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
Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges
Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges
Formulation of Stray Loss in Medium Power Inverter-fed Induction Motors
Formulation of Stray Loss in Medium Power Inverter-fed Induction Motors
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
Smart Charging: An Outlook Towards its Role and Impacts, Enablers, Markets, and the Global Energy System
Smart Charging: An Outlook Towards its Role and Impacts, Enablers, Markets, and the Global Energy System
Improved exploring starts by kernel density estimation-based state-space coverage acceleration in reinforcement learning
Improved exploring starts by kernel density estimation-based state-space coverage acceleration in reinforcement learning
Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation
Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation
Application of Fuzzy Logic in the Operation of a V2G System in the Smart Grid
Application of Fuzzy Logic in the Operation of a V2G System in the Smart Grid
gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems
gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems
Permanent magnet synchronous machine temperature estimation using low-order lumped-parameter thermal network with extended iron loss model
Permanent magnet synchronous machine temperature estimation using low-order lumped-parameter thermal network with extended iron loss model
Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor
Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor
A Fuzzy Logic and Artificial Neural Network-Based Intelligent Controller for a Vehicle-to-Grid System
A Fuzzy Logic and Artificial Neural Network-Based Intelligent Controller for a Vehicle-to-Grid System
Data Set Description: Identifying the Physics Behind an Electric Motor--Data-Driven Learning of the Electrical Behavior (Part II)
Data Set Description: Identifying the Physics Behind an Electric Motor--Data-Driven Learning of the Electrical Behavior (Part II)
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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