13E053MIP - System Modeling and Identification

Course specification
Course title		System Modeling and Identification
Acronym		13E053MIP
Study programme		Electrical Engineering and Computing
Module		Signals and Systems
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Aleksandra Krstić
Lecturer/Associate (for practice)		asst. Aleksa Stojić, electrical and computer engineer
Lecturer/Associate (for OTC)		asst. Aleksa Stojić, electrical and computer engineer Maša Tiosavljević, electrical and computer engineer Aleksa Jovanović, electrical and computer engineer
ESPB		6.0	Status	elective
Condition		none
The goal		The objective of the course is to introduce students to different approaches to modeling and identification of continuous and discrete systems, both in terms of theoretical basis of system identification in time, frequency and complex domain, as well as in terms of practical aspects of identification through the use of computers in simulation, modeling and system identification .
The outcome		Students will be competent to plan and conduct the experiments and data acquisition that will enable quality modeling and identification of different systems, select appropriate model representation, and accordingly apply nonparametric and parametric system identification techniques, as well as perform validation and simulation the behavior of the obtained models.
Contents
URL to the subject page		https://automatika.etf.bg.edu.rs/sr/13e053mip
URL to lectures		https://teams.microsoft.com/l/team/19%3aTevSoz2KsyP4fJZ1AIzn7uxLAEK8oUOTjVBynO1JRME1%40thread.tacv2/conversations?groupId=c2c87e14-ec80-4487-a1d5-55e6efa65df2&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures		System modeling. Nonparametric and parametric representation in time domain. Nonparametric identification: correlation analysis in time and frequency domain, empirical transfer function evaluation. Parametric identification in time domain: prediction error methods, linear and pseudo-linear least squares method, recursive identification, instrumental variables. Model validation.
Contents of exercises		Through different examples and assignments, students use the appropriate software to form and analyze the characteristic classes of linear and nonlinear models, solve identification problems of specific systems by various methods presented during theoretical lectures, examine the characteristics of different estimators, the notion of persistent excitation and the validity of the obtained model.
Literature
Process identification, Kovačević B, Kvaščev G, Akademska misao, Beograd, 2018. System Identification: Theory for the User, Ljung L, Prentice-Hall, Englewood Cliffs, New Jersey, 1987. System identification toolbox: User's guide, Lennart Ljung, Natick, MA: MathWorks Incorporated, 1995. Modeling, Identification and Simulation of Dynamical Systems, P.P.J. van den Bosch, A.C. van der Klauw, CRC Press, 1994.
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
3	1	1
Methods of teaching		Lectures (45), exercises (15), computer exercises (15).
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		0	Test paper	40
Practical lessons		20	Oral examination	0
Projects		0
Colloquia		40
Seminars		0