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Lecturer(s)
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Course content
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1. Introduction to predictive control 2. Formulation of the cost function, predictor and minimization method 3. Simple examples of controllers with analytical solution 4. Simple examples solved by matrix operations 5. Derivation of general predictor for input / output description 6. Derivation of general predictor for state-space description 7. Analytical solution - controller without considering the constraints 8. Solution using quadratic programming - controller with considering the constraints 9. Simulation application of input / output controller 10. Simulation application of state-space variant of the controller 11. Laboratory application to proportional hydraulic system 12. Laboratory applications to proportional mechanical system 13. Laboratory applications to integration mechanical system
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Demonstration, Laboratory work
- Contact teaching
- 52 hours per semester
- Preparation for an exam
- 20 hours per semester
- Individual project
- 10 hours per semester
- Home preparation for classes
- 26 hours per semester
- Team project
- 11 hours per semester
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Learning outcomes
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The aim of the course is to acquaint students with the problems of predictive control - from the mathematical derivation, design of the algorithms, programming to the applications of the predictive controllers.
Student after completing the course - demonstrates knowledge of predictive control - can work with cost function, predictor, analytical solution without the existence of constraints as well as with the quadratic programming in case of the constrained control action and controlled variable. - can derive, realize and apply the controller in MALTAB / Simulink environment for selected laboratory systems
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Prerequisites
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Basic knowledge of technical subjects.
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Assessment methods and criteria
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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.
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Recommended literature
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CAMACHO, E. F. a C. BORDONS. Model predictive control. New York: Springer, 2004. ISBN 1852336943.
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DUŠEK, František a Daniel HONC. Matlab a Simulink: úvod do používání. Pardubice: Univerzita Pardubice, 2005. ISBN 80-7194-776-8.
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ROSSITER, J. A. Model-based predictive control: a practical approach. Boca Raton: CRC Press, 2003. ISBN 0849312914.
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