Physical and Data-Based Modeling (PDM)
- Typ: Lecture + Exercise
- Lehrstuhl: IRS
- Semester: ST 22
Wednesday, 11:30 – 13:00 at Messtechnik, MTI (30.33)
Thursday, 14:00 – 15:30 at Fritz-Haller-Hörsaal (20.40)
Additionally live stream via Zoom:
Meeting-ID: 675 5281 0114
The first lecture will take place on 20.04.2022.
- SWS: 2 + 1
- ECTS: 6
- LVNr.: 2303166
- Prüfung: Oral exam
This lecture is held in English
|Contact||If you have any questions concerning the lecture or the exercise, please contact Armin Gießler|
In contrast to the former “Modellbildung und Identifikation”, this course requires a profound knowledge in multivariable systems and optimization. Thus, attendance of the lecture Optimization of Dynamic Systems (ODS) is an absolute precondition to appropriately follow the course! Prior knowledge about (linear) state space representations and realizations, the concept of “zeros” in the state space, and observability is highly recommended (see e.g. Regelung linearer Mehrgrößensysteme (RLM))!
Furthermore, sound understanding of Higher Mathematics I-III, linear electrical network theory and engineering mechanics / physics is required to successfully attend the lecture, exercise tasks / case studies, and exam.
This course aims at engineering students that focus on a systemic and control engineering curriculum. It encompasses fundamental topics along the complete process of modeling technical systems. Particularly, two major areas will be covered:
On the one hand, physical-based modeling techniques which derive formal model equations based on analyzing the physical first-principles of technical systems. This includes, inter alia, generalized equivalent circuits, bond graphs, port-Hamiltonian systems, variational analysis (Euler-Lagrange of the first kind). Selected topics of physical-based control methods will also be briefly introduced to integrate the complete physical control design in the wider control context and highlight its possible benefits.On the other hand, data-based identification techniques will be covered which are used to identify concrete model parameters for a given technical system from experimental data sets. When combining the identification with an initial, non-physical, structural set up of model equations, the complete process is often referred to as data-based modeling or black-box modeling.
P. E. Wellstead: Introduction to Physical System Modelling
|Course Material||On Ilias all relevant course material (including lecture slides, exercise and tutorial sheets and semester schedule) can be downloaded|
Attendance time in lecture/exercise: 60h
Preparation and revision of course content: 75hPreparation and attendance of the oral exam: 30h
After finishing this lecture, the students
Oral examination of approximately 20 minutes. The grades will be announced directly after the exam. In order to arrange exam appointments can be arranged at the secretary (Ms. Stassen). Beforehand, an online registration via CAS is mandatory!