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Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

Author

Listed:
  • Wenqiang Wu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Jiarui Liu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Fangyi Li

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    Key Laboratory for Safety Control of Bridge Engineering, Ministry of Education and Hunan Province, Changsha University of Science and Technology, Changsha 410114, China)

  • Yuanqing Zhang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Zikai Hu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

For a class of multiagent systems with an unknown time-varying input dead-zone, a prescribed settling time adaptive neural network consensus control method is developed. In practical applications, some control signals are difficult to use effectively due to the extensive existence of an input dead-zone. Moreover, the time-varying input gains further seriously degrade the performance of the systems and even cause system instability. In addition, multiagent systems need frequent communication to ensure a system’s consistency. This may lead to communication congestion. To solve this problem, an event-triggered adaptive neural network control method is proposed. Further, combined with the prescribed settling time transform function, the developed consensus method greatly increases the convergence rate. It is demonstrated that all followers of multiagent systems can track the virtual leader within a prescribed time and not exhibit Zeno behavior. Finally, the theoretical analysis and simulation verify the effectiveness of the designed control method.

Suggested Citation

  • Wenqiang Wu & Jiarui Liu & Fangyi Li & Yuanqing Zhang & Zikai Hu, 2023. "Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone," Mathematics, MDPI, vol. 11(4), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:988-:d:1069088
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    References listed on IDEAS

    as
    1. Xu, Jiahong & Wang, Lijie & Liu, Yang & Sun, Jize & Pan, Yingnan, 2022. "Finite-time adaptive optimal consensus control for multi-agent systems subject to time-varying output constraints," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    2. Lili Zhang & Bing Chen & Chong Lin & Yun Shang, 2021. "Fuzzy adaptive finite-time consensus tracking control for nonlinear multi-agent systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(7), pages 1346-1358, May.
    3. Zhao, Guangtong & Cao, Liang & Li, Xiaomeng & Zhou, Qi, 2022. "Observer-based dynamic event-triggered control for nonstrict-feedback stochastic nonlinear multiagent systems," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    4. Wu, Li-Bing & Park, Ju H. & Xie, Xiang-Peng & Liu, Ya-Juan & Yang, Zhi-Chun, 2020. "Event-triggered adaptive asymptotic tracking control of uncertain nonlinear systems with unknown dead-zone constraints," Applied Mathematics and Computation, Elsevier, vol. 386(C).
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