Self-tuning fuzzy logic controller for direct torque control of slip energy recovery system

Proposes a self-tuning fuzzy logic controller for the slip energy recovery system. The control system is designed to maintain efficiency of motor by keeping the speed constant and the good transient response by using direct torque control. Fuzzy logic controller has been designed by genetic algorithm optimization technique as a means to determine and optimize the fuzzy logic controller design. In the proposed approach normalization factors and/or membership function parameters and/or the controller policy, are translated into bit-strings. These bit-strings are processed by the genetic algorithm and if the selection process as well as the objective function is chosen properly, a near-optimal solution can be found. To examine the efficiency of the proposed approach, a self-tuning fuzzy logic controller for direct torque control of the wound rotor induction motor drive is designed for the current in DC link circuit. A particular objective function is chosen to achieve a high dynamic performance. The simulation results demonstrate a significant enhancement in shortening the development time, and improving system performance over a conventional fuzzy logic controller.

[1]  M. Jamshidi,et al.  Fuzzy logic and control: software and hardware applications , 1993 .

[2]  T. Furuya,et al.  Adaptive fuzzy-neuro controller for speed of wound rotor induction motor with slip energy recovery , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[3]  E.P. Nowicki,et al.  A self-organizing and self-tuning fuzzy logic controller for field oriented control of induction motor drives , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[4]  K. Shida,et al.  A multi-operator self-tuning genetic algorithm for fuzzy control rule optimization , 1996, Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation.

[5]  S. Tunyasrirut,et al.  Fuzzy-Neuro Controller for Speed of Slip Energy Recovery and Active Power Filter Compensator , 2000 .

[6]  H. Le-Huy,et al.  Control of a direct-drive DC motor by fuzzy logic , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[7]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[8]  G. Vukovich,et al.  A fuzzy genetic algorithm with effective search and optimization , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[9]  T. Furuya,et al.  Adaptive Control for Speed of Wound Rotor Induction Motor With Slip Energy Recovery , 1998 .

[10]  T. Furuya,et al.  Fuzzy logic control for speed of wound rotor induction motor with slip energy recovery , 1999, SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456).