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Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints

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Book cover Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11954))

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Abstract

Consensus control of a class of high-order nonlinear multi-agent systems subject to multiple state constraints and input saturation is studied in this work. Barrier functions are employed to design a distributed controller which achieves consensus without violating the state constraints and input saturation provided that some feasibility conditions on the initial states and controller parameters are satisfied. The feasibility conditions can be checked off-line. Backstepping method and Lyapunov analysis are employed to study the convergence properties of the designed controller.

This work is supported by the Natural Science Foundation of Jiangsu Province under Grant BK20170695, the National Natural Science Foundation of China under Grants 61703094, the National Priority Research Project NPRP 9 166-1-031 from Qatar National Research Fund.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing, 211189, China

    Junjie Fu, Guanghui Wen & Yuezu Lv

  2. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

Authors
  1. Junjie Fu
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  2. Guanghui Wen
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  3. Yuezu Lv
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  4. Tingwen Huang
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Corresponding author

Correspondence to Junjie Fu .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Fu, J., Wen, G., Lv, Y., Huang, T. (2019). Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_41

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  • DOI: https://doi.org/10.1007/978-3-030-36711-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36710-7

  • Online ISBN: 978-3-030-36711-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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