Arne Dankers

ICT 413

Department of Electrical and Software Engineering

Calgary, Canada

I was awarded an M.Sc. at the University of Calgary in Electrical and Computer Engineering and a Ph.D. from the Delft Center for Systems and Control (DCSC) at the Delft University of Technology in The Netherlands.

My research is in system identification (aka data driven modelling). The main point of system identification is to estimate a model of a system (i.e. learn the governing differential equations of a system) given data generated by the system. I am developing a framework to make it possible to estimate the dynamics of a component of a complex system the consists of several of interconnected, nonlinear components. Some examples include power grids and robotic systems. This framework can then be used to monitor the components of a system for changes or faults (preventative maintenance, or fault detection), or to (re)design/adapt controllers to ensure optimality. For example, the objective may be to monitor the health of a battery connected to the grid, or monitor a gearbox in a robotic arm to detect when the gearbox is wearing out so that it can be replaced before it fails.

I combine tools from control engineering, system identification, statistics, machine learning, mathematical modelling, and graph theory to develop this framework.

selected publications

Identification of dynamic models in complex networks with prediction error methods—Basic methods for consistent module estimates

Paul M.J. Van den Hof, Arne Dankers, Peter S.C. Heuberger, and Xavier Bombois

Automatica, 2013

Abs DOI

The problem of identifying dynamical models on the basis of measurement data is usually considered in a classical open-loop or closed-loop setting. In this paper, this problem is generalized to dynamical systems that operate in a complex interconnection structure and the objective is to consistently identify the dynamics of a particular module in the network. For a known interconnection structure it is shown that the classical prediction error methods for closed-loop identification can be generalized to provide consistent model estimates, under specified experimental circumstances. Two classes of methods considered in this paper are the direct method and the joint-IO method that rely on consistent noise models, and indirect methods that rely on external excitation signals like two-stage and IV methods. Graph theoretical tools are presented to verify the topological conditions under which the several methods lead to consistent module estimates.
Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

Arne Dankers, Paul M. J. Hof, Xavier Bombois, and Peter S. C. Heuberger

IEEE Transactions on Automatic Control, Apr 2016

Abs DOI

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.
Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach

Arne Dankers, Paul M.J. Van den Hof, Xavier Bombois, and Peter S.C. Heuberger

Automatica, Apr 2015

Abs DOI

In this paper we consider the identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network. This is an extension of the errors-in-variables (EIV) identification framework to the case of dynamic networks. The consequence of measuring the variables with sensor noise is that some prediction error identification methods no longer result in consistent estimates. The method developed in this paper is based on a combination of the instrumental variable philosophy and closed-loop prediction error identification methods, and leads to consistent estimates of modules in a dynamic network. We consider a flexible choice of which internal variables need to be measured in order to identify the module of interest. This allows for a flexible sensor placement scheme. We also present a method that can be used to validate the identified model.