Notes on the module handbook of the department Mechanical and Process Engineering
- BSc. Bio- und Chemieingenieurwissenschaften (Stand WS 20/21): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MH_BSc_BCI.pdf
- BEd. Lehramt Metalltechnik (Stand WS 19/20): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MHB_Bachelor_Lehramt_Metalltechnik.pdf
- MSc. Bio- und Chemieingenieurwissenschaften (Stand WS 20/21): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MH_Msc_BCI.pdf
- MEd. Lehramt Metalltechnik Werkstoffe und Fertigung (Stand WS 19/20): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MHB_Master_Lehramt_Metalltechnik_-_Werkstoffe_und_Fertigung.pdf
- MEd. Lehramt Metalltechnik Maschinen- und Fahrzeugtechnik (Stand WS 19/20): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MHB_Master_Lehramt_Metalltechnik_-_Fahrzeugtechnik.pdf
- MEd. Lehramt Metalltechnik Verfahrenstechnik (Stand WS 19/20): https://www.mv.uni-kl.de/fileadmin/mv/Studium_Lehre/Modulhandbuecher/MHB_Master_Lehramt_Metalltechnik_-_Verfahrenstechnik.pdf
Machatronics (2V+2U, 5.0 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|-||K||Lecture with exercise classes (V/U)||5.0 CP||94 h|
|2||U||Lecture hall exercise class||28 h|
|(2V+2U)||5.0 CP||56 h||94 h|
- Signals and systems: Signal spaces; Discrete-time and continuous-time signals; Sampling and quantization.
- Control: Systems and Models; Control loops; Control and regulation; Model predictive control.
- Filtering: Deterministic and stochastic processes; Discrete / continuous Kalman filtering.
- Event-driven systems: Language models; Automata and Petri nets; Hybrid systems.
- Decision Theory: Bellman Equation; Q-learning.
- Information Theory: Entropy; Channel models; Coding.
Competencies / intended learning achievements
At the border area between digital technology and mechanical engineering, modern mechatronics only takes place on higher abstraction levels of a regulated mechanical system. System theory forms the basis of mechatronics. In Industry 4.0, a further mutation of mechatronics is emerging, referred to as "cyber-physics." Besides, technical cognition and learning systems are increasingly moving into the center of industrial attention. Mechatronics has thus developed into a technical "discipline of intelligent systems." The lecture is aimed at students of mechanical engineering, electrical engineering, and computer science. It develops and teaches them over five modules ("Signals and Systems", "Filtering", "Control", "Event-Controlled Systems", "Machine Learning" or "Information Theory") to develop a common "language" in the context of algorithms and systems theory.
The students will be able to:
- acquire elementary principles of signal theory and differentiate between different classes of signals, in-depth knowledge of filtering and algorithms, especially Kalman filters, and use them in applications.
- understand industry-relevant concepts and algorithms of higher control engineering such as feedforward control, control loop structure with two degrees of freedom, model predictive control (MPC), and Iterative learning control (ILC), and apply them in practical examples.
- model and analyze automation technology processes formally via Automata, Petri networks, and simple hybrid systems and design them using Supervisory Control Theory.
- acquire basic concepts of autonomous learning systems, especially reinforcement learning, or optionally understand fundamental concepts of information theory and algorithmics.
Finally, the students visit the test vehicles, experimental environments at the chair and access the ongoing developments and results achieved in the research projects in autonomous driving, cooperative robotics, drones, and Industry 4.0.
In the exercise, the knowledge imparted in the lecture is deepened based on specific tasks so that the students will be able to:
- develop practical Kalman filters for robotics and vehicle technology applications, program in Matlab / Simulink, and examine them for robustness and performance.
- try out various control algorithms, in particular PID, structure with two degrees of freedom, model predictive control, and iterative learning control using practical examples (in particular robotics and vehicle technology) and implement them in Matlab / Simulink.
- model discrete event systems using Automata and Petri nets and to investigate their properties using practical examples from robotics
- develop and implement simple learning algorithms in the context of reinforcement learning, especially in the context of the megatrend "autonomous driving".
- N. Bajcinca: “Mechatronik”, Skriptum (SS 2018), TU Kaiserslautern.
- Bishop, R.H. (Edt.): “Mechatronics: An Introduction”. Taylor and Francis, 2006.
- Lunze, J.: “Ereignisdiskrete Systeme ”. Oldenburg Wissenschaftsverlag, 2006.
Requirements for attendance (informal)
Requirements for attendance (formal)
References to Course [MV-MEC-86675-K-4]
|[MV-MEC-229-M-4]||Mechatronics||P: Obligatory||2V+2U, 5.0 LP|