Subject Datasheet
Download PDFBudapest University of Technology and Economics | |
Faculty of Transportation Engineering and Vehicle Engineering |
1. Subject name | Control theory | ||||
2. Subject name in Hungarian | Irányításelmélet ML | ||||
3. Code | BMEKOKAM122 | 4. Evaluation type | mid-term grade | 5. Credits | 5 |
6. Weekly contact hours | 2 (11) Lecture | 1 (5) Practice | 1 (5) Lab | ||
7. Curriculum | Logistics Engineering MSc (L) |
8. Role | Mandatory (mc) at Logistics Engineering MSc (L) |
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9. Working hours for fulfilling the requirements of the subject | 150 | ||||
Contact hours | 56 | Preparation for seminars | 15 | Homework | 0 |
Reading written materials | 52 | Midterm preparation | 27 | Exam preparation | 0 |
10. Department | Department of Control for Transportation and Vehicle Systems | ||||
11. Responsible lecturer | Dr. Gáspár Péter | ||||
12. Lecturers | Dr. Gáspár Péter | ||||
13. Prerequisites | |||||
14. Description of lectures | |||||
Introduction. Recap on the basic concepts of control theory and stability theory (stability conditions, stability of closed loop systems). State space theory (state space representations and properties, transformations). Continuous state space of linear time-variant dynamic systems. Control in state space. State feedback design. Optimal controls. Linear Quadratic Controller Design (LQR). Computer controlled systems. Designing discrete controls. Observability, controllability properties. Stability. State estimation. Kalman filtering. Problems from different means of transport :road, air, logistics. Presentation of design tasks through vehicle, transport and logistic examples. Computer-oriented control theory tasks. Outlook (introductory, problematic). Postmodern techniques. Predictive controls. Error detection and importance in transport. MIMO systems. Nonlinear systems. |
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15. Description of practices | |||||
Implementation of the methods learned during the lectures | |||||
16. Description of labortory practices | |||||
Implementation of the methods learned during the lectures | |||||
17. Learning outcomes | |||||
A. Knowledge
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18. Requirements, way to determine a grade (obtain a signature) | |||||
Two midsemester exams, which are the prerequisite of the midterm grade. The final grade depends on the results of midsemester exams (with 50-50% weight). | |||||
19. Opportunity for repeat/retake and delayed completion | |||||
Both midterm exams can be retried once. | |||||
20. Learning materials | |||||
Lecture Notes, Kailath: Linear Systems, Prentice Hall | |||||
Effective date | 10 October 2019 | This Subject Datasheet is valid for | Inactive courses |