Self-tuning control based on generalized minimum variance criterion for auto-regressive models

被引：45

作者：

Patete, Anna ^{[1
]}

Furuta, Katsuhisa ^{[2
]}

Tomizuka, Masayoshi ^{[3
]}

机构：

[1] Tokyo Denki Univ, Dept Adv Multidisciplinary Engn, Saitama 3500394, Japan

[2] Tokyo Denki Univ, Dept Syst & Comp Engn, Saitama 3500394, Japan

[3] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA

来源：

AUTOMATICA | 2008年 / 44卷 / 08期

关键词：

AR systems; discrete-time systems; generalized minimum variance control; self-tuning control; sliding mode control;

D O I：

10.1016/j.automatica.2007.11.008

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Theoretical problems on self-tuning control include stability, performance and convergence of the recursive algorithm involved. In this paper, the problem of controlling minimum or non-minimum phase auto-regressive models with constant but unknown parameters is considered. The stability of an algorithm obtained by combining a recursive estimator for the controller parameters and a generalized minimum variance criterion is proved. The main result is the theorem which assures the overall stability for the closed-loop system in presence of white noise in the input-output relation, where the estimated parameters do not necessarily converge to the true values. The algorithm is proved by the Lyapunov theory. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：1970 / 1975

页数：6

共 16 条

[1]

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