Neuroadaptive fixed-time prescribed performance for full-state-constrained uncertain systems using dynamic surface...

In this article, precise control of a class of full-state constrained systems with uncertainty and unknown dynamics is studied. A neuroadaptive strategy is proposed to address unknown dynamics and parametric uncertainties. Moreover, disturbance observers are built to estimate unknown disturbances. Subsequently, a novel Lyapunov function incorporating an asymmetric prescribed performance function is constructed, ensuring that the tracking error converges to a small region within a fixed time. Furthermore, a neuroadaptive fixed-time prescribed performance controller with full-state constraints and disturbance compensation is developed, avoiding the use of tracking error transformation function in previous prescribed performance control and thus simplifying the controller design process. Moreover, dynamic surface technology is adopted to prevent the differential explosion problem generated in backstepping design. In addition, Lyapunov theory proves that the error system is locally ultimately exponentially bounded, and the asymmetric fixed-time prescribed tracking performance is guaranteed without violating any state constraints. Finally, the designed controller is tested by experiments.