DocumentCode :
581543
Title :
Data-driven Kalman filter for linear continuous-time parametric uncertain systems with non-uniformly sampled data
Author :
He, Pingsheng ; Ma, Hongbin ; Yang, Chenguang ; Fu, Mengyin
Author_Institution :
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
fYear :
2012
fDate :
25-27 July 2012
Firstpage :
219
Lastpage :
224
Abstract :
This paper develops one Kalman filtering technique for parametric uncertain continuous-time linear systems with non-uniformly sampled data. The considered problem is challenging in sense that normal Kalman filter is not applicable due to the unknown parameter in the system dynamics and the unknown parameter cannot be identified directly due to the lack of good state estimates. Based on a new discretization scheme addressing the known parameter and the non-uniformly sampled data, an algorithm based on Kalman filtering theory is proposed to estimate the uncertain parameter and states simultaneously, whose main idea is to merge the parameter estimation and state filtering in the same loop, that is to say, with the help of discrete-time model obtained, the estimated states are used to estimate the parameter and the estimated parameter is fed into the state estimation. One typical numerical example is given to illustrate the feasibility and effectiveness of the proposed algorithm.
Keywords :
Kalman filters; continuous time systems; discrete time systems; linear systems; parameter estimation; sampled data systems; state estimation; uncertain systems; data-driven Kalman filter; discrete-time model; linear continuous-time parametric uncertain systems; nonuniformly sampled data; parameter estimation; state estimation; state filtering; Equations; Kalman filters; Mathematical model; Parameter estimation; State estimation; Uncertainty; Discretization Scheme; Kalman Filter; Non-uniformly Sampled Data; Parametric Uncertainty; Simutaneously Estimating Parameter and States;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
ISSN :
1934-1768
Print_ISBN :
978-1-4673-2581-3
Type :
conf
Filename :
6389931
Link To Document :
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