DocumentCode :
706605
Title :
Adaptive control of multivariable linear stochastic systems. A strong approximation approach
Author :
Gerencser, L. ; Vago, Zs
Author_Institution :
Comput. & Autom. Inst., Budapest, Hungary
fYear :
1999
fDate :
Aug. 31 1999-Sept. 3 1999
Firstpage :
1643
Lastpage :
1647
Abstract :
This paper is an extension and further development of the results of [5]. It was shown there that open loop identifiability of a linear stochastic control system under persistently exiting input implies closed loop identifiability using an appropriate dither. It was assumed there that the covariance of the system noise was known, and the covariance matrix of the dither was fixed apriori. In this paper we estimate the system parameters together with the covariance of the system noise, and we let the covariance of the dither depend on the system parameters. A recursive estimation procedure will be presented and the estimator will be characterized in the form of a strong approximation theorem. The covariance-matrix of the estimation error will be expressed in terms of the covariance matrix of the system´s noise and the dither, respectively.
Keywords :
adaptive control; closed loop systems; covariance matrices; identification; linear systems; multivariable control systems; open loop systems; recursive estimation; stochastic systems; adaptive control; closed loop identifiability; covariance matrix; dither; estimation error; multivariable linear stochastic systems; open loop identifiability; recursive estimation procedure; strong approximation approach; Adaptive control; Approximation methods; Control systems; Covariance matrices; Noise; Stochastic systems; Transfer functions; Linear stochastic systems; adaptive control; closed-loop identification; recursive estimation; strong approximation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
Type :
conf
Filename :
7099549
Link To Document :
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