Abstract
This paper proposes a novel approach to the formulation of multirate adaptive estimators which are used in system identification to accomodate two or more rates of sampling. The formulation is based on the multirate form of the classical Kalman filter to do model based filtering coupled with a maximum likelihood based estimation for model adaptation. The strategy is experimentally evaluated by application to a fed-batch bioreactor.
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