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Observer-based robust adaptive neural control for nonlinear multi-agent systems with quantised input

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  • Xing-Yu Zhang
  • Yuan-Xin Li
  • Jiaxu Sun

Abstract

This article discusses the issue of robust adaptive neural network (NN) consensus tracking control for nonlinear strict-feedback multi-agent systems with quantised input. By combining the neural network approach with robust techniques, a novel switching function is introduced to guarantee the tracking performance of this system. To estimate the unmeasured state, an NN-based adaptive state observer is developed. Based on backstepping dynamic surface control algorithms, a robust output feedback controller is constructed to guarantee that all signals in the closed-loop system remain globally uniformly ultimately bounded. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented algorithm.

Suggested Citation

  • Xing-Yu Zhang & Yuan-Xin Li & Jiaxu Sun, 2024. "Observer-based robust adaptive neural control for nonlinear multi-agent systems with quantised input," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(6), pages 1270-1282, April.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:6:p:1270-1282
    DOI: 10.1080/00207721.2024.2304133
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