Course detail
Modelling and Identification
FEKT-MKC-MIDAcad. year: 2023/2024
Not applicable.
Language of instruction
Czech
Number of ECTS credits
6
Mode of study
Not applicable.
Guarantor
Entry knowledge
Not applicable.
Rules for evaluation and completion of the course
Not applicable.
Aims
Not applicable.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Ljung, L.: System Identification, Theory for the User, Prentice Hall, 1999
Soderstrom, T., Stoica, P.: System Identification. Prentice Hall International, 1989
Soderstrom, T., Stoica, P.: System Identification. Prentice Hall International, 1989
Recommended reading
Fikar, M., Mikleš, J.: Identifikácia systémov. STU Bratislava 1999
Isemrann, R., Munchhof, M. : Identification of Dynamic Systems - An Introduction with Applications. Springer 978-540-78878-2, 2011.
Noskievič, P.: Modelování a identifikace systémů. Montanex Ostrava 1999
Šimandl, M.: Identifikace systémů a filtrace. Západočeská univerzita v Plzni, 2001, ISBN 80-7082-170-1.
Isemrann, R., Munchhof, M. : Identification of Dynamic Systems - An Introduction with Applications. Springer 978-540-78878-2, 2011.
Noskievič, P.: Modelování a identifikace systémů. Montanex Ostrava 1999
Šimandl, M.: Identifikace systémů a filtrace. Západočeská univerzita v Plzni, 2001, ISBN 80-7082-170-1.
Elearning
eLearning: currently opened course
Classification of course in study plans
- Programme N-AIŘ-K Master's 2 year of study, winter semester, compulsory
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
Syllabus
1. Brief introduction into system identification.
2. Parameter identification from impulse and step response.
3. Identification using correlation methods, frequency.
4. Spectral analysis.
5. Input signals for identification, least squares method.
6. Recursive least squares method.
7. Instrumental variable methods.
8. System Identification Toolbox.
9. Identification methods base on whitening of prediction error.
10. Extended frequency analysis, PMS motor parameters identification.
11. Identification using recursive least squares method.
12. Test + work on project.
13. Real experiment - DC motor parameters identification.
2. Parameter identification from impulse and step response.
3. Identification using correlation methods, frequency.
4. Spectral analysis.
5. Input signals for identification, least squares method.
6. Recursive least squares method.
7. Instrumental variable methods.
8. System Identification Toolbox.
9. Identification methods base on whitening of prediction error.
10. Extended frequency analysis, PMS motor parameters identification.
11. Identification using recursive least squares method.
12. Test + work on project.
13. Real experiment - DC motor parameters identification.
Guided consultation
26 hod., optionally
Teacher / Lecturer
Syllabus
Consultation with students.
Elearning
eLearning: currently opened course