期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 66, 期 12, 页码 6179-6186出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3062594
关键词
Directed graphs; Uncertainty; Multi-agent systems; Laplace equations; Vehicle dynamics; Adaptation models; Switches; Directed graphs; model reference adaptive consensus (MRACon); multiagent systems; switching topologies; uncertainties
资金
- National Natural Science Foundation of China [62073098, U1913209, 61991400, 61991403, 61803053, 61773081, 61860206008, 61933012]
- National Natural Science Foundation of Guangdong [2019A1515012076]
- Shenzhen Basic Research Program [JCYJ20180306171828190, JCYJ20200109113210134, Dec. 2018 [28]]
This article addresses the challenging leaderless consensus problem of uncertain multiagent systems under general directed graphs by proposing the model reference adaptive consensus scheme. By designing linear reference models, dynamic or position consensus is achieved without the need for communication of virtual signals, only through interaction of relative states among the agents.
Due to the nonsymmetric property of the associated Laplacian matrix and the existence of uncertainties in the agent dynamics, the leaderless consensus problem of uncertain multiagent systems under general directed graphs is challenging. Motivated by the classical model reference adaptive control, in this article, we propose a simple yet efficient scheme, called the model reference adaptive consensus, by arranging each agent a reference output to track, where the output is generated by a linear reference model with the relative state measurements as input. We consider two typical agent dynamics, namely, the general linear dynamics with matching uncertainties and the second-order dynamics with extended matching uncertainties. Different linear reference models are designed for the above uncertain agent dynamics. For the first one, dynamical consensus is achieved under a fixed directed graph containing a directed spanning tree. For the latter one, position consensus is achieved under switching uniformly jointly connected graphs. In the proposed algorithms, only the relative states are interacted among the agents and there is no requirement on the communication of virtual signals.
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