Maximilian Schenke
Leistungselektronik und Elektrische Antriebstechnik (LEA)
schenke@lea.uni-paderborn.deID
11 Publications
2023 | Journal Article | LibreCat-ID: 46863
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
| DOI
2023 | Conference Paper | LibreCat-ID: 46865
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 & Drives Conference (IEMDC). https://doi.org/10.1109/iemdc55163.2023.10239018
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 46864
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 & Drives Conference (IEMDC). https://doi.org/10.1109/iemdc55163.2023.10239044
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 40212
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
2021 | Journal Article | LibreCat-ID: 22162
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
| DOI
2021 | Journal Article | LibreCat-ID: 21254
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
| DOI
2021 | Journal Article | LibreCat-ID: 25031
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
| DOI
2021 | Journal Article | LibreCat-ID: 29662
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
2019 | Journal Article | LibreCat-ID: 25030
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
| DOI
2018 | Conference Paper | LibreCat-ID: 29628
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
2018 | Conference Paper | LibreCat-ID: 29625
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
11 Publications
2023 | Journal Article | LibreCat-ID: 46863
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
| DOI
2023 | Conference Paper | LibreCat-ID: 46865
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 & Drives Conference (IEMDC). https://doi.org/10.1109/iemdc55163.2023.10239018
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 46864
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 & Drives Conference (IEMDC). https://doi.org/10.1109/iemdc55163.2023.10239044
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 40212
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
2021 | Journal Article | LibreCat-ID: 22162
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
| DOI
2021 | Journal Article | LibreCat-ID: 21254
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
| DOI
2021 | Journal Article | LibreCat-ID: 25031
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
| DOI
2021 | Journal Article | LibreCat-ID: 29662
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
2019 | Journal Article | LibreCat-ID: 25030
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
| DOI
2018 | Conference Paper | LibreCat-ID: 29628
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
2018 | Conference Paper | LibreCat-ID: 29625
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