Orthonormal basis selection for LPV system identification, the Fuzzy-Kolmogorov c-Max approach

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2006, Pages 2529-2534

  Toth R ^a   Heuberger, P S C ^a   Van Den Hof, P M J ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC STABILITY; ERROR ANALYSIS; FUZZY SYSTEMS; INVARIANCE; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

LINEAR PARAMETER VARYING (LPV); ORTHONORMAL BASIS SELECTION; SAMPLE SYSTEM POLES;

FUZZY CLUSTERING;

EID: 39749113195     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/cdc.2006.377063     Document Type: Conference Paper

References (18)

1. B. L. Stevens and F. L. Lewis, Aircraft Control and Simulation. John Wiley and Sons Inc., 2003.
2. ∞ control of induction motor," in Proceedings of the 43rd IEEE Conf. on Decision and Control, 2004, pp. 4435-440.
3. W. Rugh and J. Shamma, "Research on gain scheduling," Automatica, vol. 36, no. 10, pp. 1401-1425, 2000.
4. R. Murray-Smith and T. A. Johansen, Eds., Multiple Model Approaches to Modelling and Control. Taylor and Francis, 1997.
5. V. Verdult and M. Verhaegen, "Subspace identification of multivariable linear parameter-varying systems," Automatica, vol. 38, no. 5, pp. 805-814, 2002.
6. P. S. C. Heuberger, P. M. J. Van den Hof, and Bo Wahlberg, Eds., Modeling and Identification with Rational Orthonormal Basis Functions. Springer-Verlag, 2005.
7. T. Oliveira e Silva, "A n-width result for the generalized orthonormal basis function model," in Preprints of the 13th IFAC World Congress, vol. I, San Francisco, USA, 1996, pp. 375-380.
8. R. Tóth, P. S. C. Heuberger, and P. M. J. Van den Hof, "Optimal pole selection for LPV system identification wih OBFs, a clustering approach," in Preprints of the 14th IFAC Symp. on System Ident., Newcastle, Australia, 2006, pp. 356-361.
9. N. Vergeer, "Identification of LPV systems using orhonormal basis functions," Master's thesis, Delft Technical University, 2005.
10. P. S. C. Heuberger, P. M. J. Van den Hof, and O. H. Bosgra, "A generalized orthonormal basis for linear dynamical systems," IEEE Tr. Aut Contr., vol. 40, no. 3, pp. 451-465, 1995.
11. B. M. Ninness and F. Gustafsson, "A unifying construction of orthonormal bases for system identification," IEEE Tr. Aut Contr., vol. 42, no. 4, pp. 515-521, 1997.
12. A. Pinkus, n-Widths in Aprroximation Theory. New York: Springer-Verlag, 1985.
13. P. M. Mäkilä and J. R. Partington, "Robust aproximate modelling of stable linear systems," Int. J. of Control, vol. 58, no. 3, pp. 665-683, 1993.
14. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York and London: Plenum Press, 1981.
15. U. Kaymak and M. Setnes, "Fuzzy clustering with volume prototypes and adaptive cluster merging," IEEE Tr. Fuzzy Sys., vol. 10, no. 6, pp. 705-711, December 2002.
16. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. New Jersey: Englewood Cliffs, Prentice Hall, 1988.
17. S. P. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. Philadelphia: SIAM, 1994.
18. X. L. X. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE Tr. Patt. Anal. Machine Intell., vol. 13, no. 8, pp. 841-847, August 1991.