Artificial Intelligence Tools for Discrete Multiestimation Adaptive Control Scheme with Model Reduction Issues

A. Bilbao-Guillerna, M. de la Sen, S. Alonso-Quesada, and A. Ibeas (Spain)


Discrete adaptive control, multiestimation schemes, supervisory control, switching techniques, model reduction


A multiestimation-based adaptive control scheme is presented for a plant with known poles and unknown zeros. The plant is decomposed in several first order filters with unknown scalar numerators. A set of different reduced plant models is obtained by containing a distinct number of the first order filters in order to consider different approximated models. The scheme chooses in real time the estimator/controller 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. A higher supervision algorithm is used in order to find the best updated online value for a weighting factor.

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