Multi-step policy evaluation for adaptive-critic-based tracking control towards nonlinear systems - Details

Author：

Li, X. (Li, X..) | Ren, J. (Ren, J..) | Wang, D. (Wang, D..)

Indexed by：

Scopus

Abstract：

Currently,　there　are　a　large　number　of　tracking　problems　in　the　industry　concerning　nonlinear　systems　with　unknown　dynamics.　In　order　to　obtain　the　optimal　control　policy,　a　multi-step　adaptive　critic　tracking　control　(MsACTC)　algorithm　is　developed　in　this　paper.　By　constructing　a　steady　control　law,　the　tracking　problem　is　transformed　into　a　regulation　problem.　The　MsACTC　algorithm　has　an　adjustable　convergence　rate　during　the　iterative　process　by　in-corporating　a　multi-step　policy　evaluation　mechanism.　The　convergence　proof　of　the　algorithm　is　provided.　In　order　to　implement　the　algorithm,　three　neural　networks　are　built,　including　the　model　network,　the　critic　network,　and　the　action　network.　Finally,　two　numerical　simulation　examples　are　given　to　verify　the　effectiveness　of　the　algorithm.　Simulation　results　show　that　the　MsACTC　algorithm　has　satisfactory　performance　in　terms　of　the　applicability,　tracking　accuracy,　and　convergence　speed.　©　The　Author(s)　2023.

Keyword：

Adaptive critic control nonlinear systems multi-step policy evaluation optimal tracking control

Author Community：

[ 1 ] [Li X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Li X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Li X.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Li X.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Ren J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Ren J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Ren J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Ren J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 11 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 12 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China

Reprint Author's Address：

Email：

Show more details

Related Keywords：

Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning
2025，NEURAL NETWORKS
Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear Systems
2025，IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Adjustable Iterative Q-Learning Schemes for Model-Free Optimal Tracking Control
2023，IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Static/dynamic event-triggered learning control for constrained nonlinear systems
2024，NONLINEAR DYNAMICS

Source ：

Complex Engineering Systems

ISSN： 2770-6249

Year： 2023

Issue： 4

Volume： 3

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： 5

Affiliated Colleges：

Get Fulltext

DOI Library Discovery Baidu Scholar Search SCOPUS

Type
Departments

All Years Choose Year From to