Identification of structured LTI MIMO state-space models

Identification of structured LTI MIMO state-space models

Yu, C. (TU Delft Applied Mechanics)
Verhaegen, M.H.G. (TU Delft Team Michel Verhaegen)
Kovalsky, S (External organisation)
Basri, R (External organisation)

Valcher, ME (editor)
Ohta, Y (editor)
Sampei, M (editor)

2015

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification
method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition
for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial
condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.

http://resolver.tudelft.nl/uuid:e24ee66b-377b-480b-9d76-b55a4dc5bcaa

https://doi.org/10.1109/CDC.2015.7402630

IEEE, Piscataway, NJ, USA

978-1-4799-7884-7

Proceedings of the 2015 IEEE 54th Annual Conference on Decision and Control

54th IEEE Conference on Decision and Control, CDC 2015, 2015-12-15 → 2015-12-18, Osaka, Japan

Accepted Author Manuscript

Institutional Repository

conference paper

© 2015 C. Yu, M.H.G. Verhaegen, S Kovalsky, R Basri