선형 시변시스템을 위한 신경망 기반의 새로운 이득계획 QFT 기법

A New Gain Scheduled QFT Method Based on Neural Networks for Linear Time-Varying System

  • 발행 : 2000.09.01

초록

The properties of linear time-varying(LTV) systems vary because of the time-varying property of plant parameters. The generalized controller design method for linear time-varying systems does not exit because the analytic soultion of dynamic equation has not been found yet. Hence, to design a controller for LTV systems, the robust control methods for uncertain LTI systems which are the approximation of LTV systems have been generally ised omstead. However, these methods are not sufficient to reflect the fast dynamics of the original time-varying systems such as missiles and supersonic aircraft. In general, both the performance and the robustness of the control system which is designed with these are not satisfactory. In addition, since a better model will give the more robustness to the controlled system, a gain scheduling technique based on LTI controller design methods has been uesd to solve time problem. Therefore, we propose a new gain scheduled QFT method for LTV systems based on neural networks in this paper. The gain scheduled QFT involves gain dcheduling procedured which are the first trial for QFT and are well suited consideration of the properties of the existing QFT method. The proposed method is illustrated by a numerical example.

참고문헌

  1. D. J. Leith and W. E Leithead, 'Appropriate realization of MIMO gain-scheduled controllers,' International Journal of Control, vol. 70, no 1, pp. 13-50, 1998 https://doi.org/10.1080/002071798222442
  2. D. J. Leith and W E. Leitheacl, 'Gain-scheduled and nonlinear systems: dynamic analysis by velocity-based linearization families.'' International Journal of Control, vol. 70, no. 2, pp. 289-317, 1998 https://doi.org/10.1080/002071798222415
  3. I. Horowitz, Synthesis of feedback systems, Academic Press, 1963
  4. I. Horowitz, 'Survey of Quantitative feedback theory,' International Journal of Control, vol. 53, no 2, pp. 255-291. 1991 https://doi.org/10.1080/00207179108953619
  5. C. H. Houpis, R. R. Sating, S. Rasmussen, and S. Sheldon. 'Quantitative feedback theory technique and applications,' International Journal of Control. vol. 59, no. 1, pp. 39-70, 1994 https://doi.org/10.1080/00207179408923069
  6. C. F. Lin, Advanced control systems design, Pnntice Hall, 1994
  7. C. Borghesani, O. Yaniv, and Y Chait, Quantitative feedback theory toolbox user's guide. The Mathworks, inc., 1994
  8. I. Horowitz, Quantitative feedback theory(QFT), QFT Publications, 1992
  9. G D. Hallkias and G. F. Bryant, 'Optimal loop-shaping for systems with large parameter uncertainty via linear programming,' International Journal of Control, vol. 62, no. 1. pp. 557-568, 1995 https://doi.org/10.1080/00207179508921556
  10. H. Demuth and M Beale, Neural network toolbox user's guide, The Mathworks, inc.. 1997
  11. J. Gu, J. Shen, and L Chen, 'The QFT control design via genetic algorithms and application,' Proceedings of the 14th World Congress of IFAC, pp 103-108, 1999
  12. W H. Chen, D. J. Ballance, W. Feng, and Y. Li, 'Genetic algorithm enabled computer-automateddesign of QFT control systems,' Proc. of the 1999 IEEE International Symposium on Computer Aided Control System Design, pp. 492-497, 1999