Identification of diffusively coupled linear networks through structured polynomial models

Abstract

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 paper 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. Further, a multi-step least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method.

References in corpus (6)

• Revealing Network Connectivity From Dynamics
• Identifiability of linear dynamic networks
• Reduction of Second-Order Network Systems with Structure Preservation
• Model Reduction Methods for Complex Network Systems
• Allocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks
• A scalable multi-step least squares method for network identification with unknown disturbance topology

Cited by in corpus (2)

• Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
• Nonlinear Signal Transmission and Harmonic Generation in Plasma Oscillator Chain with Coupling Asymmetry