Studies of electrical engineering and information technology at the Karlsruhe Institute of Technology (KIT). Bachelor’s thesis at the Institute of Control Systems (IRS) on load forecasting in electricity distribution grids with high renewable energy penetration (2016). Following this, master’s studies with focus on control engineering and systems theory. Internship at the Autonomous Driving Campus of BMW in Unterschleißheim in the field of simulative validation of highly automated driving functions. Master’s thesis at the IRS on exploration strategies for cooperative controllers based on reinforcement learning methods (2019).
Since July 2019, member of the scientific staff of the Institute of Control Systems.
Modeling and Identification in Cooperative Human-Machine Scenarios
In almost all industrial sectors and even in the everyday life the amount of automated tasks increases. This leads to significant changes in the working and everyday environment. However, since the automation mainly concerns tasks and not complete jobs, humans more and more have to interact with highly automated systems. Examples can be found in the field of medical engineering, manufacturing engineering (fenceless factory) or automotive engineering.
In such situations, the human and the automation influence a technical system mutually and equally. In order to design the automation in such a cooperative human-machine scenario properly, a description resp. model of the human behavior is an important first step. Then, based on this model an identification algorithm will be needed to determine the crucial parameters, which describe how the human behaves. Designing the automation based on such an identified model provides the opportunity to get the most of the synergies of the cooperation.
Within the research project, describing the human as an optimal controller acts as a promising modeling approach. Thereby, the variability of human movements leads to additional challenges. In context of the research project, this variability is to be integrated and analyzed in the identification algorithms. A promising starting point is given by methods relating to Inverse Reinforcement Learning.
|Inverse dynamische Spiele basierend auf Methoden des Adaptive Dynamic Programming||Master Thesis|
|Title||Type||Person in Charge|
|Path Integral Inverse Reinforcement Learning||Master Thesis|
|Simulationsumgebung für die optimale Regelung von Robotersystemen||Bachelor Thesis|
Belgardt, S.; Doer, C.; Hohmann, S.; Karg, P.; Rothfuß, S.; Siebenrock, F.; Stork, W.; Terzidis, O.; Tittel, A.; Zwick, T.
2021. Handbuch Qualität in Studium, Lehre und Forschung, 76, 67–84
Stark, O.; Karg, P.; Hohmann, S.
2020. 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea (South), 14-18 Dec. 2020, 5159–5166, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CDC42340.2020.9304346
Flad, M.; Karg, P.; Roitberg, A.; Martin, M.; Mazewitsch, M.; Lange, C.; Kenar, E.; Ahrens, L.; Flecken, B.; Kalb, L.; Karakaya, B.; Ludwig, J.; Pruksch, A.; Stiefelhagen, R.; Hohmann, S.
2020. Smart Automotive Mobility: Reliable Technology for the Mobile Human. Ed.: Gerrit Meixner, 1–70, Springer
Flad, M.; Ludwig, J.; Roitberg, A.; Karg, P.; Stiefelhagen, R.; Hohmann, S.
2019. Technische Informationsbibliothek (TIB)
Korus, J.-D.; Karg, P.; Ramos, P. G.; Schütz, C.; Zimmermann, M.; Müller, S.
2019. IFAC-PapersOnLine, 52 (8), 1–6. doi:10.1016/j.ifacol.2019.08.026
Sauter, P. S.; Karg, P.; Kluwe, M.; Hohmann, S.
2018. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018; Sarajevo; Bosnia and Herzegovina; 21 October 2018 through 25 October 2018, Art. Nr.: 8571524, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISGTEurope.2018.8571524
Sauter, P.; Karg, P.; Pfeifer, M.; Kluwe, M.; Zimmerlin, M.; Leibfried, T.; Hohmann, S.
2017. Die Energiewende : Blueprints for the New Energy Age, Proceedings of the International ETG Congress 2017, World Conference Center, Bonn, 28th - 29th November 2017, 13–18, VDE Verlag