Identification of PWARX hybrid models with unknown and possibly different orders

Proceedings of the American Control Conference

Volumn 1, Issue , 2004, Pages 547-552

  Vidal, René ^a,b  

^a Johns Hopkins University   (United States)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; LINEAR SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SWITCHING;

HYBRID MODELS; HYBRID SYSTEMS; JUMP-MARKOV CHAINS; PIECEWISE AFFINE SYSTEMS;

HYBRID COMPUTERS;

EID: 8744311393     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/acc.2004.1383660     Document Type: Conference Paper

References (25)

1. A. Alessandri and P. Coletta. Design of Luenberger observers for a class of hybrid linear systems. In Hybrid Systems: Computation and Control, volume 2034 of LNCS, pages 7-18. Springer Verlag, 2001.
2. M. Babaali and M. Egerstedt. Observability of switched linear systems. In Hybrid Systems: Computation and Control, LNCS, 2004.
3. A. Balluchi, L. Benvenuti, M. Di Benedetto, and A. Sangiovanni- Vincentelli. Design of observers for hybrid systems. In Hybrid Systems: Computation and Control, volume 2289 of LNCS, pages 76-89. Springer Verlag, 2002.
4. A. Bemporad, G. Ferrari, and M. Morari. Observability and control-lability of piecewise affi ne and hybrid systems. IEEE Transactions on Automatic Control, 45(10): 1864-1876, October 2000.
5. A. Bemporad, A. Garulli, S. Paoletti, and A. Vicino. A greedy approach to identification of piecewise affine models. In Hybrid Systems: Computation and Control, LNCS, pages 97-112, 2003.
6. A. Bemporad, J. Roll, and L. Ljung. Identifi cation of hybrid systems via mixed-integer programming. In Proc. of IEEE Conf. on Decision and Control, pages 786-792, 2001.
7. M. Billio, A. Monfort, and C.P. Robert. Bayesian estimation of switching ARMA models. Journal of Econometrics, (93):229-255, 1999.
8. A. Blake, B. North, and M. Isard. Learning multi-class dynamics. Advances in Neural Information Processing Systems, 11:389-395, 1999. MIT Press.
9. P. Collins and J. Van Schuppen. Observability of piecewise-affi ne hybrid systems. In Hybrid Systems: Computation and Control, LNCS. Springer Verlag, 2004.
10. A. Doucet, A. Logothetis, and V. Krishnamurthy. Stochastic sampling algorithms for state estimation of jump Markov linear systems. IEEE Transactions on Automatic Control, 45(1):188-202, 2000.
11. J. Ezzine and A. H. Haddad. Controllability and observability of hybrid systems. International journal of Control, 49(6):2045-2055, 1989.
12. G. Ferrari-Trecate, D. Mignone, and M. Morari. Moving horizon estimation for hybrid systems. In Proceedings of the American Control Conference, pages 1684-1688, 2000.
13. G. Ferrari-Trecate, M. Muselli, D. Liberati, and M. Morari. A clustering technique for the identifi cation of piecewise affi ne systems. Automatica, 39(2):205-217, 2003.
14. Z. Ghahramani and G. E. Hinton. Variational learning for switching state-space models. Neural Computation, 12(4):963-996, 1998.
15. K. Kanatani and C. Matsunaga. Estimating the number of independent motions for multibody motion segmentation. In Asian Conference on Computer Vision, 2002.
16. K. Murphy. Switching Kalman filters. Technical report, U. C. Berkeley, 1998.
17. V. Pavlovic, J. M. Rehg, T. J. Cham, and K. P. Murphy. A dynamic Bayesian network approach to fi gure tracking using learned dynamic models. In Proc. of the Intl. Conf. on Comp. Vision, pages 94-101, 1999.
18. F. Szigeti. A differential algebraic condition for controllability and observability of time varying linear systems. In Proc. of IEEE Conference on Decision and Control, pages 3088-3090, 1992.
19. J. K. Tugnait. Detection and estimation for abruptly changing systems. Automatica, 18(5):607-615, 1982.
20. D. Del Vecchio and R. Murray. Observers for a class of hybrid systems on a lattice. In Hybrid Systems: Computation and Control. Springer Verlag, 2004.
21. R. Vidal, A. Chiuso, and S. Soatto. Observability and identifi ability of jump linear systems. In Proc. of IEEE Conference on Decision and Control, pages 3614-3619, 2002.
22. R. Vidai, A. Chiuso, S. Soatto, and S. Sastry. Observability of linear hybrid systems. In Hybrid Systems: Computation and Control, LNCS, pages 526-539. Springer Verlag, 2003.
23. R. Vidai, Y. Ma, and J. Piazzi. A new GPCA algorithm for clustering subspaces by fitting, differentiating and dividing polynomials. In IEEE Conference on Computer Vision and Pattern Recognition, 2004.
24. R. Vidai, Y. Ma, and S. Sastry. Generalized principal component analysis (GPCA). In IEEE Conference on Computer Vision and Pattern Recognition, volume I, pages 621-628, 2003.
25. R. Vidai, S. Soatto, Y. Ma, and S. Sastry. An algebraic geometric approach to the identifi cation of a class of linear hybrid systems. In Proc. of IEEE Conference on Decision and Control, 2003.