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Adaptive synchronisation of unknown nonlinear networked systems with prescribed performance

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  • Hashim. A. Hashim
  • Sami El-Ferik
  • Frank L. Lewis

Abstract

This paper proposes an adaptive tracking control with prescribed performance function for distributive cooperative control of highly nonlinear multi-agent systems. The use of such approach confines the tracking error within a large predefined set to a predefined smaller set. The key idea is to transform the constrained system into unconstrained one through the transformation of the output error. Agents’ dynamics are assumed unknown, and the controller is developed for a strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying the necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness for the transformed error as well as a bounded adaptive estimate of the unknown parameters and dynamics. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous multi-agent system with uncertain time-variant parameters and external disturbances.

Suggested Citation

  • Hashim. A. Hashim & Sami El-Ferik & Frank L. Lewis, 2017. "Adaptive synchronisation of unknown nonlinear networked systems with prescribed performance," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(4), pages 885-898, March.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:4:p:885-898
    DOI: 10.1080/00207721.2016.1226984
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    1. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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    2. Fengfa Yue & Xingfei Li & Cheng Chen & Wenbin Tan, 2017. "Adaptive integral backstepping sliding mode control for opto-electronic tracking system based on modified LuGre friction model," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(16), pages 3374-3381, December.

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