An induction motor position controller optimally designed with fuzzy phase-plane control and genetic algorithms

Abstract A new controller that employs fuzzy phase-plane control (FPPC) and genetic algorithms (GAs) is presented herein. For optimal position control of an induction motor, it is equipped with a modified evolutionary direction operator (MEDO). As the FPPC technique is applied, the proposed controller has many advantages, such as satisfactory control performance under a wide range of operating conditions is obtained, multifarious expert experiences and defuzziness are not required, and it has a quicker response than conventional fuzzy controllers do. GAs optimize the parameters of fuzzy phase-plane function, and the MEDO determines a better evolutionary direction for solution searching. The proposed technique was successfully developed and applied to the position control of an induction motor. Simulation results confirmed that the approach is computationally efficient and has excellent control performance.

[1]  Armando Bellini,et al.  A Microcomputer-Based Optimal Control System to Reduce the Effects of the Parametric Variations and Speed Measurement Errors in Induction Motor Drives , 1986, IEEE Transactions on Industry Applications.

[2]  Osamu Inoue,et al.  New evolutionary direction operator for genetic algorithms , 1995 .

[3]  C.Y. Won,et al.  Position control of induction motor with a new fuzzy-sliding mode controller , 1993, Conference Record of the Power Conversion Conference - Yokohama 1993.

[4]  Chang-Ming Liaw,et al.  Design and implementation of a high-performance field-oriented induction motor drive , 1991 .

[5]  Y.F. Li,et al.  Development of fuzzy algorithms for servo systems , 1989, IEEE Control Systems Magazine.

[6]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[7]  Toly Chen,et al.  A fuzzy sliding mode controller for induction motor position control , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[8]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[9]  Akhtar Kalam,et al.  Comparison of fuzzy logic based and rule based power system stabilizer , 1992, [Proceedings 1992] The First IEEE Conference on Control Applications.

[10]  Vadim I. Utkin,et al.  Sliding mode control design principles and applications to electric drives , 1993, IEEE Trans. Ind. Electron..

[11]  Ching-Tsai Pan,et al.  Design and implementation of an adaptive controller for current-fed induction motor , 1988 .

[12]  O. Malik,et al.  A fuzzy logic based stabilizer for a synchronous machine , 1991 .

[13]  S. Ahmed-Zaid,et al.  A fuzzy velocity controller for DC drives , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[14]  Bimal K. Bose,et al.  Power Electronics and Ac Drives , 1986 .

[15]  Bernard P. Zeigler,et al.  Designing fuzzy net controllers using genetic algorithms , 1995 .

[16]  S. C. Kim,et al.  New fuzzy-sliding mode controller for position control for induction motor , 1993, Proceedings Eighth Annual Applied Power Electronics Conference and Exposition,.

[17]  A. Ishigame,et al.  Design of electric power system stabilizer based on fuzzy control theory , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.