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
Insights and Challenges of Co-Simulation-Based Optimal Pulse Pattern Evaluation for Electric Drives
Insights and Challenges of Co-Simulation-Based Optimal Pulse Pattern Evaluation for Electric Drives
Design of a Calorimetric Measurement System for Determination of the Power Losses in Magnetic Components
Design of a Calorimetric Measurement System for Determination of the Power Losses in Magnetic Components
Power Flow Control Strategy Based on Space Vector Modulation of a Three-Port Converter
Power Flow Control Strategy Based on Space Vector Modulation of a Three-Port Converter
Stray Loss Formulation for Inverter-Driven Induction Motors for a Wide Range of Switching Frequency and Motor's Loading
Stray Loss Formulation for Inverter-Driven Induction Motors for a Wide Range of Switching Frequency and Motor's Loading
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors in the Whole Speed and Modulation Range Considering Current and Torque Constraints
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors in the Whole Speed and Modulation Range 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 Direct Torque Control via Edge-Computing-Assisted Safe Reinforcement Learning for a Permanent-Magnet Synchronous Motor
Finite-Set Direct Torque Control via Edge-Computing-Assisted Safe Reinforcement Learning for a Permanent-Magnet Synchronous Motor
Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines
Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines
Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems
Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems
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
Fade-Over Strategy for use of Model Predictive Direct Self-Control with Field-Oriented Control
Fade-Over Strategy for use of Model Predictive Direct Self-Control with Field-Oriented Control
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
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