Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

This paper investigates an adaptive fuzzy tracking controller for uncertain strict-feedback nonlinear systems suffering from asymmetric time-varying state constraints, unmeasured states, and unknown external disturbances. Firstly, a state observer with a simplified structure is constructed to approximate the unavailable system states based on fuzzy logic systems. The difficulties caused by time-varying state constraints are effectively addressed in a direct approach via the nonlinear state-dependent functions. Compared to the classical barrier Lyapunov functions-based (BLFs-based) method, the strict condition imposed on the virtual controller is eliminated, which significantly enhances the applicability of the controller in practical systems. Meanwhile, to handle the issue of "explosion of complexity," a novel adaptive fuzzy tracking control technique is proposed by using the sliding-mode differentiator. Finally, rigorous stability analysis is employed to verify that the error between the system output signal and the reference signal can converge to an arbitrarily small set around the origin, and all closed-loop signals are bounded without time-varying state constraints violation. The effectiveness of the theoretical result is further illustrated by two simulation examples.