Fuzzy sliding-mode controlled induction motor servo drive based on real-time genetic algorithm

Wen Der Chou, Faa Jeng Lin, Po Kai Huang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

A genetic algorithm (GA)-based fuzzy sliding-mode control system is proposed in this study. The proposed controller combines the merits of the sliding-mode control, the fuzzy inference mechanism and the GA. First, a sliding-mode controller with an integral-operation switching surface is designed. Then, a fuzzy sliding-mode controller is investigated, in which a simple fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation of the membership functions is investigated to estimate the optimal bound of the uncertainties at the adaptive fuzzy sliding-mode controller. Since most of the parameters of the fuzzy inference mechanism are constants, optimal response usually cannot be obtained due to the existence of uncertainties. Therefore, a real-time GA with real-value coding is developed to search the optimal parameters of the fuzzy inference mechanism to improve the control performance. Finally, the position control of an induction motor (IM) servo drive using the proposed control strategy is illustrated.

Keywords

  • Adaptive fuzzy sliding-mode controller
  • Genetic algorithm
  • Induction motor servo drive
  • Sliding-mode controller

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