Adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration

被引：0

作者：

Lin, Xiaofeng ^{[1
]}

Ding, Qiang ^{[1
]}

Kong, Weikai ^{[1
]}

Song, Chunning ^{[1
]}

Huang, Qingbao ^{[1
]}

机构：

[1] Guangxi Univ, Sch Elect Engn, Nanning, Peoples R China

来源：

2014 IEEE SYMPOSIUM ON ADAPTIVE DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING (ADPRL) | 2014年

关键词：

Adaptive dynamic programming; value iteration; tracking control; echo state network;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptive dynamic programming is proposed in this paper. By system transformation, the optimal tracking problem is converted into the optimal regulating problem for the tracking error dynamics. Then, general value iteration algorithm is developed to obtain the optimal control with convergence analysis. Considering the advantages of echo state network, we use three echo state networks with levenberg-Marquardt (LM) adjusting algorithm to approximate the system, the cost function and the control law. A simulation example is given to demonstrate the effectiveness of the presented scheme.

引用

页码：224 / 229

页数：6

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