5.00 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Lataire John;
Language
English
Prerequisites
This courses assumes familiarity with transfer functions, as taught in
LINMA1510 (Linear Control) or LFSAB1106 (Applied mathematics : Signals
and systems)
LINMA1510 (Linear Control) or LFSAB1106 (Applied mathematics : Signals
and systems)
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.
Learning outcomes
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
|
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
The grade will be based on
- An exam at the end of the year. The exam is normally a written exam, but may be replaced by a remote oral exam in case required by sanitary situation or by practical constraints.
- A project on the identification of a system on the basis of real input/output data. The grade of the project is determined by the report prepared by the students, and on an individual oral interview.
- Problem sets during the year. The problems are graded based on the student's demonstration of his/her understanding of the problem and its solution. This may involve individual oral interviews.
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.
Students are expected to be familiar with dynamical systems and transfer functions.
Students are expected to be familiar with dynamical systems and transfer functions.
Online resources
Bibliography
Le cours s'appuie sur un syllabus disponible sur Moodle
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
Learning outcomes
Master [120] in Mechanical Engineering
Master [120] in Data Science Engineering
Master [120] in Electrical Engineering
Master [120] in Electro-mechanical Engineering
Master [120] in Data Science: Information Technology
Master [120] in Biomedical Engineering
Master [120] in Mathematical Engineering