Identification of Diffusively Coupled Linear Networks Through Structured Polynomial Models
- Resource Type
- Authors
- Lizan Kivits; Paul M.J. Van den Hof
- Source
- IEEE Transactions on Automatic Control, 68(6), 3513-3528. Institute of Electrical and Electronics Engineers
IEEE Transactions on Automatic Control
- Subject
- Integrated circuit interconnections
physical networks
Diffusive couplings
linear dynamic networks
Object recognition
Topology
Computer Science Applications
Springs
data-driven modeling
Control and Systems Engineering
Power system dynamics
Couplings
Heuristic algorithms
Electrical and Electronic Engineering
parameter estimation
system identification
- Language
- ISSN
- 2334-3303
0018-9286
Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric, and therefore, physical dynamic networks can be represented by undirected graphs. This article shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Furthermore, a multistep least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method.