한국정밀공학회지 (Journal of the Korean Society for Precision Engineering)

한국정밀공학회 (Korean Society for Precision Engineering)
권호 표지

동적 마찰이 있는 다변수 시스템에서의 PID 학습 제어

PID Learning Controller for Multivariable System with Dynamic Friction

  • 정병묵 (영남대학교 공과대학 기계공학부)
  • 발행 : 2007.12.01
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초록

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

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참고문헌

  1. Ziegler, J. G. and Nichols, N. B., 'Optimum setting for automatic controllers,' Trans. ASME, Vol. 64, pp. 759-768, 1942
  2. Astrom, K. J. and Hagglund, T., 'Automatic tuning of simple regulators with specifications on phase and amplitude margins,' Automatica, Vol. 20, No. 5, pp. 645-651, 1984 https://doi.org/10.1016/0005-1098(84)90014-1
  3. Astrom, K. J., Hang, C. C., Person, P. and Ho, W. K., 'Towards Intelligent PID control,' Automatica, Vol. 28, No. 1, pp. 1-9, 1992 https://doi.org/10.1016/0005-1098(92)90002-W
  4. Zhao, Z. Y., Tomizuka, M. and Isaka, S., 'Fuzzy gain scheduling of PID controllers,' IEE Trans. Systems, Man, & Cybernetics, Vol. 23, No. 5, pp. 1392-1398, 1993 https://doi.org/10.1109/21.260670
  5. Warwick, K. and Kang, Y. H, 'Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares,' Proc, of the Institute of Mechanical Engineers, part I: Journal of Systems and Control Engineering, Vol. 212 No.6, pp. 437-448, 1998
  6. Narendra, K. S. and Parthasarathy, K., 'Identification and control of dynamical systems using neural networks,' IEEE Trans. Neural Networks, Vol. 1, No. 1, pp. 4-26, 1990 https://doi.org/10.1109/72.80202
  7. Sanner, R. M. and Slotine, J. E., 'Gaussian networks for direct adaptive control,' IEEE Trans. Neural Networks, Vol. 3, No. 6, pp. 837-863, 1992 https://doi.org/10.1109/72.165588
  8. Chen, F. C., 'Back-propagation neural networks for nonlinear self-tuning adaptive control,' IEEE Control System Magazine, Vol. 10, No. 3, pp. 44-48, 1990 https://doi.org/10.1109/37.55123
  9. Chen, F. C. and Khalil, H. K., 'Adaptive control of nonlinear systems using neural networks,' Int. J. of Control, Vol. 55, No. 6, pp. 1299-1317, 1992 https://doi.org/10.1080/00207179208934286
  10. Lim, Y. K. and Chung, B. M., 'A learning method of PID controller by Jacobian in multi variable system,' J. of KSPE, Vol. 20, No. 2, pp, 112-119, 2003
  11. Lim, Y. K. and Chung, B. M., 'A learning method of LQR controller using Jacobian,' J. of KSPE, Vol. 22, No.8, pp. 34-41, 2005
  12. Chung, B. M., 'Control of nonlinear multivariable Systems using direct fuzzy learning method,' Int. J. of Intelligent & Fuzzy Systems, Vol. 5, No. 3, pp. 297-310, 1997
  13. Zhang, F., 'Matrix Theory: Basic Results and Techniques,' Springer, pp. 159-207, 1999
  14. Ahn, H. S., Chen, Y. Q. and Dou, H., 'State-Periodic Adaptive Compensation of Cogging and Coulomb Friction in Permanent-Magnet Linear Motors,' IEEE Trans. Magnetics, Vol. 41, No. 1, pp. 90-98, 2005 https://doi.org/10.1109/TMAG.2004.840182