Adjustable iterative Q-learning for advanced neural tracking control with stability guarantee - Details

Author：

Wang, Y. (Wang, Y..) | Wang, D. (Wang, D..) | Zhao, M. (Zhao, M..) | Liu, A. (Liu, A..) | Qiao, J. (Qiao, J..)

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EI Scopus SCIE

Abstract：

In　this　article,　an　accelerated　Q-learning　algorithm　with　evolving　control　is　established　to　solve　the　optimal　tracking　control　problem.　First,　an　accelerated　Q-learning　scheme　is　constructed　with　an　advanced　Q-function.　By　utilizing　the　advanced　Q-function,　calculating　of　the　feedforward　control　input　can　be　avoided　and　the　terminal　tracking　error　can　be　eliminated.　Then,　by　introducing　the　relaxation　factor,　the　convergence　rate　of　the　iterative　Q-function　sequence　is　accelerated　significantly,　which　is　a　potential　way　to　diminish　the　computational　burden.　Furthermore,　the　convergence,　positive　definiteness,　and　stability　conditions　of　the　accelerated　Q-learning　algorithm　are　analyzed　with　some　preconditions　of　the　relaxation　factor.　Thus,　the　developed　algorithm　can　achieve　evolving　control.　Finally,　the　fantastic　performance　of　the　developed　algorithm　with　critic　network　implementation　is　verified　through　two　simulation　examples.　©　2024　Elsevier　B.V.

Keyword：

Q-learning Adaptive dynamic programming Neural tracking control Nonlinear systems Stability analysis Accelerated convergence rate

Author Community：

[ 1 ] [Wang Y.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Wang Y.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Wang Y.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Wang Y.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Zhao M.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Zhao M.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 11 ] [Zhao M.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 12 ] [Zhao M.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 13 ] [Liu A.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 14 ] [Liu A.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 15 ] [Liu A.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 16 ] [Liu A.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 17 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 18 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 19 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 20 ] [Qiao J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China

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Related Keywords：

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Source ：

Neurocomputing

ISSN： 0925-2312

Year： 2024

Volume： 584

6 . 0 0 0

JCR@2022

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 2

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 4

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