Generalized minimum variance adaptive control and parameter convergence for stochastic systems
作者:
D. DOWN,
R. H. KWONG,
期刊:
International Journal of Control
(Taylor Available online 1996)
卷期:
Volume 63,
issue 1
页码: 147-160
ISSN:0020-7179
年代: 1996
DOI:10.1080/00207179608921836
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Two stochastic adaptive control schemes, the stochastic gradient and modified least squares, are studied. We consider these for scalar ARMAX systems with general input delays. First, when the algorithms are based on generalized minimum variance control with reference tracking, sufficient conditions for stability and optimality are found. This is done using martingale convergence analysis. Secondly, we examine parameter convergence for each of the algorithms, and establish conditions for convergence of the parameter estimates to a random multiple of the true parameters.
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