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
Evaluation of the severity and self-management practice in primary dysmenorrhea in medical and dental students: A cross-sectional study in a teaching hospital
Evaluation of the severity and self-management practice in primary dysmenorrhea in medical and dental students: A cross-sectional study in a teaching hospital
Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors
Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors
Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks
Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks
Accessibility of Electric Vehicle in Indian Market
Accessibility of Electric Vehicle in Indian Market
Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments
Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments
Formulation of stray loss in medium power inverter-fed induction motors
Formulation of stray loss in medium power inverter-fed induction motors
Gray-Box Loss Model for Induction Motor Drives
Gray-Box Loss Model for Induction Motor Drives
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark
An Insight into the Battery Degradation for a Proposal of a Battery Friendly Charging Technique
An Insight into the Battery Degradation for a Proposal of a Battery Friendly Charging Technique
Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments
Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments
Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach
Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach
A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors
A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors
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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