Meet the Jury Alisa Rupenyan-Vasileva on Flexible automation for robot-based manufacturing

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 Arenberg Doctoral School is proud to invite you to Meet The Jury!

When internationally renowned experts visit KU Leuven as a member of a PhD Examination Committee, we like to seize this opportunity to give this expert a forum to a large audience. All members of Science, Engineering & Technology are most welcome to the Meet The Jury Lectures.

Prof. Alisa Rupenyan-Vasileva (ZHAW School of Engineering, Switzerland) will give a lecture on “Flexible automation for robot-based manufacturing”.

Abstract:
Data and intelligent algorithm integration in the control and operations of industrial equipment opens new opportunities for improved efficiency and sustainability across manufacturing domains. While the repetitive nature of manufacturing tasks is beneficial, a major challenge in achieving flexible automation is to accomodate variability introduced by different systems, components, and operating conditions without requiring manual re-tuning of process parameters. This talk presents frameworks that leverage data from both physical systems and multi-fidelity digital twins to enhance system autonomy and operational flexibility. We introduce a meta-learning approach for control that systematically handles system variability by learning from diverse operational data and simulation sources. This framework enables control algorithms to adapt quickly to new conditions while maintaining safety and performance. We also present a multi-fidelity digital twin-based framework for data-driven optimization using Bayesian optimization that maintains the connection between the digital twin and the real system and adjusts for changes in the system, and we discuss adaptive control approaches for repetitive tasks. The proposed frameworks are validated on several complementary applications: a ball-on-a-plate system; iterative robot winding system, high-precision robotic drive; and finally, implementation on industrial delta robots where digital twins, meta-learning, and iterative learning control work together to achieve robust performance. The results show how systematic integration of physical and simulated data sources can create manufacturing systems that maintain high performance and adapt to the inherent variability in real industrial environments, reducing the need for interventions and system or operation-specific tuning.

Biography:
Prof. Alisa Rupenyan is the Rieter endowed chair in Industrial AI from the Rieter foundation at the ZHAW Centre for AI, Zurich University of Applied Sciences, and her research is focused on in continuous optimization and automation of manufacturing processes and motion systems. Previously, she was group leader for Automation at Inspire (a Swiss institute for research and technology transfer partnering with ETH Zurich) and senior scientist at the Automatic Control Laboratory at ETH Zurich. Her research interests include autonomous machines, decision-making in industrial settings, and process control. She is technical committee member at IEEE-CSS and IEEE-IES, and executive member at the IFAC Industry committee, and associated editor for Control Engineering Practice.)

Following the lecture, there will be opportunity for young researchers to interact with her.

• Venue: ELEC Aula R (ESAT, Electrical Engineering Department, Kasteelpark Arenberg 10 , 3001 Heverlee)
• Date: 9 September 2025 – 15:00-16:00
• Prof. Alisa Rupenyan-Vasileva is visiting KU Leuven on the occasion of the PhD defence of Jean Pierre Allamaa

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