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Mixture‐based adaptive probabilistic control
Author(s) -
Kárný Miroslav,
Böhm Josef,
Guy Tatiana V.,
Nedoma Petr
Publication year - 2003
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.742
Subject(s) - probabilistic logic , computer science , forgetting , class (philosophy) , control theory (sociology) , basis (linear algebra) , equivalence (formal languages) , bayes' theorem , bayesian probability , control (management) , artificial intelligence , mathematics , philosophy , linguistics , geometry , discrete mathematics
Abstract Quasi‐Bayes algorithm, combined with stabilized forgetting, provides a tool for efficient recursive estimation of dynamic probabilistic mixture models. They can be interpreted either as models of closed‐loop with switching modes and controllers or as a universal approximation of a wide class of non‐linear control loops. Fully probabilistic control design extended to mixture models makes basis of a powerful class of adaptive controllers based on the receding‐horizon certainty equivalence strategy. Paper summarizes the basic elements mentioned above, classifies possible types of control problems and provides solution of the key one referred to as ‘simultaneous’ design. Results are illustrated on mixtures with components formed by normal auto‐regression models with external variable (ARX). Copyright © 2003 John Wiley & Sons, Ltd.