Data-based bipartite formation control for multi-agent systems with communication constraints
Authors: Wang, J., Zhao, H., Yu, H., Yang, R., Li, J.
Journal: Science Progress
Publication Date: 01/01/2024
Volume: 107
Issue: 1
eISSN: 2047-7163
ISSN: 0036-8504
DOI: 10.1177/00368504241227620
Abstract:This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant’s quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.
Source: Scopus
Data-based bipartite formation control for multi-agent systems with communication constraints.
Authors: Wang, J., Zhao, H., Yu, H., Yang, R., Li, J.
Journal: Sci Prog
Publication Date: 2024
Volume: 107
Issue: 1
Pages: 368504241227620
eISSN: 2047-7163
DOI: 10.1177/00368504241227620
Abstract:This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.
Source: PubMed