|
Lecturer(s)
|
-
Dušek František, doc. Ing. CSc.
-
Honc Daniel, Ing. Ph.D.
|
|
Course content
|
Lectures and exercises are devoted to the following topics (in each week of the semester): 1. Introduction to experimental identification (EI) and mathematical-physical analysis (MFA) 2. Experiment planning, input signal selection, random process statistical characteristics 3. Explicit solution of EI using least squares method (LSM) - standard and extended version 4. Recurrent EI solutions using LSM, exponential forgetting 5. Numeric EI of continuous transfer function parameters 6. Laboratory application of EI methods - GUNT systems 7. Examples of MFA applications - hydraulic systems - water tanks 8. Examples of using MFA - thermal systems - heater, flow heater 9. Examples of using MFA - electrical systems - RLC 10. Examples of MFA applications - electromagnetic systems - DC motor, magnetic levitation 11. Examples for using MFA - mechanical systems - ball on plate 12. Laboratory application of MFA methods - GUNT laboratory systems 13. Laboratory application of MFA methods - GUNT laboratory systems
|
|
Learning activities and teaching methods
|
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Demonstration, Laboratory work
- Preparation for an exam
- 24 hours per semester
- Home preparation for classes
- 40 hours per semester
- Term paper
- 34 hours per semester
- Participation in classes
- 52 hours per semester
|
|
Learning outcomes
|
The aim of the course is to acquaint students with the problems of modeling and identification of dynamic systems, to teach them to create a model using mathematical-physical analysis and experimental identification.
After completing the course, the students demonstrate knowledge of modeling and identification. They can decide on the structure of the model and obtain data for experimental identification of continuous and discrete-time transfer function parameters, they can create models of hydraulic, thermal, electrical, electromagnetic and mechanical systems in an analytical way, they can design and realize experiments to find unknown parameters.
|
|
Prerequisites
|
The knowledge of mathematics and physics at the basic university level is assumed.
|
|
Assessment methods and criteria
|
Oral examination, Home assignment evaluation, Discussion
Students are motivated in a positive way to physically participate in lectures and exercises. They are required to be active and provide feedback during the teaching period. Towards the end of the teaching period, students are provided with an updated document of the topics that will be covered in the exam. The examination is oral. Students are given a number of questions and tasks, given time to prepare, and then the teacher individually assesses their answers to see how well they know the subject matter and grades their knowledge in the standard way with a grade of A to F. If a student fails the exam, the teacher clearly tells them why this has happened and, if possible, briefly explains the subject matter again.
|
|
Recommended literature
|
-
Drábek, O. Experimentální identifikace. Pardubice, 1987.
-
Dušek, F. Matlab a Simulink: úvod do používání. Pardubice, 2005. ISBN 80-7194-776-8.
-
Noskievič, P. Modelování a simulace mechatronických systémů pomocí programu MATLAB SIMULINK.. Ostrava, 2013. ISBN 978-80-248-3231-9.
-
Severance, F. L. System modeling and simulation: an introduction. New York, 2001. ISBN 978-0471496946.
|