A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems

In this paper the distributed leader-follower consensus tracking problem is investigated for unknown nonlinear non-affine discrete-time multi-agent systems. Via a dynamic linearization method both for the agent system and the local ideal distributed controller, a distributed adaptive control scheme...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.207884-207893
Main Authors: Yu, Xian, Jin, Shangtai, Liu, Genfeng, Lei, Ting, Ren, Ye
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
consensus tracking
Control systems design
Data models
data-driven control
Decentralized control
Discrete time systems
Dynamic linearization
Heuristic algorithms
Multi-agent systems
Multiagent systems
Nonlinear control
Nonlinear systems
Optimization
Topology
Tracking problem
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Summary:In this paper the distributed leader-follower consensus tracking problem is investigated for unknown nonlinear non-affine discrete-time multi-agent systems. Via a dynamic linearization method both for the agent system and the local ideal distributed controller, a distributed adaptive control scheme is proposed in this paper using the Newton-type optimization method. The proposed approach is data-driven since only the local measurement information among neighboring agents is utilized in the control system design. The consensus tracking stabilities of the proposed approach are rigorously guaranteed in the cases of fixed and switching communication topologies. The simulations are conducted to verify the effectiveness of the proposed approach.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3038629