A memory state-feedback controller via random pocket dropouts for multi-agent systems with external disturbance

Volume 33, Issue 1, pp 71--86 http://dx.doi.org/10.22436/jmcs.033.01.06

Publication Date: November 26, 2023 Submission Date: August 11, 2023 Revision Date: September 20, 2023 Accteptance Date: November 01, 2023

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Authors

J. Thipcha - Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai 52290, Thailand. A. Stephen - Center for Computational Modeling, Chennai Institute of Technology, Chennai-600 069, India. A. Srinidhi - Department of Mathematics, Alagappa University, Karaikudi-630 004, India. R. Raja - Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi-630 004, India. - Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon. N. Kaewbanjak - Faculty of Science at Sriracha, Kasetsart University, Sriracha Campus, Chon Buri, 20230, Thailand. K. Mukdasai - Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand. P. Singkibud - Department of Applied Mathematics and Statistics, Faculty of Science and Liberal Arts, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand. N. Yotha - Department of Applied Mathematics and Statistics, Faculty of Science and Liberal Arts, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand.

Abstract

This study explores the challenge of achieving consensus in multi-agent systems (MASs) when facing the random packet dropouts and disturbances. It employs memory state-feedback control (MSFC) in the context of undirected graphs and specified leader agents. The analysis focuses on mean square consensus, considering MASs within strongly connected networks or networks with undirected spanning trees. The MSFC approach is developed to ensure asymptotic consensus despite packet dropouts and also to reduce the impact of disturbances. Specifically, the consensus analysis leverages the Lyapunov-Krasovskii functional (LKF) framework, and the necessary conditions for implementing the proposed MSFC are established using linear matrix inequalities (LMIs). The system, augmented with an \(H_{\infty}\) attenuation level, is guaranteed to achieve asymptotic mean-square stability according to the provided criteria. In conclusion, two examples are provided to illustrate the effectiveness and practicality of the proposed control mechanism.

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Keywords

• Memory state-feedback control
• multi-agent systems
• Kronecker product
• leader-following consensus
• linear matrix inequality

MSC

•  93A16
•  93B52
•  93D20

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