Developing a self-learning adaptive genetic algorithm

Lee, Loo Hay; Fang, Yingli

doi:10.1109/wcica.2000.860046

Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393)

DOI: 10.1109/wcica.2000.860046

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Developing a self-learning adaptive genetic algorithm

Loo Hay Lee

Yingli Fang²

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Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Lin

Huang

2006

2006 1ST IEEE Conference on Industrial Electronics and Applications

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A recurrent fuzzy neural network (RFNN) using genetic algorithm (GA) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. 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 discretetype Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a realtime 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 RFNN using GA controller are described in detail. Finally, experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.

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Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Lin

Huang

2006

2006 1ST IEEE Conference on Industrial Electronics and Applications

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Developing a self-learning adaptive genetic algorithm

Cited by 1 publication

References 7 publications

Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

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