State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach

Journal article

Yang, Yang, Fan, Xin, Xu, Chuang, Wu, Jinran and Sun, Baohua. (2021). State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach. Neurocomputing. 458, pp. 284-296. https://doi.org/10.1016/j.neucom.2021.05.046
AuthorsYang, Yang, Fan, Xin, Xu, Chuang, Wu, Jinran and Sun, Baohua
Abstract

A state consensus cooperative adaptive dynamic programming (ADP) control strategy is proposed for a nonlinear multi-agent system (MAS) with output constraints. On the basis of the transformation function, state models of leader and followers are transformed into affine ones. By using a monotonically increasing mapping function, the state-consensus cooperative control problem for an MAS with output constraints is equivalently transformed into a cooperative approximately optimal control one for an affine MAS. Then, a neural network observer is constructed for estimation of inner states, and, by graph theory and ADP method, the state consensus cooperative ADP control strategy is developed. The proposed strategy guarantees the performance index of the transformed system is approximately optimal. Furthermore, the stability analysis of whole closed-loop system is presented. Through the Lyapunov Theorem, we prove that the states of the MAS achieve consensus and the output signals of the followers satisfy the constraints. Also, all signals of the closed-loop MAS are bounded, and the trajectory of the leader node is cooperative bounded. The theoretical analysis and effectiveness of the strategy are verified by both a physical and a numerical example.

Keywordsadaptive dynamic programming; output constraints; transformation function; neural network observer
Year2021
JournalNeurocomputing
Journal citation458, pp. 284-296
PublisherElsevier B.V.
ISSN0925-2312
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neucom.2021.05.046
Scopus EID2-s2.0-85108664439
Page range284-296
FunderNational Natural Science Foundation of China (NSFC)
Natural Science Foundation of Jiangsu Province
Nanjing University of Posts and Telecommunications (NUPTSF), China
Australian Research Council (ARC)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online27 May 2021
Publication process dates
Accepted27 May 2021
Deposited06 Jul 2023
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant ID61873130
61833011
61833008
BK20191377
BK20191376
NY220102
NY220194
2020XZZ11
CE140100049
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