Abstract
For most of linear time-varying (LTV) systems, it is difficult to design time-varying controllers in analytic way. Accordingly, by approximating LTV systems as uncertain linear time-invariant, control design approaches such as robust control have been applied to the resulting uncertain LTI systems. In particular, a robust control method such as quantitative feedback theory (QFT) has an advantage of guaranteeing the frozen-time stability and the performance specification against plant parameter uncertainties. However, if these methods are applied to the approximated linear time-invariant (LTI) plants with large uncertainty, the resulting control law becomes complicated and also may not become ineffective with faster dynamic behavior. In this paper, as a method to enhance the fast dynamic performance of LTV systems with bounded time-varying parameters, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Borghesani, C, Yaniv, O., and Chait, Y., 1994,Quantitative Feedback Theory Toolbox User’s Guide, The Mathworks, Inc..
Chait, Y., 1991, “Robust Internal Stability in Multi Input-Output Quantitative Feedback Theory,”Proceedings of the 30th Conference on Decision and Control, pp. 2970–2971.
Chen, W. H., Ballance, D. J.. Feng, W., and Li, Y., 1999, “Genetic Algorithm Enabled Computer-Automated Design of QFT Control System,”Proceedings of the IEEE International Symposium on Computer Aided Control System Design, pp. 492–497.
Choi, J. W., Lee, H. C, and Yoo, W. S., 1999, “Eigenstrwcture Assignment for Lirear Time-Voring Systems via Differential Sylvester Equortion,”KSME International Journal, Vol. 13, pp. 609–619.
Choi, J. W., Lee. H. C, and Zhu. J. J., 2001, “Decoupling and Tracking Control Vsing Eigenstrwcture Assignment for Lirear Time-Voring Systems,”KSME International Journal of Control, Vol. 74, pp. 453–464.
Demuth, H., and Beale. M., 1997,Neural Network Toolbox User’s Guide, The Mathworks, Inc..
Gu, J., Shen, J., and Chen, L., 1999, “The QFT Control Design via Genetic Algorithms and Application,”Proceedings of the 14th Triennial World Congress of IFAC, pp. 103–108.
Halikias, G. D., and Bryant, G. F., 1995, “Optimal Loop-Shaping for Systems with Large Parameter Uncertainty via Linear Programming,”International Journal of Control, Vol. 62, pp. 557–568.
Haykin, S., 1994,Neural Networks : A Comprehensive Foundation, Macmillan College Publishing.
Horowitz, I. M., 1963,Synthesis of feedback systems, Academic Press.
Horowitz, I. M., 1991, “Survey of Quantitative Feedback Theory,”International Journal of Control, Vol. 53. pp. 255–291.
Horowitz, I. M.. 1992,Quantitative Feedback Theory (QFT), QFT Publications.
Houpis, C. H., Sating. R. R., Rasmussen, S., and Sheldon, S., 1994, “Quantitative Feedback Theory Technique and Applications,”International Journal of Control. Vol. 59, pp. 39–70.
Jang, J. -S. R., Sun, C. -T., and Mizutani, E., 1997,Neuro-Fuzzy and Soft Computing, Prentice Hall.
Lin, C. F., 1994, Advanced Control Systems Design, Prentice Hall.
Shin. B. K., Hahn, O. J., Yi, K. S., and Lee, K. I., 2000, “A Supervisor-Based Neural-Adaptive Shift Controller for Automatic Transmis sions Considering Throttle Opening and Driving Load,”KSME International Journal. Vol. 14, pp. 418–425.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Choi, J.W., Yoo, WS., Lee, S. et al. QFT Parameter-Scheduling control design for linear Time-Varying systems based on RBF networks. KSME International Journal 17, 484–491 (2003). https://doi.org/10.1007/BF02984449
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF02984449