Cyber Physical Modeling (CPM)

Lecturers

Prof. Dr.-Ing. Sören Hohmann

Dozent

Prof. Dr.-Ing. Mike Barth

Dozent

M. Sc. Felix Thömmes

Betreuung der Vorlesung

M. Sc. Leonie Schicketanz

Betreuung der Vorlesung

Overview

Contact  If you have any questions concerning the lecture or the exercise, please contact Felix Thömmes or Leonie Schicketanz.
Recommendations

Optimization of Dynamic Systems (ODS)
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.
Teaching Content This course aims at engineering students that focus on a system-based engineering curriculum, including architectures, modeling & simulation for Cyber Physical Systems. The module is designed to teach students the theoretical and practical aspects of Digital Twins and their interconnection with their physical counterpart. It encompasses fundamental topics along the complete process of modeling technical systems. For this purpose, it includes the conception and construction of digital twins including their model components. In terms of modeling and simulation of physical systems, two major areas will be covered: On the one hand, physical-based modeling techniques which derive formal model equations based on analyzing the physical firstprinciples 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. Both modeling areas base on available information about the physical system which is structured in Meta- and Information-Models. Examples that are covered in this lecture are Metamodels, e.g. AutomationML or the asset administration shell principles. Also, semantic web principles and ontologies will be part of the lecture content.
Literature

P. E. Wellstead: Introduction to Physical System Modelling
W. Borutzky: Bond Graph Methodology
A. van der Schaft, D. Jeltsema: Port-Hamiltonian Systems: An Introductory Overview
R. Isermann, M. Münchhof: Identification of dynamic systems : an introduction with applications
Course Material On Ilias all relevant course material (including lecture slides, exercise and tutorial sheets and semester schedule) can be downloaded
Workload

1. Workload attendance in lectures an exercise: 3+1 SWS (60 h) 

2. Pre-/Postprocessing of the lecture (90 h) 

3. Preparation of and attendance in the exam: (30 h) 

 

A total of 180 h = 6 CR
Goals
  • The students are familiar with the concepts of Cyber-Physical System.
  • Students understand the need for advanced methods and services in the field of automation. 
  • Students can validate different information models and ontologies for their applicability in CPS. 
  • Students will be able to model data, information and knowledge or extract them from existing systems. 
  • The students know suitable modeling tools and their application. 
  • The students understand the general model concept as well as the characteristics of physical and data-based modeling and can describe their differences. 
  • They can structure complex systems and systematically analyze dependencies of subsystems. 
  • They can explain the general procedure of physical and data-based modeling, apply it to technical systems, and analyze the results. 
  • They can apply causal and non-causal modeling approaches and distinguish between them. 
  • Students have gained an understanding of generalized, cross-domain, physical relationships and can develop models for electrical, mechanical, pneumatic and hydraulic systems. 
  • They can describe the relationship between generalized, cross-domain, physical models and basic procedures of physical-based control and explain their advantages / limitations based on basic knowledge of control engineering. 
  • The students can estimate and judge the effects of disturbances and real conditions on the identification results
Exam

exam  SS25 on Thursday, 25th September 2025 from 11:00 - 12:30  Hörsaal am Fasanengarten
Evaluation