Data-Based Optimal Control for Weakly Coupled Nonlinear Systems Using Policy Iteration

被引：25

作者：

Li, Chao ^{[1
]}

Liu, Derong ^{[2
]}

Wang, Ding ^{[1
,3
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

[3] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2018年 / 48卷 / 04期

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

Adaptive dynamic programming (ADP); neural networks (NNs); optimal control; policy iteration (PI); unknown dynamics; weakly coupled systems; ADAPTIVE OPTIMAL-CONTROL; ROBUST OPTIMAL-CONTROL; INTERCONNECTED SYSTEMS; DECENTRALIZED CONTROL; DESIGN; ALGORITHM; GAMES;

D O I：

10.1109/TSMC.2016.2606479

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a data-based online learning algorithm is established to solve the optimal control problem for weakly coupled continuous-time nonlinear systems with completely unknown dynamics. Using the weak coupling theory, we reformulate the original problem into three reduced-order optimal control problems. We establish an online model-free integral policy iteration algorithm to solve the decoupled optimal control problems without system dynamics. To implement the data-based online learning algorithm, the actor-critic technique based on neural networks and the least squares method are used. Two simulation examples are given to verify the effectiveness of the developed algorithm.

引用

页码：511 / 521

页数：11

共 47 条

[1] OPTIMAL-CONTROL OF WEAKLY COUPLED BILINEAR-SYSTEMS [J].