BCT-31806 Parameter Estimation and Model Structure Identification

Course

Credits 6.00

Language of instruction:

English

Assumed knowledge on:

BCT-20306 Modelling Dynamic Systems.

Continuation courses:

BCT-31306 Systems and Control Theory; BCT-21306 Control Engineering; BCT-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 biobased sciences.
In the accompanying practical course the System Identification Toolbox will be used for the identification of a laboratory-scale process.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- identify a simple dynamic process model from given input/output signals;
- model systems on the basis of prior system's knowledge and experimental input/output data, and to estimate the unknown model parameters;
- simulate and analyse 'simple' models from systems biology and to estimate the unknown parameters;
- apply the 'System Identification Toolbox' of the Matlab software package.

Activities:

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

Examination:

Observations during practical's. The practical report is judged with a mark.
Exam consist of the average of the take home exams (60%) and the practical report (40%) with scores of at least 5.5.

Literature:

Course information
K.J. Keesman (2011). System Identification: an Introduction. London. Springer. 323p. ISBN 978-0-85729-522-4. Series: Advanced Textbooks in Control and Signal Processing.
Additional material will be available at the start of the course.