Course detail

Modelling and Identification

FEKT-MPC-MIDAcad. year: 2020/2021

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

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Ing. Petr Blaha, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

After passing the course, student should be able to
- use non-parametric identification methods
- select suitable excitation signal for the identification
- program and use the basic least squares method
- explain where biased estimate comes from and how to overcome this issue
- use the approaches which enable to enhance the quality of the estimates during practical applications
- utilize the universal programming equipment of MATLAB Simulink and its toolboxes for the identification of dynamic systems

Prerequisites

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

Co-requisites

Not applicable.

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.

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. Numerically 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 identification.
12. Identification of nonlinear dynamic systems.
13. Recapitulation, course summary.

Work placements

Not applicable.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Ljung, L.: System Identification, Theory for the User, Prentice Hall, 1999 (EN)
Soderstrom, T., Stoica, P.: System Identification. Prentice Hall International, 1989 (EN)

Recommended reading

Fikar, M., Mikleš, J.: Identifikácia systémov. STU Bratislava 1999 (SK)
Isemrann, R., Munchhof, M. : Identification of Dynamic Systems - An Introduction with Applications. Springer 978-540-78878-2, 2011. (EN)
Noskievič, P.: Modelování a identifikace systémů. Montanex Ostrava 1999 (CS)
Šimandl, M.: Identifikace systémů a filtrace. Západočeská univerzita v Plzni, 2001, ISBN 80-7082-170-1. (CS)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MPC-KAM Master's 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Petr Blaha, Ph.D.

Syllabus

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. Numerically 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 identification.
12. Identification of nonlinear dynamic systems.
13. Course summary.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Ondřej Bartík

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.

Elearning

eLearning: currently opened course