Identification of dynamic models in complex networks with Prediction Error Methods

Predictor Input Selection

Journal article (2016)

Authors

A.G. Dankers OLD Model-based Measurement & Control
P.M.J. van den Hof Eindhoven University of Technology
X.J.A. Bombois École Centrale de Lyon
P.S.C. Heuberger Eindhoven University of Technology
Research Group
OLD Model-based Measurement & Control
Graph theory System identification Linear systems Dynamic networks Closed-loop identification
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Published Date

2016

Language

English

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Research Group

OLD Model-based Measurement & Control

Abstract

This paper addresses the problem of obtaining an estimate of a particular module of interest that is embedded in a dynamic network with known interconnection structure. In this paper it is shown that there is considerable freedom as to which variables can be included as inputs to the predictor, while still obtaining consistent estimates of the particular module of interest. This freedom is encoded into sufficient conditions on the set of predictor inputs that allow for consistent identification of the module. The conditions can be used to design a sensor placement scheme, or to determine whether it is possible to obtain consistent estimates while refraining from measuring particular variables in the network. As identification methods the Direct and Two Stage Prediction-Error methods are considered. Algorithms are presented for checking the conditions using tools from graph theory.