Distributed Bipartite Consensus of Multi-Agent Systems via Disturbance Rejection Control Strategy

Subramanian Manickavalli, Arumugam Parivallal, Ramasamy Kavikumar () and Boomipalagan Kaviarasan ()
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Subramanian Manickavalli: Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India
Arumugam Parivallal: School of Electrical Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Ramasamy Kavikumar: School of Electrical Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Boomipalagan Kaviarasan: Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India

Mathematics, 2024, vol. 12, issue 20, 1-13

Abstract: This work aims to focus on analyzing the consensus control problem in cooperative–competitive networks in the occurrence of external disturbances. The primary motive of this work is to employ the equivalent input-disturbance estimation technique to compensate for the impact of external disturbances in the considered multi-agent system. In particular, a suitable low-pass filter is implemented to enhance the accuracy of disturbance estimation performance. In addition, a specific signed, connected, and structurally balanced undirected communication graph with positive and negative edge weights is considered to express the cooperation–competition communication among neighboring agents. The cooperative–competitive multi-agent system reaches its final state with same magnitude and in opposite direction under the considered structurally balanced graph. By utilizing the properties of Lyapunov stability theory and graph theory, the adequate conditions assuring the bipartite consensus of the examined multi-agent system are established as linear matrix inequalities. An illustrative example is delivered at the end to check the efficacy of the designed control scheme.

Keywords: multi-agent systems; bipartite consensus; observer-based control; disturbance rejection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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