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A neural multicontroller for strongly nonlinear systems

Author

Listed:
  • Yassin Farhat
  • Ali Zribi
  • Asma Atig
  • Ridha Ben Abdennour

Abstract

The application of neural networks can present some limitations for the control of strongly nonlinear systems. In this paper, a new control scheme based on a neural multicontroller (NMC) is proposed. Indeed, the developed strategy considers a set of local neural controllers which adapt their parameters thanks to an online adaptation algorithm. The instantaneous choice of the adequate local controller is based on a switching mechanism. The advantages of the proposed method are (1) to avoid the computational complexity issues due to the search for an optimal adapting rate when a single neural controller is used, and in presence of strongly nonlinear systems, and (2) to improve the control law by selecting the suitable controller which generates the most valid control law satisfying the desired closed-loop performances. Numerical examples are illustrated to evaluate the performance of the proposed control scheme compared to the classical one. An application of the new strategy on a chemical reactor model validates satisfactory results.

Suggested Citation

  • Yassin Farhat & Ali Zribi & Asma Atig & Ridha Ben Abdennour, 2022. "A neural multicontroller for strongly nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(8), pages 1778-1795, June.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:8:p:1778-1795
    DOI: 10.1080/00207721.2021.2024295
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