Adaptive Neural Prescribed-Time Control of Switched Nonlinear Systems With Mode-Dependent Average Dwell Time
Danping Zeng, Zhi Liu, C. L. Philip Chen, Yaonan Wang, Yun Zhang, Zongze Wu
Abstract
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.