Adaptive Neural Prescribed-Time Control of Switched Nonlinear Systems With Mode-Dependent Average Dwell Time

Most current adaptive neural control strategies for switched nonlinear systems, both finite-time and fixed-time, are limited by a conservatively estimated settling time. Besides, the convergence accuracy of these methods is only bounded but unknown and uncertain. This study proposes a neural adaptive prescribed-time control method to solve such a problem. Specifically, a critical design step is that the practical prescribed-time control problem is converted into a practical stabilization problem by developing a new singularity-avoidance error-dependent scalar transformation. Guided by this idea, an adaptive neural prescribed-time controller is constructed, ensuring prescribed transient behavior and all tracking errors to achieve preset accuracy within the prescribed time simultaneously. Furthermore, by utilizing extended multiple Lyapunov functions, a new mode-dependent average dwell time condition is derived to ensure that all signals in the controlled system remain bounded. Finally, simulations demonstrate the feasibility of the developed scheme.