Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach

被引：161

作者：

He, Shuping ^{[1
,2
]}

Fang, Haiyang ^{[1
]}

Zhang, Maoguang ^{[1
]}

Liu, Fei ^{[3
]}

Ding, Zhengtao ^{[4
]}

机构：

[1] Anhui Univ, Sch Elect Engn & Automat, Hefei 230601, Peoples R China

[2] Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China

[3] Jiangnan Univ, Inst Automat, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China

[4] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Adaptive optimal control; algebraic Riccati equation (ARE); linear differential inclusion (LDI); nonlinear systems; policy iteration (PI); STABILITY ANALYSIS; LINEAR-SYSTEMS; CONTROL DESIGN; TIME; ALGORITHM;

D O I：

10.1109/TNNLS.2019.2905715

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method.

引用

页码：549 / 558

页数：10

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