A robust integrator algorithm with genetic based fuzzy controller feedback for direct vector control

Abstract The voltage model used for direct vector control has in the flux calculation process an open integration problem, which is generally solved with a feedback loop. In this paper, a new design method is developed for the feedback loop of the integrator. The method, as apart from standards in the literature, uses a fuzzy controller. Fuzzy controllers are knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate human knowledge into their knowledge base. The determination of these rules and membership functions is one the key problems when designing fuzzy controllers, and is generally affected by subjective decisions. In this study, a fuzzy controller with rules and membership functions determined by genetic algorithms (GAs) in this study is designed and tested on various motors of different power ratings. The proposed method is simulated by using MATLAB/SIMULINK and implemented on an experimental system using a TMS320C31 digital signal processor.

[1]  R. Colyer.  Modern Electrical Drives , 2000 .

[2]  H. Takagi,et al.  Integrating Design Stages of Fuzzy Systems using Genetic Algorithms 1 , 1993 .

[3]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[4]  Francisco Herrera,et al.  A learning process for fuzzy control rules using genetic algorithms , 1998, Fuzzy Sets Syst..

[5]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[6]  Fuzzy Logic in Control Systems : Fuzzy Logic , 2022 .

[7]  George K. I. Mann,et al.  New methodology for analytical and optimal design of fuzzy PID controllers , 1999, IEEE Trans. Fuzzy Syst..

[8]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[9]  Bimal K. Bose,et al.  A programmable cascaded low-pass filter-based flux synthesis for a stator flux-oriented vector-controlled induction motor drive , 1997, IEEE Trans. Ind. Electron..

[10]  Mehmet Kaya,et al.  Determination of fuzzy logic membership functions using genetic algorithms , 2001, Fuzzy Sets Syst..

[11]  Kouki Matsuse,et al.  Lower speed range drive for sensorless vector controlled induction machines with stator voltage offset compensation method , 2000 .

[12]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[13]  J.J.A. van der Burgt.  The voltage/current model in field-oriented AC drives at very low flux frequencies , 1996 .

[14]  T. Ohtani,et al.  Vector control of induction motor without shaft encoder , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .