Gray box identification using difference of convex programming

Conference Paper (2017)
Author(s)

C. Yu (TU Delft - Team Raf Van de Plas)

Lennart Ljung (Linköping University)

M.H.G. Verhaegen (TU Delft - Team Raf Van de Plas)

Research Group
Team Raf Van de Plas
Copyright
© 2017 C. Yu, Lennart Ljung, M.H.G. Verhaegen
DOI related publication
https://doi.org/10.1016/j.ifacol.2017.08.1469
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 C. Yu, Lennart Ljung, M.H.G. Verhaegen
Research Group
Team Raf Van de Plas
Volume number
50-1
Pages (from-to)
9462-9467
Reuse Rights

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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.

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