Identification of structured state-space models. (April 2018)

• Record Type: Journal Article
• Authors: Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel
• Is Part Of: Automatica. Volume 90(2018)
• Page Start: 54
• Page End: 61
• Publication Date: 2018-04
• Subjects: Structured state-space model -- Difference-of-convex problem -- Prediction-error method; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2017.12.023
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Identification of structured state-space (gray-box) model is popular for modeling physical and network systems. Due to the non-convex nature of the gray-box identification problem, good initial parameter estimates are crucial for successful applications. In this paper, the non-convex gray-box identification problem is reformulated as a structured low-rank matrix factorization problem by exploiting the rank and structured properties of a block Hankel matrix constructed by the system impulse response. To address the low-rank optimization problem, it is first transformed into a difference-of-convex (DC) formulation and then solved using the sequentially convex relaxation method. Compared with the classical gray-box identification methods like the prediction-error method (PEM), the new approach turns out to be more robust against converging to non-global minima, as supported by a simulation study. The developed identification can either be directly used for gray-box identification or provide an initial parameter estimate for the PEM.