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A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems
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DOI：10.1109/ACCESS.2020.3038629.png)
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Abstract
En 中文
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.
Keywords:
Adaptive control
Decentralized control
Heuristic algorithms
Data models
Multi-agent systems
Adaptation models
Topology
Dynamic linearization
data-driven control
adaptive control
multi-agent systems
consensus tracking
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