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
Deep residual convolutional and recurrent neural networks for temperature estimation in permanent magnet synchronous motors
Deep residual convolutional and recurrent neural networks for temperature estimation in permanent magnet synchronous motors
Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter
Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter
Energy Management for a Nano-CHP Unit and an Electrical Storage System in a Residential Application
Energy Management for a Nano-CHP Unit and an Electrical Storage System in a Residential Application
Koopman operator based finite-set model predictive control for electrical drives
Koopman operator based finite-set model predictive control for electrical drives
Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines
Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines
Energy Management for a Nano-Cl+PUnit and an Electrical Storage System in a Residential Application
Energy Management for a Nano-Cl+PUnit and an Electrical Storage System in a Residential Application
A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation
A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation
Lifetime Extension of Photovoltaic Modules by Influencing the Module Temperature Using Phase Change Material
Lifetime Extension of Photovoltaic Modules by Influencing the Module Temperature Using Phase Change Material
Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
Ein Beitrag zur thermischen Ausnutzung permanenterregter Synchronmotoren in automobilen Traktionsanwendungen
Ein Beitrag zur thermischen Ausnutzung permanenterregter Synchronmotoren in automobilen Traktionsanwendungen
Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors
Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors
A direct model predictive torque control approach to meet torque and loss objectives simultaneously in permanent magnet synchronous motor applications
A direct model predictive torque control approach to meet torque and loss objectives simultaneously in permanent magnet synchronous motor applications
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