Fixed-time adaptive control for nonstrict-feedback nonlinear systems with sensor faults and input saturation

This paper addresses the challenge of fixed-time adaptive control for nonstrict-feedback nonlinear systems with input saturation and sensor faults. The difficulty arising from the non-smooth saturation nonlinearity is to overcome by employing a smooth non-affine function to estimate the saturation signal. For systems with unknown functions, radial basis function neural networks (RBFNN) are utilized to approximate these unknown nonlinearities. Using the backstepping technique, an adaptive fixed-time control (FxTC) strategy is developed based on the Lyapunov function method. This approach ensures that all signals within the closed-loop system remain bounded, and the system output tracks the desired signal to a bounded compact set within a fixed time. Moreover, the convergence time of the tracking error is independent of the system’s initial states. The proposed approach is validated using a pendulum system and one-link manipulator system as examples. Additionally, comparative results are presented in the simulation section to demonstrate the superiority of the proposed method.