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Graduate courses
Departments' graduate courses for PhD-students.
Syllabus for |
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| SSY230 - System identification |
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| Syllabus adopted 2015-02-17 by Head of Programme (or corresponding) |
| Owner: MPSYS |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Not passed |
| Education cycle: Second-cycle |
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Major subject: Automation and Mechatronics Engineering, Electrical Engineering
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Department: 32 - ELECTRICAL ENGINEERING
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Teaching language: English
Block schedule:
D
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0108 |
Examination |
4,5 c |
Grading: TH |
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4,5 c
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31 May 2016 pm EKL, |
05 Apr 2016 pm M, |
Contact examiner |
| 0208 |
Laboratory |
3,0 c |
Grading: UG |
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3,0 c
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In programs
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (elective)
Examiner:
Professor Jonas Sjöberg
Go to Course Homepage
Eligibility:
In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
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.
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