Mechanical Parameter Identification of Servo Systems using Robust Support Vector Regression

Support Vector Regression을 이용한 서보 시스템의 기계적 상수 추정

  • Published : 2005.10.01

Abstract

The overall performance of AC servo system is greatly affected the uncertainties of unpredictable mechanical parameter variations and external load disturbances. To overcome this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an on-line identification method of mechanical parameters/load disturbances for AC servo system using support vector regression(SVR). The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with time-varying/nonlinear parameters.

서보 시스템의 전체 제어 성능은 기계적 상수의 변화와 부하 토크의 영향을 크게 받는다. 그러므로 서보 시스템의 성능을 향상시키기 위해서는 기계적 상수와 부하 토크를 정확히 알 필요가 있다. 본 논문에서는 Support Vector Regression(SVR)을 이용한 기계적 상수와 부하 토크 추정 알고리즘을 제안한다. 실험 결과는 제안된 SVR 알고리즘이 서보 시스템의 기계적 상수와 부하 토크를 정확하게 추정하고 있음을 보여준다.

Keywords

References

  1. M. Iwasaki et al., 'Robust Speed Control of IM with Torque Feedforward Control,' IEEE Trans. Ind. Electronics, Vol.40, No.6, pp.553-560, 1993 https://doi.org/10.1109/41.245892
  2. 이동희, 최철, 김철우, '이중 속도 제어 구조에 의한 서보 제어기의 비선형 마찰 토크 보상,' 전력전자학회 논문지, 612-619, 2004
  3. Ichiro Awaya, Yoshiki Kato, Iwao Miyake, and Masami Ito, 'New Motion control with Inertia Identification Function using Disturbance Observer,' Proc. of IECON'92, Vol.1, pp.77-81, 1992
  4. 이교범, 송중호, 최익, 유지윤, '확장 루엔버거 관측기를 이용한 전동기의 저속 성능 향상', 전력전자학회 논문지, pp.231-239, 2004
  5. T. Fukuda and T. Shibata, 'Theory and Applications of Neural Networks for Industrial Control System', IEEE Trans. on Ind. Electronics, Vol.39, pp.472-489, Dec., 1992 https://doi.org/10.1109/41.170966
  6. C. Huang, T. Chen, and C. Huang, 'Robust Control of Induction Motor with a Neural-Network Load Torque Estimator and a Neural-Network Identification', IEEE Trans. on Ind. Electronics, Vol.46, pp.990-998, Oct. 1999 https://doi.org/10.1109/41.793348
  7. T. Chen and T. Sheu, 'Model Reference Neural Network Controller for Induction Motor Speed Control', IEEE Trans. on Energy Conversion, vol,17, pp.157-163, June. 2002 https://doi.org/10.1109/TEC.2002.1009462
  8. C. Cortes and V. Vapnik, 'Support vector networks', M. Learning, 20:273-297, 1995
  9. V. Vapnik, The Nature of Statistical Learning Theory., Springer, 1995
  10. V. Vapnik, Statistical Learning Theory. New York:Wiley, 1998
  11. B. Scholkopf et al,. 'Comparing support vector machines with Gaussian kernels to radial basis function classifier,' IEEE Trans. Signal Processing, VoI.45, pp.2758-2765, 1997 https://doi.org/10.1109/78.650102
  12. B. Boser, I. Guyon, and V. Vapnik, 'A training algorithm for optimal margin classifiers', presented at the 5th Annu. Workshop Comput. Learning Theory, 1992
  13. H. Druker et al., 'Support Vector regression machines', In Neural Information Processing Systems. Cambridge, A:MIT Press, Vol.9, 1997
  14. A. J. Smola and B. Scholkopf, 'A tutorial on support vector regression', Royal Holloway College, London, U.K,. Neuro COLT Tech. Rep. TR-1998-030, 1998.[1] ANSI C63.4-1992
  15. 이교범, 유지윤, '방사형 기저 함수망 외란관측기를 이용한 서보시스템의 저속응답 성능개선', 전력전자학회 논문지, pp.467-477, 2004
  16. B. Scholkopf and A. J. Smola, 'Learning with Kernels'., MIT Press, Cambridge, MA, 2002
  17. B. Armstron-Helouvry, P. Dupont and C. Canudas de Wit, 'A servey of models, analysis tools and compensation methods for the control of machines with friction,' Automatica, Vol. 30, pp.1083-1138, 1994 https://doi.org/10.1016/0005-1098(94)90209-7
  18. Vladimir Cherkassky and Yunqian Ma, 'Practical selection of SVM parameters and noise estimation for SVM regression,' Neural Networks, Vol.17, pp.113-126, 2004 https://doi.org/10.1016/S0893-6080(03)00169-2
  19. R. J. Vanderbei. 'LOQO: An interior point code for quadratic programming',. TRSOR-94-15, Statistics and Operations Research, Princeton Univ., NJ, 1994
  20. 설승기 '전기기기제어론', 브레인 코리아, 2002, 8