Course detail

Modelling and Identification

FEKT-MMIDAcad. 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.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Ljung, L.: System Identification, Theory for the User, Prentice Hall, 1987 (EN)
Noskievič, P.: Modelování a identifikace systémů. Montanex Ostrava 1999 (CS)
Fikar, M-Mikleš J.: Identifikácia systémov. STU Bratislava 1999 (SK)
Soderstrom T., Stoica P.: System Identification. Prentice Hall International, 1989 (EN)
Šimandl, M.: Identifikace systémů a filtrace. Západočeská univerzita v Plzni, 2001, ISBN 80-7082-170-1. (CS)
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

Czech

Work placements

Not applicable.

Course curriculum

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.

Aims

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.

Classification of course in study plans

  • Programme EEKR-M1 Master's

    branch M1-BEI , 1. year of study, winter semester, 5 credits, optional specialized
    branch M1-KAM , 2. year of study, winter semester, 5 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, winter semester, 5 credits, optional specialized

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Computer exercise

26 hours, compulsory

Teacher / Lecturer