A genetic algorithm based recurrent fuzzy neural network for linear induction motor servo drive

Faa Jeng Lin, Po Kai Huang, Wen Der Chou

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

A genetic algorithm (GA) based recurrent fuzzy neural network (RFNN) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. The GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides good control performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.

Keywords

  • Genetic algorithm
  • Linear induction motor
  • Recurrent fuzzy neural network

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