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Precision Speed Control of PMSM Using Neural Network Disturbance Observer and Parameter Compensator  

Go, Jong-Seon (Dept.of Electric Electronics Information Engineering, Wonkwang University)
Lee, Yong-Jae (Dept.of Electric Electronics Information Engineering, Wonkwang University)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers B / v.51, no.10, 2002 , pp. 573-580 More about this Journal
Abstract
This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.
Keywords
PMSM; Precision speed control; Neural network disturbance observer; Parameter compensator;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Chen, F. 'Back-Propagation Neural Networks for Nonlinear Self-Tuning Adaptive Control', IEEE Control System Magazine, April, 1990   DOI   ScienceOn
2 Jong Sun Ko and Myung Joong Youn, 'Robust Digital Position Control of BLDD Motors using Neural Network with State Feedback', Proc. of the 3rd. International Workshop on Advanced Motion Control, pp. 852-861, March, 1994
3 K. Hornik, M. Stinchcombe and H. White, 'Multilayer feedforward networks are universal approximators,' Neural Networks, vol. 2, pp. 359-366, 1989   DOI   ScienceOn
4 C. T. Chen, Linear System Theory and Design, Holt, Rinehart and Winston, Inc., 1984
5 C. Y. Huang, T. C. Chen, C. L. Huang 'Robust Control of Induction Motor with A Neural-Network Load Torque Estimator and A Neural-Network Identification' IEEE Transaction on Industrial Electronics, vol. 46, no. 5, pp 990-998, 1999   DOI   ScienceOn
6 J. D. Landau, System Identification and Control Design. Englewood Cliffs, NJ, Prentice-Hall, 1990
7 G. C. Goodwin, K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ, Prentice-Hall, 1984
8 P. C. Krause, Analysis of electric machinery, McGraw-Hill, 1984
9 K. J. Astrom and B. Wittenmark Computer controlled system, Prentice Hall, International, 1997
10 J. S. Ko, J. H. Lee, S. k. Chung, and M. J. Youn 'A Robust Position Control of Brushless DC motor with Dead Beat Load Torque Observer' IEEE Transaction on Industrial Electronics, vol. 40, no. 5, pp. 512-520, 1993   DOI   ScienceOn
11 D. W. Novotny and R. D. Lorentz 'Introduction to field orientation and high performance AC drives' IEEE-IAS Tutorial Course, 1986