SCO-31806 System Identification & Model Predictive Control

Vak

Studiepunten 6.00

Language of instruction:

English

Expected knowledge on:

Signals and Systems Modelling

Continuation courses:

Systems and Control Theory, Control Engineering and Process Control, Real Time Systems

Contents:

The aim of this course is to introduce methods for the identification of dynamic models on the basis of prior system's knowledge, given data and model objective, and to estimate the unknown parameters. In practice, the main model objectives are prediction, experiment design and control, which will receive ample attention. In particular the concept of Model Predictive Control, most relevant for chemical and biological systems, will be further introduced. In the accompanying practical course the System Identification Toolbox will be used for the identification of a laboratory-scale process.

Aims:

- insight in the field of system identification;
- know how to model systems on the basis of experimental input/output data and be able to estimate the unknown model parameters;
- identify a simple dynamic process model from given input/output signals;
- be able to apply the System Identification Toolbox of the MATLAB software package;
- insight in the concept of Model Predictive Control;
- know how to design and apply a Model Predictive Controller.

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:

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

Literature:

Course information, lectures notes "SYSTEM IDENTIFICATION: An Introduction" by K.J. Keesman and additional MPC material will be available at the start of the course. The manuals for the practicals can be obtained via R.J.C. van Ooteghem MSc (practical course leader).