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Further results on stable weighted multiple model adaptive control: Discrete‐time stochastic plant
Author(s) -
Zhang Weicun
Publication year - 2015
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.2555
Subject(s) - weighting , control theory (sociology) , convergence (economics) , stability (learning theory) , lti system theory , adaptive control , computer science , rate of convergence , mathematical optimization , discrete time and continuous time , mathematics , linear system , control (management) , artificial intelligence , statistics , machine learning , medicine , mathematical analysis , computer network , channel (broadcasting) , economics , radiology , economic growth
Summary A new weighting algorithm is proposed to relax the convergence conditions and to improve the convergence rate for weighted multiple model adaptive control systems. The stability and convergence of the corresponding weighted multiple model adaptive control systems of two types of stochastic plants, one is linear time‐invariant system (LTI) with unknown parameters, the other is linear time‐varying system with jumping parameters, are proved. Finally, some simulation results are presented to verify the effectiveness of the proposed weighting algorithm and the performance of the closed‐loop control system. Copyright © 2015 John Wiley & Sons, Ltd.