Finite-Set Direct Torque Control via Edge Computing-Assisted Safe Reinforcement Learning for a Permanent Magnet Synchronous Motor
M. Schenke, B. Haucke-Korber, O. Wallscheid, IEEE Transactions on Power Electronics (2023) 1–16.
LibreCat: /record/46863
DOI: 10.1109/tpel.2023.3303651

Deep Q Direct Torque Control with a Reduced Control Set Towards Six-Step Operation of Permanent Magnet Synchronous Motors
B. Haucke-Korber, M. Schenke, O. Wallscheid, in: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC), IEEE, 2023.
LibreCat: /record/46865
DOI: 10.1109/iemdc55163.2023.10239018

Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives
F. Book, A. Traue, M. Schenke, B. Haucke-Korber, O. Wallscheid, in: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC), IEEE, 2023.
LibreCat: /record/46864
DOI: 10.1109/iemdc55163.2023.10239044

Reinforcement Learning-Based Deep Q Direct Torque Control with Adaptable Switching Frequency Towards Six-Step Operation of Permanent Magnet Synchronous Motors
B. Haucke-Korber, M. Schenke, O. Wallscheid, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.
LibreCat: /record/40212

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments
G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021) 187–201.
LibreCat: /record/22162
DOI: 10.1109/ojpel.2021.3065877

gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems
P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid, Journal of Open Source Software (2021).
LibreCat: /record/21254
DOI: 10.21105/joss.02498

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors
M. Schenke, O. Wallscheid, IEEE Open Journal of the Industrial Electronics Society (2021) 388–400.
LibreCat: /record/25031
DOI: 10.1109/ojies.2021.3075521

Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning
M. Schenke, O. Wallscheid, ArXiv Preprint ArXiv:2105.08990 (2021).
LibreCat: /record/29662

Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept
M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial Informatics (2019) 4650–4658.
LibreCat: /record/25030
DOI: 10.1109/tii.2019.2948387

Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines
O. Wallscheid, M. Schenke, J. Böcker, in: 2018 21st International Conference on Electrical Machines and Systems (ICEMS), 2018, pp. 1181–1186.
LibreCat: /record/29628

A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation
O. Wallscheid, M. Schenke, J. Böcker, in: 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), 2018, pp. 793–799.
LibreCat: /record/29625