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

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

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

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

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

Balakrishna, P., Book, G., Kirchgässner, W., Schenke, M., Traue, A., &; Wallscheid, O. (2021). gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems. Journal of Open Source Software, Article 2498.https://doi.org/10.21105/joss.02498 
LibreCat: /record/21254
DOI: 10.21105/joss.02498

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

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

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

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

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