Course: System Identification

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Course title System Identification
Course code MTI/IDS
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
  • Hubka Lukáš, Ing. Ph.D.
Course content
Contents of the course: This course covers identification methods of linear and nonlinear systems under presence of noise and disturbances. The identification methods of continuous and descrete models is lectured. The course assumes the knowledge of elementary SISO identification methods that were explained in the previous courses and it considerably extends the range of SISO methods towards more advanced and more effective procedures. Identification methods for MIMO case are explained in the second part of the course. Students are obliged to perform experiments in laboratories with these systems in order to gain practical experience with the application of identification methods. The identification methods are programmed using MATLAB software.

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing), Laboratory work
  • Class attendance - 56 hours per semester
Learning outcomes
The course is serving an overview about several system identification methods, linear as nonlinear ones. There are described methods resulting in nonparametric and/or parametric model. Methods resulting in the parametric model are in continuous and discrete time. One part, of course, is focused on online identification methods. A separate part of the course shows the model validation utilization and a methodology how to select the right model and the model structure. The application of all methods is demonstrated on examples.
Students will apply acquired knowledge in design and in optimizing of dynamic system models. The developed models can be used for designing of control systems in power and machine engineering, in chemical, glass, aircraft and car industries. Students received experiences of mathematical modelling can use in the optimization and in the quality increase of large assortment of technical products.
Condition of registration: Exam from subject Basics of Control Engineering.

Assessment methods and criteria
Oral exam, Practical demonstration of acquired skills

Recommended literature
  • Noskievič, P. Modelování a identifikace systémů. MONTANEX a.s., Ostrava, 1999. ISBN 80-7225-030-2.
  • Schoukens, J., Pintelon, R., Rolain, Y. Mastering System Identification in 100 Exercises. IEEE Press-Wiley, 2012.
  • Schoukens, J. System Identification. Brusel, 2013.
  • Soderstrom, T., Stoica, P. System Identification. Prentice Hall, Uppsala, 2001. ISBN 0-13-881236-5.
  • Van Overschee, P., De Moor, B. Subspace Identification for Linear systems: Theory - Implementation - Applications. Kluwer Academic Publishers, Leuven, 1996.

Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Automatic Control and Applied Computer Science (2016) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Summer