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

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 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 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

[1]  Faa-Jeng Lin,et al.  Induction motor servo drive with adaptive rotor time-constant estimation , 1998 .

[2]  Graham E. Dawson,et al.  Peak thrust operation of linear induction machines from parameter identification , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[3]  Rong-Jong Wai,et al.  Adaptive backstepping control using recurrent neural network for linear induction motor drive , 2002, IEEE Trans. Ind. Electron..

[4]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[5]  Chih-Hong Lin,et al.  On-line gain-tuning IP controller using RFNN , 2001 .

[6]  Germano Lambert-Torres,et al.  A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems , 1998, IEEE Trans. Neural Networks.

[7]  Bernard Friedland,et al.  On adaptive friction compensation , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[8]  Gary B. Lamont,et al.  Parallel real-valued genetic algorithms for bioremediation optimization of TCE-contaminated groundwater , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Pai-Yi Huang,et al.  Real-coded genetic algorithm based fuzzy sliding-mode control design for precision positioning , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[10]  Rong-Jong Wai,et al.  A supervisory fuzzy neural network control system for tracking periodic inputs , 1999, IEEE Trans. Fuzzy Syst..

[11]  I. Boldea,et al.  Linear Electric Actuators and Generators: Linear Electric Actuators and Generators , 1997 .

[12]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[13]  Loo Hay Lee,et al.  Developing a self-learning adaptive genetic algorithm , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).