4 ECTS credits
110 u studietijd

Aanbieding 1 met studiegidsnummer 4005030ENR voor alle studenten in het 1e semester met een verdiepend master niveau.

Semester
1e semester
Inschrijving onder examencontract
Niet mogelijk
Beoordelingsvoet
Beoordeling (0 tot 20)
2e zittijd mogelijk
Ja
Onderwijstaal
Engels
Onder samenwerkingsakkoord
Onder interuniversitair akkoord mbt. opleiding
Uitdovend
Ja
Faculteit
Faculteit Ingenieurswetenschappen
Verantwoordelijke vakgroep
Elektriciteit
Onderwijsteam
John Lataire (titularis)
Onderdelen en contacturen
18 contacturen Hoorcollege
36 contacturen Werkcolleges, practica en oefeningen
48 contacturen Zelfstudie en externe werkvormen
Inhoud

This course describes the various steps one must go through for obtaining a linear model of a dynamical process. It starts with the choice (design) of the measurement setup, the choice (design) of the excitation signal, the choice of the parametric model (discrete time, continuous time, parametric versus non-parametric noise model, …), the estimation of the parametric model (identification toolboxes in Matlab), till finally the model selection and the model validation. Hereby the influence of each error source (stochastic measurement errors, systematic measurement errors, nonlinear distortions, time-varying effects, model errors, …) on the result is studied in detail.

Overview of the content

  • Nonparametric Estimation of the Frequency Response Function and the Noise Variance
    • Estimation in open and closed loop
    • Impact of nonlinear and/or time-varying behaviour
    • Local polynomial method
    • Multiple-input, multiple output systems
  • Parametric models for linear time-invariant systems
    • Spectral representations of the dynamic systems
    • Impact measurement setup – zero-order-hold versus band-limited
    • Lumped versus distributed models
    • Multiple-input, multiple output systems
  • Parametric Estimation Plant Model using a Known and estimated Noise Model: generation initial estimates, minimization cost function, calculation covariance)
  • Guidelines

Overview of the project

  • Design of a special class of periodic excitation signals for measuring dynamical systems: quantification of the level of the noise, the nonlinear distortions and the time-varying effects.
  • Use of an advanced data acquisition setup for measuring dynamical systems: uploading and downloading the signals via Matlab.
  • Measurement and identification of electronic circuits available in the lab: nonlinear dynamical systems and time-varying dynamical systems.
  • Use of existing system identification toolboxes in Matlab (Time- or Frequency Domain System Identification toolboxes) and/or development of own Matlab routines.
Studiemateriaal
Handboek (Aanbevolen) : System Identification, Theory for the User, English. L. Ljung, 2de, Biliotheek van de vakgroep, 9780136566953, 1999
Handboek (Vereist) : System identification, A Frequency Domain Approach, R. Pintelon and J. Schoukens, 2de, Biliotheek van de vakgroep, 9780470640371, 2012
Praktisch cursusmateriaal (Aanbevolen) : recent papers from top ranked international journals on system identification
Cursustekst (Vereist) : Identification of dynamical systems, John Lataire, Canvas
Handboek (Vereist) : Mastering System Identification in 100 Exercises, J. Schoukens, R. Pintelon, and Y. Rolain, 1st, Biliotheek van de vakgroep, 9780470936986, 2012
Bijkomende info

Expected prior knowledge

  • A good knowledge of system theory, basic statistics, basic numerical analysis, and basic data-driven modelling
  • Basic lab skills for measuring dynamical systems

Course material

  • Digital course material (Recommended):
    • Slides and notes used during the lectures are made available on Canvas
    • Frequency Domain System Identification Matlab toolbox available for the students
  • Strongly recommended reference material::
    • R. Pintelon and J. Schoukens (2012). System Identification: A Frequency Domain Approach, 2nd edition, Wiley-IEEE Press, Hoboken, NJ (USA).
    • J. Schoukens, R. Pintelon, and Y. Rolain (2012). Mastering System Identification in 100 Exercises, Wiley-IEEE Press, Hoboken, NJ.
Leerresultaten

Algemene competenties

General expectation

After following this course, the student should be able to solve independently a system identification problem. A critical interpretation of the measurements and the results obtained is of key importance.

