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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
SSY230 - System identification
 
Syllabus adopted 2008-02-27 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering
Department: 32 - ELECTRICAL ENGINEERING

This course round is cancelled


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0108 Examination 4,5c Grading: TH   4,5c    
0208 Laboratory 3,0c Grading: UG   3,0c    

In programs

MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR - Special research and PhD course, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR - Special research and PhD course, Year 1 (elective)

Examiner:

Professor  Jonas Sjöberg



Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic knowledge in automatic control, statistics, signals and systems

Aim

The course aims to give the fundamental theory of identification of dynamical systems, i.e. how to use measured input-output data to build mathematical models, typically in terms of differential or difference equations. It is an advanced course offered to Ph.D students and Master students. The course requires more independent active work from the participants than normally at the master's program.

Learning outcomes (after completion of the course the student should be able to)

  • understand and explain the properties of the input signals for an identification experiment influence the quality of the estimated model.
  • understand and explain the possibilities and limitations concerning the quality of estimated models and on which factors these limitations depend.
  • understand and explain properties of different model structures and identification methods.
  • understand and use methods for validating estimated models.
  • understand and use computer tools for system identification.

      Content

      The course includes:
      • The mathematical foundations of System Identification
      • Choice of model structure Linear and nonlinear models
      • Non-parametric techniques
      • Parametrizations and model structures
      • Parameter estimation
      • Asymptotic statistical theory
      • User choices
      • Experimental design

          Organisation

          The course comprises lectures and a number of hands on assignments/laboratory experiments that address important parts of the course.

          Literature

          Not decided yet

          Examination

          Examination is based on written exam, grading scale TH, and passed assignment/laboration.


        • Page manager Published: Thu 04 Feb 2021.