Data-Driven Finite-Horizon Approximate Optimal Control for Discrete-Time Nonlinear Systems Using Iterative HDP Approach
提出一种数据驱动的有限时域最优控制方法，利用迭代自适应动态规划近似求解Hamilton-Jacobi-Bellman方程，无需系统模型即可获得最优控制律，适用于离散时间非线性仿射系统。
This paper presents a data-based finite-horizon optimal control approach for discrete-time nonlinear affine systems. The iterative adaptive dynamic programming (ADP) is used to approximately solve Hamilton-Jacobi-Bellman equation by minimizing the cost function in finite time. The idea is implemented with the heuristic dynamic programming (HDP) involved the model network, which makes the iterative control at the first step can be obtained without the system function, meanwhile the action network is used to obtain the approximate optimal control law and the critic network is utilized for approximating the optimal cost function. The convergence of the iterative ADP algorithm and the stability of the weight estimation errors based on the HDP structure are intensively analyzed. Finally, two simulation examples are provided to demonstrate the theoretical results and show the performance of the proposed method.