An Online Single-Network Adaptive Algorithm for Continuous-Time Nonlinear Optimal Control

被引：0

作者：

Lee, Jae Young ^{[1
]}

Park, Jin Bae ^{[1
]}

Choi, Yoon Ho ^{[2
]}

Lee, Keun Uk ^{[1
]}

机构：

[1] Yonsei Univ, Dept Elect & Elect Engn, Seoul 120749, South Korea

[2] Kyonggi Univ, Dept Elect Engn, Kyonggi Do, South Korea

来源：

2013 13TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2013) | 2013年

基金：

新加坡国家研究基金会;

关键词：

Adaptive control; optimal control; actor-critic; approximate dynamic programming; nonlinear control; SYSTEMS;

D O I：

暂无

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, we propose an online adaptive neural-algorithm to solve the CT nonlinear optimal control problems. Compared to the existing methods, which adopt the architecture with two neural networks (NNs) for actor-critic implementations, only one NN for critic is used to implement the algorithm, simplifying the structure of the computation model. Moreover, we also provide a generalized learning rule for updating the NN weights, which covers the existing critic update rules as special cases. The theoretical and numerical results are given under the required persistent excitation condition to verify and analyze stability and performance of the proposed method.

引用

页码：1687 / 1690

页数：4

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