TY - GEN
T1 - Local subspace identification of distributed homogeneous systems with general interconnection patterns
AU - Yu, Chengpu
AU - Verhaegen, M
N1 - Accepted Author Manuscript
PY - 2015
Y1 - 2015
N2 - This paper studies the local identification of large-scale homogeneous systemswith general network topologies. The considered local system identification problem involves unmeasurable signals between neighboring subsystems. Compared with our previous work in Yu et al. (2014) which solves the local identification of 1D homogeneous systems, the main challenge of this work is how to deal with the general network topology. To overcome this problem, we first decompose the interested local system into separate subsystems using some state, input and output transform, namely the spatially lifted local system has block diagonal system matrices.We subsequently estimate the Markov parameters of the local system by solving a nuclear norm regularized optimization problem. To realize the state-space system model from the estimated Markov parameters, another nuclear norm regularized optimization problem is provided by taking into account of the inherent dependence of a redundant parameter vector. Finally, the overall identification procedure is summarized.
AB - This paper studies the local identification of large-scale homogeneous systemswith general network topologies. The considered local system identification problem involves unmeasurable signals between neighboring subsystems. Compared with our previous work in Yu et al. (2014) which solves the local identification of 1D homogeneous systems, the main challenge of this work is how to deal with the general network topology. To overcome this problem, we first decompose the interested local system into separate subsystems using some state, input and output transform, namely the spatially lifted local system has block diagonal system matrices.We subsequently estimate the Markov parameters of the local system by solving a nuclear norm regularized optimization problem. To realize the state-space system model from the estimated Markov parameters, another nuclear norm regularized optimization problem is provided by taking into account of the inherent dependence of a redundant parameter vector. Finally, the overall identification procedure is summarized.
KW - Subspace identification
KW - nuclear norm
KW - networked systems
UR - http://resolver.tudelft.nl/uuid:a93f4eb7-5bd0-4850-a4c7-1c85021b745f
U2 - 10.1016/j.ifacol.2015.12.192
DO - 10.1016/j.ifacol.2015.12.192
M3 - Conference contribution
SP - 585
EP - 589
BT - IFAC-PapersOnline - 17th IFAC Symposium on System Identification
A2 - Zhao, Y
A2 - Bai, E-W
A2 - Zhang, J-F
PB - IFAC
CY - Laxenburg, Austria
T2 - SYSID 2015, Beijing, China
Y2 - 19 October 2015 through 21 October 2015
ER -