Self-tuning control based on generalized minimum variance criterion for auto-regressive models
✍ Scribed by Anna Patete; Katsuhisa Furuta; Masayoshi Tomizuka
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 433 KB
- Volume
- 44
- Category
- Article
- ISSN
- 0005-1098
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✦ Synopsis
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.