Discrete multiestimation adaptive control with model reduction

A discrete multiestimation-based adaptive control scheme is presented for a plant with known poles and unknown zeros. The plant is decomposed into several elementary filters with unknown numerators for analysis and design purposes. A set of reduced models is obtained by using a different combination of several order filters. The scheme chooses in real time the estimator/controller associated to a particular reduced model possessing the best performance according to an identification performance index by implementing a switching rule between estimators. The switching rule is subject to a minimum residence time at each identifier/adaptive controller parameterization for closed-loop stabilization purposes. The main novelty in this paper related with previous results is the use in an integrated way of multiestimation and model reduction which may be linked with knowledge about reference input spectrum if known.