Curriculum Vitae

Thomas Rudolf studied electrical engineering and information technology at the Karlsruhe Institut of Technology (KIT) and Chalmers University of Technology in Gothenburg, Sweden. During his studies in the specialized fields of information and automation he focused on control engineering, signal processing and system optimization. He concluded his studies with his master thesis about the design of a robust position control and a differential flatness-based velocity control for a four-wheel drive, four-wheel steering electrical vehicle („Entwurf einer robusten Positionsregelung mit kaskadierter flachheitsbasierter Geschwindigkeitsregelung eines radselektiv angetriebenen Elektrofahrzeugs“) at the Institute for Control Systems (IRS).

Since January 2018, he has been employed as a research scientist at the FZI Research Center for Information Technology in the division of Embedded Systems and Sensors Engineering (ESS).

Research

Teaching

Open Theses
Title	Type
Systemidentifikation und Parametrierung mit Reinforcement Learning	Master Thesis

Current Theses
Title	Type	Person in Charge
Entwurf eines Reinforcement-Learning-Verfahrens zur Verhaltensplanung im hochautomatisierten Fahren	Master Thesis	Mingxiao Gao

Publications

2021

Automated Anomaly Detection in Data Streams on Engine Test Benches Using Multi-Scale Convolutional Recurrent Encoder-Decoder.
Juhl, J.; Rudolf, T.; Lukas, K.; Schwab, S.; Dummer, G.; Hohmann, S.; Stork, W.
2021. Proceedings of the International Vienna Motor Symposium 2021. Hrsg.: B. Geringer, Österreichischer Verein für Kraftfahrzeugtechnik (ÖVK)

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications.
Rudolf, T.; Schurmann, T.; Schwab, S.; Hohmann, S.
2021. Proceedings of the IEEE. doi:10.1109/JPROC.2021.3055136

2020

Robust Position and Velocity Tracking Control of a Four-wheel Drive and Four-wheel Steered Electric Vehicle.
Schwartz, M.; Rudolf, T.; Hohmann, S.
2020. 2020 6th International Conference on Control, Automation and Robotics (ICCAR) : April 20-23, 2020, Singapore, 415–422, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICCAR49639.2020.9108078

2019

Regenerative Breaking Strategies for Electric Heavy Duty Trucks.
Rudolf, T.; Bischof, F.; Schwab, S.; Hohmann, S.
2019. 6th SINO-EU Doctoral School for Sustainability Engineering (SESE 2019), Lisbon, Portugal, July 22–24, 2019