An interval-valued recursive estimation framework for linearly parameterized systems

TL;DR

This paper introduces a recursive interval-valued estimation method for identifying parameters in linearly parameterized systems, accounting for unknown but bounded errors, including noise and model mismatch, with a focus on balancing computational complexity and estimation accuracy.

Contribution

It presents a novel recursive interval estimation framework that extends traditional methods to handle unknown bounded errors in time-varying systems.

Findings

Effective bounding of model errors in parameter estimation

Trade-off analysis between computational complexity and estimate tightness

Applicability to slowly time-varying systems

Abstract

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.