A new self-learning optimal control laws for a class of discrete-time nonlinear systems based on ESN architecture

被引：0
|
作者：
SONG RuiZhuo [1 ]
XIAO WenDong [1 ]
SUN ChangYin [1 ]
机构：
[1] School of Automation and Electrical Engineering,University of Science and Technology Beijing
来源：
Science China(Information Sciences) | 2014年 / 57卷 / 06期
基金：
中国博士后科学基金; 中国国家自然科学基金; 北京市自然科学基金;
关键词：
adaptive dynamic programming; discrete-time; optimal control; ESN; costate function;
D O I：
暂无
中图分类号：
TP13 [自动控制理论];
学科分类号：
0711 ; 071102 ; 0811 ; 081101 ; 081103 ;
摘要：
A novel self-learning optimal control method for a class of discrete-time nonlinear systems is proposed based on iteration adaptive dynamic programming(ADP)algorithm.It is proven that the iteration costate functions converge to the optimal one,and a detailed convergence analysis of the iteration ADP algorithm is given.Furthermore,echo state network(ESN)architecture is used as the approximator of the costate function for each iteration.To ensure the reliability of the ESN approximator,the ESN mean square training error is constrained in the satisfactory range.Two simulation examples are given to demonstrate that the proposed control method has a fast response speed due to the special structure and the fast training process.
引用
收藏
页码：284 / 293
页数：10
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