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Neural network adaptive dynamic sliding mode formation control of multi-agent systems

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  • Yang Fei
  • Peng Shi
  • Cheng-Chew Lim

Abstract

This paper considers the problem of achieving time-varying formation for second-order multi-agent systems with actuator hysteresis, unknown system dynamics and external disturbances. A novel adaptive dynamic sliding mode scheme is developed to control a group of agents to follow desired trajectories. First, a dynamic sliding mode approach based on local formation tracking error is utilised to reject external disturbances and obtain smooth and chattering-free control input. Then, Chebyshev neural network is employed to estimate the nonlinear function related to the system's dynamic equation. A smooth projection law is also applied to regulate the output of the neural network. Moreover, a Bouc–Wen hysteresis compensator has been added to the current control law to offset the known actuator hysteresis effect. Finally, a numerical simulation based on a multiple omni-directional robot system is presented to illustrate the performance of the proposed control law.

Suggested Citation

  • Yang Fei & Peng Shi & Cheng-Chew Lim, 2020. "Neural network adaptive dynamic sliding mode formation control of multi-agent systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(11), pages 2025-2040, July.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:11:p:2025-2040
    DOI: 10.1080/00207721.2020.1783385
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    Cited by:

    1. Derakhshannia, Mehran & Moosapour, Seyyed Sajjad, 2022. "Disturbance observer-based sliding mode control for consensus tracking of chaotic nonlinear multi-agent systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 610-628.

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