Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

Yulian JiangCollege of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning Province, Shenyang 110819, ChinaCorrespondence[email protected]
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Jianchang LiuCollege of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning Province, Shenyang 110819, ChinaView further author information

Shubin TanCollege of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning Province, Shenyang 110819, ChinaView further author information

Pingsong MingCollege of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning Province, Shenyang 110819, ChinaView further author information

Abstract

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H_∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H_∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.

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Notes on contributors

Yulian Jiang

Yulian Jiang is a PhD candidate at Northeastern University. Her research interests cover intelligent control theory and application and multi-agent systems.

Jianchang Liu

Jianchang Liu is a Professor at the School of Information Science and Engineering, Northeastern University. His research interests cover modelling, control and optimisation for complex process, fault diagnosis, intelligent control theory and application.

Shubin Tan

Shubin Tan is an Associate Professor at the School of Information Science and Engineering, Northeastern University. His research interests cover modelling, control and optimisation for complex process, rolling automation, fault diagnosis, intelligent control theory and application.

Pingsong Ming

Pingsong Ming is a PhD candidate at Northeastern University. His research interests cover intelligent control theory and application, rolling automation and multi-agent systems.

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