Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design

In this paper, a novel multiobjective optimization method based on a genetic-fuzzy algorithm (GFA) is proposed. The new GFA method is used for optimal design of a switched reluctance motor (SRM) with two objective functions: high efficiency and low torque ripple. The results of the optimal design for an 8/6, four-phase, 4 kW, 250 V, 1500 r.p.m. SRM show improvement in both efficiency and torque ripple of the motor.

[1]  Mehdi Moallem,et al.  An improved magnetic equivalent circuit method for predicting the characteristics of highly saturated electromagnetic devices , 1998 .

[2]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[3]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[4]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Chin-Teng Lin,et al.  A neural fuzzy control system with structure and parameter learning , 1995 .

[6]  H. Ishibuchi,et al.  MOGA: multi-objective genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[7]  A. A. Arkadan,et al.  Switched reluctance motor drive systems dynamic performance prediction and experimental verification , 1994 .

[8]  T.J.E. Miller,et al.  Nonlinear theory of the switched reluctance motor for rapid computer-aided design , 1990 .

[9]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[10]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[11]  Mehdi Moallem.  Performance characteristics of switched -reluctance motor drive , 1989 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[14]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[15]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[16]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[17]  Timothy J. E. Miller,et al.  Switched Reluctance Motors and Their Control , 1993 .

[18]  B. A. White,et al.  Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design , 1997 .