Current research projects

Finite sample properties of system identification.

Students: Su Ki Ooi (PhD)
Coworkers: Marco Campi, University of Brescia, Italy,

System identification deals with the problem of building mathematical models of dynamical systems based on observed data. The asymptotic properties of system identification methods are well known, but comparatively little is known about the practical situation where only a finite number of data points are available. In this project the finite sample properties of prediction error methods are considered. Prediction error methods aim at minimising a positive criterion function of the prediction errors so that the identified model is the one with the smallest prediction errors. Ideally on would like to minimise the expected value of the criterion function, but since the underlying probability measure is unknown, this is not possible. Instead we employ a prediction error method and minimise the empirical value of the criterion function evaluated on the observed data points.

In this project we have derived non-asymptotic bounds on the value of the criterion function and non-asymptotic confidence ellipsoids for the parameter estimate. Using these bounds we can compute the number of data points required in order to guarantee a certain bound with high probability. So far the results have been obtained using techniques and methods from learning theory extended to a dynamical system setting. Currently, we are working towards refining these results so as to incorporate a-posteriori information (provided by data).

System identification and control of irrigation channels.

Students: Su Ki Ooi (PhD), Yuping Li (PhD), Ping (Valerie) Zhang (MEng)

Collaborators: Iven Mareels, Michael Cantoni, Girish Nair, University of Melbourne,
Rubicon Systems Australia, Pty, Ltd.

Water is becoming an increasingly scarce resource world wide, and modern open channel irrigation systems faces conflicting demands such as delivering water on short notice and minimum wastage of water. In this project together with industrial partner Rubicon Systems Australia Pty, Ltd, we are looking at ways of improving the efficiency of water delivery using system identification and control techniques. The water levels and flows are controlled by operating gates which are located throughout the channel network. As water levels and gate positions are measured at each gate, large amounts of high quality data are available for system identification and estimation purposes. Particular topics addressed in this work include: Estimation of flow over a gate using level and gate position measurements, System identification of irrigation channels for prediction and control purposes, Design of decentralised (monivariable) control systems, Design of multivariable control systems for irrigation channel networks, Control under communication constraints and Fault detection and performance monitoring systems for irrigation channels.
More information on CSSIP's webpage.
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