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Published December 2011 | public
Book Section - Chapter

Identification and model (in)validation of switched ARX systems: A moment-based approach

Abstract

Identification of switched systems has received considerable attention during the past few years. This chapter addresses the problem of identification and (in)validation of switched autoregressive models with external inputs from experimental data. In the first part of the chapter identification of switched linear systems from noisy measurements is considered. Given a finite number of measurements of input/output and a bound on the measurement noise, the objective is to identify a switching sequence and a set of linear submodels that are compatible with the a priori information, while minimizing the number of submodels. A deterministic approach based on convex optimization is proposed. Namely, by recasting the identification problem as polynomial optimization, we develop as efficient convex relaxations for rank minimization are available in the literature. deterministic algorithms, in which the inherent sparse structure is exploited. A finite dimensional semi-definite problem is then given which is equivalent to the identification problem. Moreover, to address computational complexity issues, an equivalent rank minimization problem subject to deterministic linear matrix inequality constraints is provided, as efficient convex relaxations for rank minimization are available in the literature.

Additional Information

© 2012 World Scientific Publishing Co. Publication date: December 2011.

Additional details

Created:
August 19, 2023
Modified:
January 13, 2024