Kalman filters for non-uniformly sampled multirate systems

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 16, Issue , 2005, Pages 99-104

  Li, Weihua ^a   Shah, Sirish L ^a   Xiao, Deyun ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

Fault detection; Filtering; Kalman filters; Non uniformly sampled multirate systems; One step prediction

Indexed keywords

FAULT DETECTION; FILTRATION; STATE ESTIMATION;

KALMAN FILTER ALGORITHMS; MULTI-RATE SYSTEMS; ONE-STEP PREDICTIONS; SAMPLED DATA; STABILITY AND CONVERGENCE;

KALMAN FILTERS;

EID: 79960718915     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20050703-6-cz-1902.01592     Document Type: Conference Paper

References (21)

1. Aström, K. (1970). Introduction to Stochastic Control Theory. Academic Press, New York.
2. Aström, K. and B. Wittenmark (1997). Computer-Controlled Systems. Prentice Hall, New Jersey.
3. Basseville, M. and I. Nikiforov (1993). Detection of Abrupt Changes-Theory and Applications. Prentice Hall, New Jersey.
4. Chen, G., G. Chen and S. Hsu (1995). Linear Stochastic Control Systems. CRC Press Inc., New York.
5. Chou., C. and M. Verhaegen (1997). Subspace algorithms for the identification of multivariable dynamic errors-in-variables models. Automatica 33, 1857-1869.
6. Chow., E. and A. Willsky (1984). Analytical redundancy and the design of robust failure detection systems. IEEE Trans. Auto. Cont. 29, 603-614.
7. Fadali, M. and H. Emara-Shabaik. (2002). Timely robust detection for multirate linear systems. International J. of Control, 75, 305-313.
8. Frank, P. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey and some new results. Automatica 26, 459-474.
9. Ge, W. and C. Fang (1988). Detection of faulty components via robust observation, International J. of Control, 47, 581-599.
10. Gudi, R., S.L. Shah and M. Gary (1994). Adaptive multirate state and arameter estimation strategies with application to a bioreactor. AIChE J., 41, 2451-2464.
11. Haykin, S. (1996). Adaptive Filter Theory (3rd Edition). Prentice Hall, New Jersey.
12. Kalman, R. (1960). A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng., Ser. 82D, 35-45.
13. Keller, J. (1999). Fault isolation filter design for linear stochastic systems. Automatica, 35, 1701-1706.
14. Li, W. and S. L. Shah (2004). Fault detection and isolation in non-uniformly sampled systems. In: Proc. of IFAC DYCOPS 7. Cambridge, MA, USA.
15. Li, W., Z. Han and S. L. Shah (2005). Subspace identification for FDI in systems with non-uniformly sampled data. Automatica, Accepted for publication in Feburary.
16. Mehra, R. and I. Peschon. (1971). an Innovations Approach to Fault Detection and Diagnosis in Dynamic Systems. Automatica, 7, 637-640.
17. Sheng, J., T. Chen and S. L. Shah (2002). Generalized predictive control for non-uniformly sampled systems. J. of Process Control, 12, 875-885.
18. Sorenson, H. (1970). Least-squares estimation: from Gauss to Kalman. IEEE Spectrum, 7, 63-68.
19. Sorenson, H. (1985). Kalman filtering: theory and application (Ed). IEEE Press, New York.
20. Souza, C., M. Gevers and G. Goodwin (1986). Riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrice. IEEE Trans. Auto. Cont., 30, 831-838.
21. Zhang, P., S. Ding, G. Wang and D. Zhou (2002). Fault detection for multirate sample-data systems with time delay. International J. of Control, 75, 1457- 1471.