Lectures
Our teaching program addresses conceptual and technical foundations in depth and rigor in the areas of control theory, cyber-physics and machine learning. We target students from engineering and mathematics in attempting to deploy a systematic and conceptual operating thinking as well as a slice of our passion in the field of complex dynamical systems and decision making algorithms and foundations. Last but not least, we demonstrate in the classes, practical lab assignments and exercises their application to modern cross-disciplinary technologies and industries.
Regelungstheorie
Ziel der Vorlesung Regelungstheorie ist eine konzise Einführung in die Theorie der dynamischen Systeme und optimalen Steuerung. Dabei stehen im Vordergrund elementarste und wichtigste konzeptionelle und technische Ausführungen der Lyapunov-Stabilität, Steuerbarkeit und Berechnung von optimalen Steuerungen zeitkontinuierlicher Systeme.
Hybrid and discrete-event Dynamical Systems
The course gives an overview of the basic concepts of modeling and control methods in discrete-event systems and a variety of classes of hybrid dynamical systems involving abstraction and algebraic techniques. Rigorous mathematical foundations of automata theory, Petri nets, hybrid automata, mixed-logical dynamics, impulsive systems, switched systems along with applications in engineering are covered in the course.
Data-driven Control
In this course, we focus on a mix of established and emerging methods that are driving current developments in many directions of control theory. In particular, we will focus on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. Vast numerical and programming demonstrations enrich a mathematically systematic presentation of ideas and techniques.
Indroduction to Autonomous Systems
This course gives an overview of various modules and aspects of autonomous systems. The students would learn different concepts such as perception, object tracking, SLAM, planning and control, which are core components of many autonomous systems. Additionally, the students would have the opportunity to learn how to implement these theoretical concepts in practice, as part of code demos.
Mechatronik
Über die Gliederung auf mehreren Modulen werden verschiedene Aspekte der Mechatronik addressiert. Dabei gilt besonderes Augenmerk gängigen Werkzeugen gehöriger Module. Die Vorlesung richtet sich an Studierende des Maschinenbaus, Elektrotechnik und Informatik und entwickelt eine gemeinsame "Sprache" im Kontext der Systemtheorie.
Machine Learning
The course gives an overview of the basic and mordern concepts, mathematical techniques and algorithms of deep neural networks (DNN), including convolutional neutral networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), as well as reinforcement learning (RL). In addition to rigorous mathematical foundations, the covered methods are illustrated by implementations on applications in various domains, including environment perception, autonomous driving and cancer research.
Maschinendynamik
Es werden die Grundlagen der Modellbildung und Analyse von komplexen mechanischen Strukturen und Systemen vermittelt. Zentrale Rolle spielen die Euler-Lagrange- und Newton-Euler-Gleichungen. Studierende erfahren einen systematischen Aufbau von technischen Konzepten, die allgemeine mathematische Beschrebung von Mehrkörpersystemen inkl. spezielle lineare Systemklassen sowie Methoden zur Lösung bzw. Analyse von Bewegungsgleichungen in Zeit- und Frequenzbereich. Die entwickelten Konzepte und Methoden werden in praktischen Beispielen der Maschinen- und Systemdynamik demonstriert.
Neural Networks in Control and Dynamics
"Neural Networks in Control and Dynamics" (abr. NNCD) develops a rigorous foundation for learning‑based modeling and control of dynamical systems. To this end, the course develops the mathematical foundations of neural ordinary differential equations (neural ODEs), partial differential equations (PDE-informed networks), and recurrent neural architectures including RNNs, LSTMs, and GRUs. Building on these fundamentals, the course culminates with attention-based architectures such as Transformers and their emerging role in modeling dynamics and decision-making.
Fahrdynamikregelung
Behandelt werden die primäre Regelungstechnik, Reifenmodelle mit Schlupf und Kraftschlussausnutzung sowie das Einspurmodell zur Analyse von Unter- und Übersteuern. Zudem geht es um Längs- und Querdynamikregelungen wie ABS, ASR und Einzelradlenkung. Auch moderne Fahrerassistenzsysteme (ADAS) werden thematisiert. Die Inhalte vertiefen die Teilnehmenden durch Übungen in Matlab/Simulink.