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Stable weighted multiple model adaptive control: discrete‐time stochastic plant
Author(s) -
Zhang Weicun
Publication year - 2013
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.2328
Subject(s) - control theory (sociology) , discrete time and continuous time , controller (irrigation) , convergence (economics) , stability (learning theory) , adaptive control , basis (linear algebra) , computer science , mathematics , mathematical optimization , scheme (mathematics) , reference model , control (management) , artificial intelligence , statistics , mathematical analysis , geometry , software engineering , machine learning , agronomy , economics , biology , economic growth
SUMMARY A stable weighted multiple model adaptive control system for uncertain linear, discrete‐time stochastic plant is presented in the paper. First, a new scheme for calculating controller weights is proposed with assured convergence, that is, the controller weight corresponding to the model closest to the true plant converges to 1, and others converge to 0; second, on the basis of virtual equivalent system concept and methodology, the stability of the overall closed‐loop control system is proved under a unified framework which is independent of specific ‘local’ control strategy. Copyright © 2012 John Wiley & Sons, Ltd.