Adaptive Fixed-Time Performance Tracking Control for Unknown Nonlinear Pure-Feedback Systems Subject to Full-State Constraints and Actuator Faults

This paper investigates the fixed-time fuzzy adaptive tracking control method based on Barrier Lyapunov Functions (BLFs) for nonlinear pure-feedback systems with full-state constraints and actuator faults. The Mean-Value Theorem (MVT) is used to transform the pure-feedback systems (PFSs) with non-affine terms into a strict-feedback structure. The BLFs are constructed to ensure that all states of the system are specified within the constraints, and the approximation ability of Fuzzy Logic Systems (FLSs) is used to handle the unknown nonlinear functions. By using the backstepping technique, a fuzzy adaptive fixed-time controller is constructed to compensate for possible faults in the actuator. Theoretical analysis proves the convergence of the tracking error in fixed-time and the boundedness of all signals in the closed-loop system. Finally, simulation results verify the established theoretical conclusions and show the effectiveness of the proposed scheme.