INFORMATICA International Journal Main Page Editorial Board Abstracting/Indexing Instructions to Authors Subscription Information Contents Author Index Papers in Production		INFORMATICA, 2010, Vol. 21, No. 1, 79-94 © Institute of Mathematics and Informatics, ISSN 0868-4952 On a Time-Varying Parameter Adaptive Self-Organizing System in the Presence of Large Outliers in Observations Rimantas PUPEIKIS Institute of Mathematics and Informatics Akademijos 4, LT-08663 Vilnius, Lithuania E-mail: pupeikis@ktl.mii.lt Abstract In the previous papers (Pupeikis, 2000; Genov et al., 2006; Atanasov and Pupeikis, 2009), a direct approach for estimating the parameters of a discrete-time linear time-invariant (LTI) dynamic system, acting in a closed-loop in the case of additive noise with contaminating outliers uniformly spread in it, is presented. It is assumed there that the parameters of the LQG (Linear Quadratic Gaussian) controller are unknown, as well as known beforehand, too. The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loop discrete-time linear dynamic system when slowly or suddenly time-varying coefficients of the transfer function of such a system as well as that of a minimum variance (MV) controller are not known and ought to be estimated. The recursive parametric identification of an open-loop system and determination of the coefficients of the MV controller are performed in each current operation by processing observations in the case of additive noise at the output with contaminating outliers uniformly spread in it. The robust recursive technique, based on the S-algorithm, with a version of Shweppe's GM-estimator and with discounting previous data, used in the estimation, by introducing a constant as well as time-varying forgetting factors in the abovementioned estimator, is applied here in the calculation of estimates of the parameters of a dynamic system. Then, the recursive parameter estimates are used in each current iteration to determine unknown parameters of the MV controller. Afterwards, the current value of the MV control signal is found in each operation, and it is used to generate the output of the system, too. The results of numerical simulation by computer are presented and discussed. Keywords: adaptive systems, closed-loop, self-tuning controller, the minimum variance control law, parametric identification, observations, outliers To preview Lithuanian abstract see full article text To preview full article text in PDF format click here You could obtain free Acrobat Reader from Adobe
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