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Controller Tuning Approach with robustness, stability and dynamic criteria for the original AVR System

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  • Blondin, M.J.
  • Sicard, P.
  • Pardalos, P.M.

Abstract

The automatic voltage regulator (AVR) system has received much interest in the last decade. Efforts are made to find a parameter set for proportional–integral–derivative based (PID) controllers to obtain a fast onset time with minimal overshoot. To assure system stability and robustness, these characteristics must be considered during the optimization process. Cost functions have been already proposed in the literature. However, either they have a high number of weighting parameters to set to achieve a satisfactory trade-off between dynamic performance, robustness and stability or they consider only robustness and stability. This paper proposes a new optimization framework using a simple performance criterion to achieve rapid time response characteristics as well as to meet robustness and stability requirements. The performance of the approach is confirmed by comparing its results to those obtained with two previously published performance criteria and the results obtained with the systune command in Matlab® and the MathWorks® PID tuning algorithm. The results obtained with the proposed approach present a better trade-off between dynamic performance, robustness and stability.

Suggested Citation

  • Blondin, M.J. & Sicard, P. & Pardalos, P.M., 2019. "Controller Tuning Approach with robustness, stability and dynamic criteria for the original AVR System," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 163(C), pages 168-182.
    • Handle: RePEc:eee:matcom:v:163:y:2019:i:c:p:168-182
    DOI: 10.1016/j.matcom.2019.02.019
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    References listed on IDEAS

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    1. Breban, Stefan & Saudemont, Christophe & Vieillard, Sébastien & Robyns, Benoît, 2013. "Experimental design and genetic algorithm optimization of a fuzzy-logic supervisor for embedded electrical power systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 91-107.
    2. Sergeyev, Yaroslav D. & Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2017. "Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 141(C), pages 96-109.
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    Cited by:

    1. Mihailo Micev & Martin Ćalasan & Diego Oliva, 2020. "Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm," Mathematics, MDPI, vol. 8(7), pages 1-22, July.

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