DocumentCode :
389628
Title :
Multi-model system parameter estimation
Author :
Markovsky, Ivan ; Van Huffel, Sabine ; De Moor, Bart
Author_Institution :
Dept. of Electr. Eng., Katholieke Univ., Leuven, Belgium
Volume :
5
fYear :
2002
fDate :
6-9 Oct. 2002
Abstract :
We pose a multi-model system parameter estimation problem. A multi-model system is a linearly parameterized system H(z, p)=Σi=1nppiHi(z). The parameter estimation problem is: given the set of systems {Hi(z)}i=1np, describing the multi-model system, find a causal system that assumes as an input the input/output signals of the multi-model system and produces as an output the parameter estimate. We propose an easy to implement suboptimal solution. The algorithm that realizes it selects the best linear combination of the estimates produced by the Kalman filters designed for the models {Hi(z)}i=1np. "Best" is defined in the sense of minimization of the output error of estimation covariance. The algorithm is appropriate for fault detection and can be viewed as an observer for the discrete state of a hybrid system.
Keywords :
Kalman filters; covariance analysis; minimisation; parameter estimation; Kalman filters; causal system; discrete state; estimation covariance; fault detection; hybrid system; input/output signals; linearly parameterized system; multi-model system parameter estimation problem; output error minimization; recursive parameter estimation; suboptimal solution; Algorithm design and analysis; Ear; Estimation error; Fault detection; Kalman filters; Noise measurement; Observers; Parameter estimation; Smoothing methods; Transfer functions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-7437-1
Type :
conf
DOI :
10.1109/ICSMC.2002.1176410
Filename :
1176410
Link To Document :
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