Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation
针对输入饱和的非线性系统，提出一种数据驱动的事件触发自适应动态规划控制方法，通过动态惩罚因子加速误差收敛，并设计新触发机制减少冗余事件，用李雅普诺夫方法证明误差系统有界。
This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.