A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

Zhao, Huarong; Peng, Li; Wu, Peiliang; Yu, Hongnian

Abstract

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents’ dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method.

• Journal Article Type: Article
• Acceptance Date: Oct 17, 2021
• Online Publication Date: Dec 2, 2021
• Publication Date: 2022-05
• Deposit Date: Jan 17, 2022
• Journal: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
• Print ISSN: 0959-6518
• Electronic ISSN: 2041-3041
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 236
• Issue: 5
• Pages: 1038-1048
• DOI: https://doi.org/10.1177/09596518211060976
• Keywords: Multi-agent systems, bipartite consensus, bounded disturbances, model-free adaptive control, data-driven control
• Public URL: http://researchrepository.napier.ac.uk/Output/2830158