Interconnected Automation Systems (IAS)
At the Chair of Interconnected Automation Systems (IAS) at the University of Siegen, we drive basic and applied research on design automation of software and hardware to improve the reliability, efficiency and sustainability of modern cyber-physical infrastructures - from industrial and mechatronic systems to energy conversion applications and electrical power systems.
Mission statement
We conduct rigorous, open and responsible research on interconnected automation systems and translate sound modeling, control and data-driven methods into trustworthy technologies that increase safety and reliability, reduce energy plus resource consumption and strengthen resilient infrastructures for the benefit of society. In teaching, we qualify engineers and researchers to combine a physical-analytical understanding of systems with computer-aided and data-oriented tools so that they can actively shape future generations of automation and energy systems.
Chair's head
Research profile
We research networked cyber-physical systems in industrial automation and mechatronics as well as in electrified energy technologies. Central fields of application are electric drives, power electronic converters, energy storage systems and charging infrastructures as well as networked electrical energy systems such as microgrids - with the aim of enabling more reliable, efficient and resilient operation under real operating conditions.
Our work covers the entire innovation chain from basic research to industrial transfer. A particular focus is on translating theoretical concepts into practical proof-of-concepts, supported by rapid software and hardware prototyping. Experimental validation, including targeted measurement campaigns on relevant test benches, is an integral part of our research process.
Open science is a cornerstone of our research practice. We publish open source software, reproducible workflows and other open resources to enable transparent evaluation, benchmarking and rapid knowledge transfer for students, researchers and industry partners. Our open source contributions can be found on GitHub: https://github.com/IAS-Uni-Siegen
Focus areas
- Optimal control methods (e.g., reinforcement learning, differential predictive control)
- Hardware design, optimization and testing of power electronic converters (component and system level)
- Hybrid modeling and system identification (combination of expert and data knowledge)
- Condition monitoring, diagnostics and digital twins (e.g. using fault and anomaly detection)
- State and parameter estimation (observer, co-estimator)
- Software-driven automation (reproducible design toolchains, verification and benchmarking)
Latest publications
Pseudolabeling Machine Learning Algorithm for Predictive Maintenance of Relays
Pseudolabeling Machine Learning Algorithm for Predictive Maintenance of Relays
Charger Integrated Coestimation of Parameters and States of Battery
Charger Integrated Coestimation of Parameters and States of Battery
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors in the Whole Speed and Modulation Range Considering Current and Torque Limits
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors in the Whole Speed and Modulation Range Considering Current and Torque Limits
Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives
Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives
Model Predictive Torque Control for Permanent- Magnet Synchronous Motors Using a Stator- Fixed Harmonic Flux Reference Generator in the Entire Modulation Range
Model Predictive Torque Control for Permanent- Magnet Synchronous Motors Using a Stator- Fixed Harmonic Flux Reference Generator in the Entire Modulation Range
Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods
Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints
Unraveling capacity fading in lithium-ion batteries using advanced cyclic tests: A real-world approach
Unraveling capacity fading in lithium-ion batteries using advanced cyclic tests: A real-world approach
Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent-Magnet Synchronous Motors
Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent-Magnet Synchronous Motors
Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes
Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes
Safe Reinforcement Learning-Based Control in Power Electronic Systems
Safe Reinforcement Learning-Based Control in Power Electronic Systems
Decoding Range Variability in Electric Vehicles: Unravelling the Influence of Cell-to-Cell Parameter Variation and Pack Configuration
Decoding Range Variability in Electric Vehicles: Unravelling the Influence of Cell-to-Cell Parameter Variation and Pack Configuration
Opening hours secretariat
Opening hours
Postal address
University of Siegen
Chair of Interconnected Automation Systems (IAS)
Hölderlinstraße 3
57076 Siegen
Visitor address
University of Siegen
Chair of Interconnected Automation Systems (IAS)
H-A Level 4
Room: H-A 4106/3
Hölderlinstraße 3
57076 Siegen
Secretariat
Secretary: Lada Lübke
Phone: +49 (0)271 / 740-3305
Fax: +49 (0)271 / 740-13305
Room: H-A 4106/3
E-Mail: IAS-office@eti.uni-siegen.de