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Optimal Tuning Strategy for MIMO Fuzzy Predictive Controllers

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  • Adel Taeib

    (Department of Electrical Engineering, National High School of Engineers of Tunis (ENSIT), Tunis, Tunisia)

  • Abdelkader Chaari

    (Department of Electrical Engineering, National High School of Engineers of Tunis (ENSIT), Tunis, Tunisia)

Abstract

This paper describes the development of a method to optimally tune constrained MPC algorithms for a nonlinear process. The T-S model is firstly established for nonlinear systems and its sequence parameters of fuzzy rules are identified by local recursive least square method. The proposed method is obtained by minimizing performance criteria in the worst-case conditions to control the process system, thus assuring robustness to the set of optimum tuning parameters. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a version of the particle swarm optimization technique. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results.

Suggested Citation

  • Adel Taeib & Abdelkader Chaari, 2015. "Optimal Tuning Strategy for MIMO Fuzzy Predictive Controllers," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 4(4), pages 87-99, October.
    • Handle: RePEc:igg:jfsa00:v:4:y:2015:i:4:p:87-99
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