Coursebooks 2017-2018

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System identification

ME-421

Lecturer(s) :

Karimi Alireza

Language:

English

Withdrawal

It is not allowed to withdraw from this subject after the registration deadline.

Summary

Identification of discrete-time linear models using experimental data is studied. The correlation method and spectral analysis are used to identify nonparametric models and the subspace and prediction error methods to estimate the plant and noise model parameters. Hands-on labs are included.

Content

Modelling, type of models and representations. Time-domain nonparametric identification methods (impulse response by the correlation aproach). Frequency-domain nonparametric identification methods based on the Fourier and spectral analysis. Parametric identification by linear regression (least squares method, instrumental variables method, recursive algorithms). Subspace identification methods. Prediction error methods (ARX, ARMAX, OE and BJ structures). Practical aspects of identification (input design, order estimation, model validation). Plant model identification in closed-loop operation.

Keywords

System identification, spectral analysis, correlation approach, prediction error method

Learning Prerequisites

Recommended courses

Dynamic systems, Control systems

Important concepts to start the course

Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

Ex-cathedra course with hands-on labs and project

Expected student activities

Hands-on laboratory for groups of two students, preparing technical reports and a mini project.

Assessment methods

Oral exam (theoretical and practical questions on project and lab reports)

Supervision

Office hours Yes
Assistants Yes
Forum No

Resources

Notes/Handbook

Course-notes (in English): System Identification

Slides available (pdf) in English

Websites

In the programs

    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2017-2018, Master semester 1
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2017-2018, Master semester 3
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Fall
    • Exam form
       During the semester
    • Credits
      3
    • Subject examined
      System identification
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9 BM5202   
9-10    
10-11 BM5202   
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German