A novel optimal control design for unknown nonlinear systems based on adaptive dynamic programming and nonlinear model predictive control

作     者：Hu, Wei Zhang, Guoshan Zheng, Yuqing 

作者机构：Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China 

出 版 物：《ASIAN JOURNAL OF CONTROL》 (亚洲控制杂志)

年 卷 期：2022年第24卷第4期

页      面：1638-1649页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China 

主　　题：adaptive control adaptive dynamic programming neural networks nonlinear model predictive control optimal control 

摘      要：This paper presents a novel adaptive optimal control algorithm by combining adaptive dynamic programming with nonlinear model predictive control for unknown continuous-time affine nonlinear systems. The adaptive optimal control design is realized by the model-critic-actor architecture. Model neural network, critic neural network and actor neural network are constructed to approximate the system dynamics, the cost function and the optimal control law respectively. The random initialization of neural networks usually influences the control performance, so three neural networks are initialized properly to obtain the suitable initial values so that the control performance is improved. Especially, actor neural network is initialized to approximate the near-optimal control law which is obtained from nonlinear model predictive control. The convergence of the proposed algorithm is proved by the Lyapunov theory. Finally, simulation results are provided to illustrate the effectiveness of the proposed algorithm.