System Identification

linma2875  2026-2027  Louvain-la-Neuve

System Identification
5.00 credits
30.0 h + 30.0 h
Q2
> Schedule
Teacher(s)
Bianchin Gianluca;
Language
English
> French-friendly
Prerequisites
This courses assumes familiarity with transfer functions, as taught in
LINMA1510 (Linear Control) or LEPL1106 (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 :

With respect to the L.O. framework, this class contributes to the developpement of the following learning outcomes
  • AA1.1, AA1.2, AA1.3
  • AA2.1, AA2.4
  • AA3.2
  • AA5.3, AA5.5
More precisely, by the end of the class, the student will be able to :
  • recognize a problem of system identificaiton;
  • propose and implement solutions to simple identification problems;
  • identify a dynamical systems using input-output data;
  • validate a model of system that has been identified, and compare different simple models;
  • design an experiment to identify a simple system;
  • develop a deeper understanding of system identification by him/herself if necessary  in order to solve more complex problems.
Transversal learning outcomes :
  • Handling unforeseen technical issues that appear when treating a real-world problem.
  • Making reasonable hypothesis for a given problem, and evaluating them a posteriori.
  • Taking part to a technical class in English.
 
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 problems under the supervision of a teaching assistant
  • Laboratory sessions to be realized in the laboratory room using the available equipment and Matlab or Python
The activities will mainly take place in person but may be organized partly or entirely remotely if required by the sanitary situation or by practical constraints.
Evaluation methods
  • Final examination (written or oral)
  • Laboratory assessment (report) completed during the semester
  • Assignments completed during the semester
  • Let (F) be the final examination grade, (D) the combined assignment grade (arithmetic mean), and (L) the combined laboratory grade (arithmetic mean), all out of 20. The final grade will be calculated as follows:
If (L < 10): Final grade = (L)
If (L \geq 10): Final grade = (50%F + 25%D + 25%L)
This reflects the requirement that students individually demonstrate a minimum level of mastery of the learning outcomes, corresponding to 10/20.
  • Laboratory experiments, laboratory assessments, and quizzes/assignments will not be organised during the second examination session and cannot be completed outside the semester. For the second examination session in August, the laboratory assessment and assignment grades will be retained. Consequently, the formula used to calculate the final grade in the second examination session remains the same as in the first session, with (D) and (L) retained.
  • In the second examination session, only the grade obtained in the second-session written examination will be taken into account when calculating the final grade.
  • If the student re-enrols in a subsequent academic year, the laboratory experiments and assignments must be completed again, except in exceptional circumstances and only with the lecturer’s approval.
  • In specific circumstances, other activities, such as oral assessments or assignments, may be used in place of other assessments.
  • The language of the written examination and continuous assessments is the same as the language of instruction (English).
The use of AI and the distribution or exchange of solutions, whether in whole or in part, are strictly prohibited for all assessed activities.
Other information
The main language used during lectures, exercise sessions, and the laboratory is English. 
Students are expected to be familiar with dynamical systems and transfer functions.
 
Online resources
https://moodleucl.uclouvain.be/course/view.php?id=9007
Bibliography
The course will mainly use notes made available on Moodle. Suggested readings are listed below. 
  1. L. Ljung System Identification - Theory for the user, Prentice Hall, 1999. (disponible en bibliothèque)
  2. T. Soderstorm and P. Stoica, System Identification (http://user.it.uu.se/~ts/sysidbook.pdf)
  3. P. van Overschee and B. de Moor  - Subspace Identification for Linear Systems: Theory, Implementation, Applications, Springer, 2011.
Faculty or entity
> MAP


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