Identification of affinely parameterized state–space models with unknown inputs

Identification of affinely parameterized state–space models with unknown inputs

Yu, Chengpu (Beijing Institute of Technology)
Chen, Jie (Beijing Institute of Technology; Tongji University)
Li, Shukai (Beijing Jiaotong University)
Verhaegen, M.H.G. (TU Delft Team Raf Van de Plas)

2020

The identification of affinely parameterized state–space system models is quite popular to model practical physical systems or networked systems, and the traditional identification methods require the measurements of both the input and output data. However, in the presence of partial unknown input, the corresponding system identification problem turns out to be challenging and sometimes unidentifiable. This paper provides the identifiability conditions in terms of the structural properties of the state–space model and presents an identification method which successively estimates the system states and the affinely parameterized system matrices. The estimation of the system matrices boils down to solving a bilinear optimization problem, which is reformulated as a difference-of-convex (DC) optimization problem and handled by the sequential convex programming method. The effectiveness of the proposed identification method is demonstrated numerically by comparing with the Gauss–Newton method and the sequential quadratic programming method.

Affinely parameterized state–space model
Subspace identification
Unknown system input

http://resolver.tudelft.nl/uuid:a422afe7-9061-4ad5-b958-4c8f017ce876

https://doi.org/10.1016/j.automatica.2020.109271

2022-09-25

0005-1098

Automatica, 122

Accepted Author Manuscript

Institutional Repository

journal article

© 2020 Chengpu Yu, Jie Chen, Shukai Li, M.H.G. Verhaegen