5 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Hendrickx Julien;
Language
English
Prerequisites
- LINMA 1510 (Automatic control) OR LINMA 2300 (Control of processes) OR equivalent
- Having attended a class on stochastic processes (as LINMA 1731) helps, but is not a prerequisite.
Main themes
This class is an introduction to system identification, which consists in finding an appropriate representation of a dynamical system using appropriate measurements. It will cover some of the main parametric and nonparametric methods for identifying dynamical systems, including in closed loop. It will also cover the properties of signals and model classes that are relevant for system identification. A realistic identification project will give students the opportunity to apply and implement the techniques that they will have learned.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | With respect to the L.O. framework, this class contributes to the developpement of the following learning outcomes
More precisely, by the end of the class, the student will be able to :
Transversal learning outcomes :
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The following topics will be covered
- Nonparametric methods: temporal analysis, frequential analysis, including Fourier and spectral analysis
- Main classes of LTI systems and their properties, including the notions of identifiability and predictors
- Certain parametric methods: linear regression, instrumental variables, prediction errors, and some statistical methods including the maximum likelihood method
- The properties of (input) signal, including the notion of information content of the signals and the level of persistence of excitation.
- The convergence of the method seen
- Identification techniques for systems controlled in closed loop
Teaching methods
- Regular lectures.
- Resolutions of simple problems under the supervison of teaching assistant in order to get familiar with new concepts.
- Problem sets to be solved in small group in order to develop a deeper understanding of the concepts.
- A complete project of system identification in realistic conditions.
Evaluation methods
- Exam at the end of the year.
- Identification of a system on the basis of real input/output data (using the Matlab System Identification Toolbox, developed by L. Ljung).
- Problem sets during the year.
Other information
The lectures and problem sessions are in English, and all documents are in English.
Homework, exams, and project reports can be written in English or French.
The organisation details are specified on iCampus.
Homework, exams, and project reports can be written in English or French.
The organisation details are specified on iCampus.
Online resources
Bibliography
Le cours s'appuie sur un syllabus disponible sur icampus
Des livres de références sont également proposés :
Des livres de références sont également proposés :
- L. Ljung System Identification - Theory for the user Prentice Hall, 1999. (disponible en bibliothèque)
- T. Soderstorm and P Stoica, System Identification (http://user.it.uu.se/~ts/sysidbook.pdf)
Faculty or entity
MAP
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Electro-mechanical Engineering
Master [120] in Electrical Engineering
Master [120] in Biomedical Engineering
Master [120] in Mathematical Engineering
Master [120] in data Science: Information technology
Master [120] in Mechanical Engineering