Adaptive Neural Prescribed-Time Control of Switched Nonlinear Systems With Mode-Dependent Average Dwell Time
针对切换非线性系统，提出一种自适应神经预设时间控制方法，通过奇异避免误差依赖标量变换将预设时间控制转化为镇定问题，确保跟踪误差在预设时间内达到预设精度，并利用扩展多Lyapunov函数导出模态依赖平均驻留时间条件保证信号有界。
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.