An interval-valued recursive estimation framework for linearly parameterized systems

This paper proposes a recursive interval-valued estimation framework for identifying the parameters of linearly parameterized systems which may be slowly time-varying. It is assumed that the model error (which may consist in measurement noise or model mismatch or both) is unknown but lies at each ti...

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Bibliographic Details
Published in:Systems & control letters 2022-10, Vol.168, p.105345, Article 105345
Main Authors: Bako, Laurent, Ndiaye, Seydi, Blanco, Eric
Format: Article
Language:English
Subjects:
Adaptive estimation
Automatic
Engineering Sciences
Interval-valued estimator
Recursive system identification
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Summary:This paper proposes a recursive interval-valued estimation framework for identifying the parameters of linearly parameterized systems which may be slowly time-varying. It is assumed that the model error (which may consist in measurement noise or model mismatch or both) is unknown but lies at each time instant in a known interval. In this context, the proposed method relies on bounding the error generated by a given reference point-valued recursive estimator, for example, the well-known recursive least squares algorithm. We discuss the trade-off between computational complexity and tightness of the estimated parametric interval.
ISSN:0167-6911
1872-7956
DOI:10.1016/j.sysconle.2022.105345