Based on the idea of data-driven control, a novel iterative adaptive dynamic programming (ADP) algorithm based on the globalized dual heuristic programming (GDHP) technique is used to solve the optimal control problem of discrete-time nonlinear switched systems. In order to solve the Hamilton–Jacobi–Bellman (HJB) equation of switched systems, the iterative ADP method is proposed and the strict convergence analysis is also provided. Three neural networks are constructed to implement the iterative ADP algorithm, where a novel model network is designed to identify the system dynamics, a critic network is used to approximate the cost function and its partial derivatives, and an action network is provided to obtain the approximate optimal control law. Two simulation examples are described to illustrate the effectiveness of the proposed method by comparing with the heuristic dynamic programming (HDP) and dual heuristic programming (DHP) methods.

基于数据驱动控制的思想，基于全局双重启发式编程（GDHP）技术的新型迭代自适应动态规划（ADP）算法用于解决离散时间非线性切换系统的最优控制问题。为了解决交换系统的汉密尔顿-雅各比-贝尔曼（HJB）方程，提出了迭代ADP方法并提供了严格的收敛性分析。构造了三个神经网络来实现迭代ADP算法，其中设计了一个新颖的模型网络来识别系统动力学，使用了注释器网络来对成本函数及其偏导数进行近似，并提供了一个动作网络来获得近似值。最优控制律。