Detailed outcomes

To successfully complete this course, the student should be able to:

  • Choose an appropriate measurement setup for a given identification problem (prediction/control or physical interpretation).
  • Design an excitation signal for measuring simultaneously in a user-defined frequency band the dynamic behaviour of a system, the noise level, the level of the nonlinear distortions, and the level of the time-varying effects.
  • Select and validate a model (discrete- or continuous-time, dynamic order) from noisy input, noisy output observations.
  • Select an appropriate estimator for a particular identification problem.
  • Analyse and interpret critically measurement and identification results.
  • Make a motivated choice between non-parametric and parametric noise models.
  • Write a clear and concise report of your identification project. Notice unexpected/peculiar phenomena in measurements and try to find an explanation. Highlight and discuss unexpected/peculiar identification results and try to find an explanation.
  • Present, explain and motivate your identification project in an oral session.

In addition, the student must have acquired insight into:

  • The problems that arise when the input signal is not known exactly and the possible solutions.
  • The difficulties of identifying dynamical systems operating in closed loop, and the possible solutions.
  • The peculiarities of identifying multiple-input, multiple-output systems.

Contribution to the program outcomes of the Master in Electronics and Information Technology Engineering

The Master in Engineering Sciences has in-depth knowledge and understanding of

  1. exact sciences with the specificity of their application to engineering
  2. the advanced methods and theories to schematize and model complex problems or processes

The Master in Engineering Sciences can

  1. reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
  2. correctly report on research or design results in the form of a technical report or in the form of a scientific paper
  3. collaborate in a (multidisciplinary) team
  4. work in an industrial environment with attention to safety, quality assurance, communication and reporting
  5. develop, plan, execute and manage engineering projects at the level of a starting professional
  6. think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information

The Master in Engineering Sciences has

  1. a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
  2. a critical attitude towards one's own results and those of others
  3. the flexibility and adaptability to work in an international and/or intercultural context
  4. an attitude of life-long learning as needed for the future development of his/her career

The Master in Electronics and Information Technology Engineering:

  1. Has an active knowledge of the theory and applications of electronics, information and communication technology, from component up to system level.
  2. Has a profound knowledge of either (i) nano- and opto-electronics and embedded systems, (ii) information and communication technology systems or (iii) measuring, modelling and control.
  3. Is able to analyze, specify, design, implement, test and evaluate individual electronic devices, components and algorithms, for signal-processing, communication and complex systems.
  4. Is able to model, simulate, measure and control electronic components and physical phenomena.

Beoordelingsinformatie

De beoordeling bestaat uit volgende opdrachtcategorieën:
Examen Mondeling bepaalt 100% van het eindcijfer

Binnen de categorie Examen Mondeling dient men volgende opdrachten af te werken:

  • oral examination project met een wegingsfactor 1 en aldus 100% van het totale eindcijfer.

    Toelichting: During the oral examination the student can use his/her written report, the course notes, and the available system identification literature.

Aanvullende info mbt evaluatie

Factors that determine the judgement

  • The quality of the written report (language, scientific content, critical attitude with respect to own results and those in the literature).
  • The quality of the oral discussion about the identification project.
  • The amount of project work performed (number of measurements and devices identified, own software developed, comparison of different methods).
  • The understanding of the basic concepts of the course (open book exam).

Grading

  • The oral open book exam counts for 100% of the final mark.
  • Notes:
    • The report must be turned in by the date that was communicated.
    • It is the responsibility of the student to make an appointment for making the measurements and for defending the project. If no appointment is made, this is equivalent to an absence.
Toegestane onvoldoende
Kijk in het aanvullend OER van je faculteit na of een toegestane onvoldoende mogelijk is voor dit opleidingsonderdeel.

Academische context

Deze aanbieding maakt deel uit van de volgende studieplannen:
Master in de ingenieurswetenschappen: fotonica: Standaard traject
Master of Photonics Engineering: On campus traject (enkel aangeboden in het Engels)
Master of Photonics Engineering: Online/Digital traject (enkel aangeboden in het Engels)