Frontiers of Electrical and Electronic Engineering >
Learning fuzzy controller and extended Kalman filter for sensorless induction motor robust against resistance variation
Received date: 05 Mar 2012
Accepted date: 23 Jul 2012
Published date: 05 Sep 2012
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This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter (EKF). Then, we introduce a new fuzzy logic speed controller based on learning by minimizing cost function. This strategy is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. The learning mechanism addresses the consequences of corrector rules, which are modified according to the comparison between the current speed of machine and an output signal or a desired trajectory. Thus, fuzzy associative memory is constructed to meet the criteria imposed in problems either control or pursuit. The consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady-state performance. The performance of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of induction motor drive.
Moulay Rachid DOUIRI , Mohamed CHERKAOUI . Learning fuzzy controller and extended Kalman filter for sensorless induction motor robust against resistance variation[J]. Frontiers of Electrical and Electronic Engineering, 2012 , 7(3) : 347 -355 . DOI: 10.1007/s11460-012-0206-y
| 1 |
Hasse K. On the dynamics of speed control of a static ac drive with a squirrel-cage induction machine. Dissertation for the Doctoral Degree. Darmstadt: Technische Hochschule Darmstadt, 1969
|
| 2 |
Blaschke F. The principle of field orientation as applied to the new transvector closed-loop control system for rotating field machines. Siemens Review, 1972, 34(5): 217-223
|
| 3 |
Ouhrouche M. Estimation of speed, rotor flux and rotor resistance in cage induction motor sensorless drive using the EKF algorithm. International Journal of Power and Energy Systems, 2002, 22(2): 103-109
|
| 4 |
Wade S, Dunnigan M W, Williams B W. Improving the accuracy of rotor resistance estimate for vector controlled induction machines. IEE Proceedings — Electric Power Applications, 1997, 144(5): 285-294
|
| 5 |
Kubota H, Matsuse K. Speed sensorless field-oriented control of induction motor with rotor resistance adaptation. IEEE Transactions on Industry Applications, 1994, 30(5): 1219-1224
|
| 6 |
Kalman R E. A new approach to linear filtering and prediction problems. Transactions of the ASME — Journal of Basic Engineering, 1960, D(82): 35-45
|
| 7 |
Schmidt G T. Linear and Nonlinear Filtering Techniques. In: Control and Dynamical Systems. New York: Academic Press, 1976
|
| 8 |
Kalman R E, Busy R S. New results in linear filtering and prediction theory. Transactions of the ASME — Journal of Basic Engineering, 1961, D(83): 95-101
|
| 9 |
Gibbs B P. Advanced Kalman Filtering, Least-Squares and Modeling. Hoboken, NJ: John Wiley and Sons, Inc., 2011, ch. 8
|
| 10 |
Zadeh L A. Fuzzy logic. Computer, 1988, 21(4): 83-92
|
| 11 |
Yager R R. Fuzzy logics and artificial intelligence. Journal of the Fuzzy Sets and Systems, 1997, 90(2): 193-198
|
| 12 |
Perry T S. Lotfi A. Zadeh, The inventor of fuzzy logic. IEEE Spectrum, 1995, 32(6): 32-35
|
| 13 |
Ouhrouche M A. EKF-based estimation of speed and rotor resistance in cage induction motor sensorless drive. In: Proceedings of International Association of Science and Technology for Development. Pennsylvania. 2000, 114-118
|
| 14 |
Mamdani E H. Application of fuzzy logic algorithms for control of simple dynamic plant. Proceedings of the IEEE, 1974, 121(12): 1585-1588
|
| 15 |
Douiri M R, Cherkaoui M, Essadki A. Genetic algorithms based fuzzy speed controllers for indirect field oriented control of induction motor drive. International Journal of Circuits, Systems and Signal Processing, 2012, 6(1): 21-28
|
| 16 |
Chen S M. A fuzzy reasoning approach for rule-based systems based on fuzzy logics. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, 26(5): 769-778
|
| 17 |
Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338-353
|
| 18 |
Slotine J J E, Li W. Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1991
|
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