ON A PARAMETER ADAPTIVE SELF-ORGANIZING SYSTEM WITH THE MINIMUM VARIANCE CONTROL LAW IN THE PRESENCE OF LARGE OUTLIERS IN OBSERVATIONS
The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loop discrete-time linear time-invariant (LTI) system when the parameters of a dynamic system as well as that of a controller are not known and ought to be estimated. The parametric identification of the open-loop LTI system and the determination of the coefficients of the MV controller are performed in each current operation by processing observations in the case of additive noise on 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 is applied here in the calculation of estimates of the parameters of a LTI system with one time-varying coefficient in the numerator of the system transfer function. 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 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.
the minimum variance control law outliers observations closed-loop parametric identification adaptive systems self-tuning controller