Gray Box Identification Using Difference of Convex Programming. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 9462
• Page End: 9467
• Publication Date: 2017-07
• Subjects: Structured state-space model -- convex-concave procedure; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1469
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Gray-box identification is prevalent in modeling physical and networked systems. However, due to the non-convex nature of the gray-box identification problem, good initial parameter estimates are crucial for a successful application. In this paper, a new identification method is proposed by exploiting the low-rank and structured Hankel matrix of impulse response. This identification problem is recasted into a difference-of-convex programming problem, which is then solved by the sequential convex programming approach with the associated initialization obtained by nuclear-norm optimization. The presented method aims to achieve the maximum impulse-response fitting while not requiring additional (non-convex) conditions to secure non-singularity of the similarity transformation relating the given state-space matrices to the gray-box parameterized ones. This overcomes a persistent shortcoming in a number of recent contributions on this topic, and the new method can be applied for the structured state-space realization even if the involved system parameters are unidentifiable. The method can be used both for directly estimating the gray-box parameters and for providing initial parameter estimates for further iterative search in a conventional gray-box identification setup.