InformatiKOM I, Bldg. 50.19, Atrium, Adenauerring 12

20. January 2026, 17:30

Andreas Kugi has been the Scientific Director of the AIT Austrian Institute of Technology, the largest Austrian RTO with more than 1.600 employees, since July 2023 and has been a Professor of Complex Dynamical Systems at the Technische Universität Wien (TU Wien) since June 2007. From 2007 to 2023, he also served as Head of the Institute of Automation and Control Engineering (ACIN) at TU Wien.

He studied electrical engineering at Technische Universität Graz (1986–1992, diploma with distinction) and received his doctorate in 1995 from Johannes Kepler University (JKU) Linz, where he completed his habilitation in control engineering in 2000. After holding a professorship in Systems Theory and Control Engineering at Saarland University (2002–2007), he received offers from TU Dresden and the Karlsruhe Institute of Technology.

His research focuses on the modeling, control, and optimization of complex dynamical systems. In collaboration with more than 40 companies, he has worked on various applications in the automotive industry, robotics, drive technology, and process automation. His goal is to bridge advanced system-theoretical concepts with industrial challenges.

He is the author of over 400 scientific publications, including more than 220 in SCIE-listed journals, and co-inventor of 170 patents in 49 patent families. He has supervised more than 55 doctoral theses as the primary advisor. His work has been recognized with 17 Best Paper Awards, and he has delivered 14 plenary or semi-plenary lectures at international conferences. His honors include the Golden Stefan Honorary Medal of the Austrian Electrotechnical Association (OVE) (2023) and the IFAC Mechatronic Systems Outstanding Investigator Award (2022).

From 2014 to 2021, he led the Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry, and from 2017 to 2023, he headed the Center for Vision, Automation & Control at AIT. He served as Editor-in-Chief of the IFAC Journal Control Engineering Practice (2010–2017) and is currently an Honorary Editor. Additionally, he is a full member of the Austrian Academy of Sciences and a member of the German Academy of Science and Engineering (acatech).

Abstract:

Control engineering has evolved from a discipline focused on system analysis and controller design into a comprehensive science of system design. Beyond mechanical construction, sensors, actuators, control, software, and domain-specific knowledge, data analytics, machine learning, and artificial intelligence (AI) now play a pivotal role. While classical automation relies on hierarchical control loops that follow a sequential sense-analyze-calculate-act process, modern systems systema­tically integrate perception, sensor fusion, learning, and decision-making in a more dynamic and adaptive manner. Advanced optimization and control algorithms enable these systems to adapt to changing environments in real time, systematically handle nonlinear effects, and continuously operate at peak efficiency. This shift also impacts mechatronics and process control, where data-driven approaches, machine learning, and AI are increasingly shaping the evolution toward intelligent, self-optimizing systems.

The first part of the talk addresses robotic systems and presents an online model-predictive planner for Cartesian reference paths in the end-effector’s pose. This planner operates robustly in dynamic environments and under varying task constraints, providing an effective interface between low-level control and high-level reasoning, including integration with large language models. Applications to large-scale autonomous machines, in particular automated pallet loading by outdoor forklifts and log handling by truck-mounted cranes, demonstrate how the tight coupling of perception, planning, and control enables high levels of autonomy in both industrial and natural settings.

The second part focuses on hybrid modeling approaches that combine first-principles and data-driven methods in process control. By fusing multi-modal sensor data with physics-based and machine learning models, it becomes possible to dynamically estimate unmeasurable product properties during manufacturing. This capability forms the foundation for advanced control concepts that adaptively correct deviations in real time and compensate for variations in raw materials or upstream disturbances. Examples from the steel industry illustrate how such approaches can significantly improve process stability, product quality, and energy efficiency.