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Consensus of second-order multi-agent systems using partial agents’ velocity measurements

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Abstract

This paper studies the consensus problems of multi-agent systems with different inertia and partial agents’ nonlinear velocity measurements. Firstly, based on one agent’s velocity measurements, two consensus protocols are developed to guarantee that all agents asymptotically achieve stationary consensus. Secondly, a new protocol, which needs \(n-1\) agents’ velocity measurements, rather than n, is presented such that all agents achieve consensus. Thirdly, two new consensus states, which are dependent on not only the initial positions and velocities, but also the masses and velocity gains of all agents, are obtained analytically. Numerical examples are given for three network topologies to illustrate the effectiveness of the derived algorithms.

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Acknowledgments

This work was supported by the National Science Foundation of China (Grant No. 61273117), the Postdoctoral Science Foundation of Zhejiang Province (Grant No. Z42103001), the Natural Science Foundation of Zhejiang Province (Grant No. LY14F030010) and the Foundation of Anhui for Scientific Research (KJHS2015B11). The authors are very grateful to reviewers and editor for their valuable comments on which the quality of this article has been improved.

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Authors and Affiliations

  1. College of Science, Zhejiang University of Technology, Hangzhou, 310023, China

    Shan Cheng & Dongmei Zhang

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Shan Cheng & Li Yu

  3. School of Applied Mathematics, Beijing Normal University, Zhuhai, Zhuhai, 519085, China

    Lujing Huo

  4. Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Australia

    Jinchen Ji

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  1. Shan Cheng
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  2. Li Yu
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  3. Dongmei Zhang
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  4. Lujing Huo
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  5. Jinchen Ji
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Correspondence to Shan Cheng.

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Cheng, S., Yu, L., Zhang, D. et al. Consensus of second-order multi-agent systems using partial agents’ velocity measurements. Nonlinear Dyn 86, 1927–1935 (2016). https://doi.org/10.1007/s11071-016-3005-9

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  • DOI: https://doi.org/10.1007/s11071-016-3005-9

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