like hysteresis and actuator faults

This paper investigates the adaptive control problem for high-order nonlinear systems with nonstrict-feedback structures, actuator faults, and unknown backlash-like hysteresis. To alleviate the computational burden caused by repeated differentiation of virtual control laws in traditional backstepping, the dynamic surface control (DSC) technique is employed. By combining Lyapunov stability theory with radial basis function neural network (RBFNN) approximation, an adaptive controller is developed within the backstepping framework, ensuring that the tracking error converges to a small neighbourhood of zero and that all closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB). The effectiveness of the proposed control scheme is illustrated through two simulation examples.