Identification (Modul A)

This web page lists courses with the full set of in-class English lectures. Additional courses that are also available to English-speaking students, are accessible by clicking above links Winter Semester and Summer semester.

Higher education teachers: Blažič Sašo
Subject code: 64259



Subject description

Prerequisits:

  • Finished B.Sc. of a (preferably) technical study.

Content (Syllabus outline):

System analysis classification, algorithm division, signal analysis (excitation and disturbance signals), the area of use.

Simple methods:

  • Strejc method (based on a step response),
  • Åström method with a relay in a closed-loop,
  • model adaptation method.

Least squares method, regression method, bias and consistency of estimates.

Dynamical model parameter estimation, model parameterisation, extended least squares method, instrumental variables method, recursive versions of least squares, the adaptation for time varying systems – weighted least squares and exponential forgetting, the influence of unknown steady states, numerical problems.

Identification of non-parametric models (frequency response analysis, Fourier analysis, correlation analysis, spectral analysis).

Identification of unstable models and closed-loop identification, identifiability of parametric and non-parametric models.

Identification with pattern recognition.

Practical aspects of identification, sampling time selection, signal pre-processing, model choice, the test of model validity and its structure, the issue of time delays, robustness, the choice of an appropriate method.

Objectives and competences:

  • To present the area of system identification, especially in relation to dynamical systems.
  • To expose the problem of biased identification results in case of ignoring external conditions and/or inappropriate choice of parameters.
  • To present the least squares method and show its applicability in different areas.
  • To show the applicability of parameter estimation methods for dynamical systems.
  • To present the methods of non-parametric model identification.
  • To expose the problems of identification of unstable systems and the problems of identifiability in a closed loop.
  • To introduce the practical problems of identification.

Intended learning outcomes:

  • Knowledge and understanding
  • Deeper understanding of dynamical system identification

Learning and teaching methods:

  • Lectures and laboratory work





Study materials

  1. Karel J. Keesman, System Identification, An Introduction, Springer, 2013.
  2. Drago Matko, Identifikacije, Univerza v Ljubljani, Fakulteta za elektrotehniko, 1998.
  3. Sašo Blažič, Drago Matko, Identifikacija, skripta, 2013.
  4. Sašo Blažič, Identifikacije, Zbirka rešenih nalog, Univerza v Ljubljani, Fakulteta za elektrotehniko, 2007.
  5. Lennart Ljung, System identification, Prentice Hall, 1999.
  6. Torsten Söderström, Petre Stoica, System identification, Prentice Hall, 1994.
  7. Karl Johan Aström. Tore Hägglund: PID Controllers: Theory, Design and Tuning. 2nd Edition. Research Triangle Park, NC: Instrument Society of America, 1995.



Study in which the course is carried out

  • 1 year - 2nd cycle - Electrical Engineering - Information and Communication Technologies
  • 1 year - 2nd cycle - Advanced Power Systems
  • 1 year - 2nd cycle - Electrical Engineering - Electronics
  • 1 year - 2nd cycle - Electrical Engineering - Electrical Power Engineering
  • 1 year - 2nd cycle - Electrical Engineering - Biomedical Engineering
  • 1 year - 2nd cycle - Electrical Engineering - Control Systems and Computer Engineering
  • 1 year - 2nd cycle - Electrical Engineering - Mechatronics
  • 1 year - 2nd cycle - Electrical Engineering - Robotics