Learning-Based N-Step Heuristic Dynamic Programming for Affine Nonlinear Optimal Regulation - Details

Author：

Xin, Peng (Xin, Peng.) | Wang, Ding (Wang, Ding.) | Zhao, Mingming (Zhao, Mingming.) | Ha, Mingming (Ha, Mingming.) | Ren, Jin (Ren, Jin.)

Indexed by：

EI Scopus

Abstract：

This　paper　introduces　n-step　heuristic　dynamic　programming　(NSHDP),　which　combines　regular　temporal　difference　(TD)　learning　with　TD(λ)　learning,　in　order　to　solve　optimal　control　problems.　First,　the　implementation　process　of　the　basic　value　iteration　algorithm　is　proposed.　Then,　based　on　the　traditional　HDP　algorithm,　the　architecture　of　the　NSHDP(λ)　algorithm　is　described.　At　the　same　time,　the　most　important　thing　is　that　the　stability　condition　of　the　NSHDP(λ)　algorithm　is　developed.　Furthermore,　the　one-step　critic　network,　the　n-step　critic　network,　and　the　action　network　are　designed,　respectively.　Finally,　the　effectiveness　of　the　proposed　algorithm　is　verified　by　simulation　experiment.　©　2022　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.

Keyword：

Dynamic programming Optimal control systems Iterative methods Neural networks Heuristic programming

Author Community：

[ 1 ] [Xin, Peng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Xin, Peng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Xin, Peng]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Xin, Peng]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Wang, Ding]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Wang, Ding]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 7 ] [Wang, Ding]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Wang, Ding]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Zhao, Mingming]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Zhao, Mingming]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 11 ] [Zhao, Mingming]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Zhao, Mingming]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 13 ] [Ha, Mingming]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 14 ] [Ren, Jin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 15 ] [Ren, Jin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 16 ] [Ren, Jin]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
[ 17 ] [Ren, Jin]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China

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Source ：

ISSN： 1934-1768

Year： 2022

Volume： 2022-July

Page： 2242-2247

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 3

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