Neural‐network‐based constrained optimal coordination for heterogeneous uncertain nonlinear multi‐agent systems

In this article, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi‐agent systems described by high‐order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady‐state cons...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 32; no. 14; pp. 8134 - 8146
Main Authors Tang, Yutao, Wang, Ding
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 25.09.2022
Subjects
Algorithms
Constraints
Coordination
Disturbances
multi‐agent system
neural network
Neural networks
nonlinear control
Nonlinearity
optimal coordination
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Summary:In this article, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi‐agent systems described by high‐order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady‐state constraint about its output. We develop a composite distributed controller for each agent by a combination of internal model and neural network techniques. All agent outputs are proven to reach the constrained minimal point of the aggregate objective function with bounded residual errors irrespective of the unknown nonlinearities and external disturbances. Two examples are finally given to demonstrate the effectiveness of the algorithm.
Bibliography:Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 61973043; Beijing Natural Science Foundation, Grant/Award Numbers: JQ19013
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6263