SCO-31806 Parameter Estimation and Model Structure Identification

Course

Credits 6.00

Language of instruction:

English

Expected knowledge on:

SCO-20306 Modelling Dynamic Systems

Continuation courses:

SCO-31306 Systems and Control Theory, SCO-21306 Control Engineering and Process Control, SCO-30806 Physical Modelling.

Contents:

Mathematical models are crucial in many scientific fields. Modelling that starts from first principles can then be a good start. For operational use in real practice, however, the model should be in good accordance with experimental data.
The aim of this course is to introduce methods for the determination or identification of static/dynamic models starting from prior system's knowledge, given data and the model objective, and to estimate the unknown parameters. In practice, the main model objectives are prediction, experiment design and management/control, which will receive ample attention.
The main application fields are the agro/biotechnology, environmental sciences and food science. In particular, attention will be paid to parameter estimation and network identification within systems biology. In the accompanying practical course the System Identification Toolbox will be used for the identification of a laboratory-scale process.

Learning outcomes:

At the end of the course, it is expected that the student is able:
- to identify a simple dynamic process model from given input/output signals;
- to model systems on the basis of prior system's knowledge and experimental input/output data, and to estimate the unknown model parameters;
- to simulate and analyze 'simple' models from systems biology and to estimate the unknown parameters;
- to apply the 'System Identification Toolbox' of the Matlab software package.

Activities:

- self study to prepare for the lectures and practicals;
- lectures to provide some additional background material;
- computer practicals to apply the theory in a Matlab environment;
- practicals using a laboratory set-up.

Examination:

Six take-home exams, containing four to eight exercises, and a report of the practical exercises.

Literature:

Course information, lectures notes "SYSTEM IDENTIFICATION: An Introduction" by K.J. Keesman and additional material will be available at the start of the course.