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- W2616285215 abstract "This paper is concerned with the links between predictive adaptive control algorithms and Statistic Learning theory. In particular, the MUSMAR controller is considered. Through the review of industrial scale plant control examples (arc welding, superheated steam and a solar collector field) it is shown that this algorithm applies to widelly different situations, outperforming standard industrial controllers. Using Saridi's Maximum Entropy formulation of Optimal and Adaptive Control, relationships between performance and certain aspects of controller structure, observed both in simulations and experiments, are shown to hold. The entropy approach also sheds light on the benefic effect of using more parameters than one would strictly need (referred as “redundant modeling”) would the plant order be perfectly known." @default.
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- W2616285215 date "2001-08-01" @default.
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- W2616285215 title "Overparametrization in Predictive Adaptive Control: Experimental Results and an Entropy Interpretation" @default.
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- W2616285215 doi "https://doi.org/10.1016/s1474-6670(17)41615-6" @default.
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