Modelling and Identification
FEKT-NMIDAcad. year: 2019/2020
The subject is oriented on:
- identification methods of dynamic systems
- approaches towards nonparametric and parametric identification
- on-line and off-line identification
- spectral estimation, assessment of noise and disturbance influence on identification results
Learning outcomes of the course unit
Students are able to provide identification of dynamical systems using various methods especially with help of Matlab and its toolboxes.
The subject knowledge on the Bachelor´s degree level is requested.
Recommended optional programme components
Recommended or required reading
Ljung, L.: System Identification, Theory for the User, Prentice Hall, 1987 (EN)
Soderstrom T., Stoica P.: System Identification. Prentice Hall International, 1989 (EN)
Isemrann R., Munchhof M. : Identification of Dynamic Systems - An Introduction with Applications. Springer 978-540-78878-2, 2011. (EN)
Planned learning activities and teaching methods
Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Materials for lectures and exercises are available for students from web pages of the course. Students have to write a single project/assignment during the course.
Assesment methods and criteria linked to learning outcomes
Numerical Exercises - Max 15 points.
Individual project - Max. 15 points.
Final Exam - Max. 70 points.
Language of instruction
1. Introduction into dynamic system identification.
2. Nonparametric identification methods, correlation methods, frequency response measurement.
3. Input signal for identification, degree of persistent excitation, pseudorandom binary sequence.
4. Least squares method, derivation, geometric representation, properties.
5. Dynamic system models for system identification, ARX, ARMAX ARARX, general model, pseudolinear regression.
6. Recursive LSM. Numericaly stable methods based on square root filtering.
7. Instrumental variable methods. Method with delayed observations, method with additional model.
8. Identification methods based on prediction error whitening. Noise model identification.
9. Practical notes on system identification.
10. Identification using neural nets and fuzzy modeling.
11. Another approaches to system identificaiton.
12. Identification of nonlinear dynamic systems.
13. Course summary.
Familiarize students with basic techniques for dynamic system identification and with their possible limitations. The students will get to know how the noise acting on the plant influences the identification results and how to cope with it.
Specification of controlled education, way of implementation and compensation for absences
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.