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11 Publications
2023 | Journal Article | LibreCat-ID: 46863
M. Schenke, B. Haucke-Korber, and O. Wallscheid, “Finite-Set Direct Torque Control via Edge Computing-Assisted Safe Reinforcement Learning for a Permanent Magnet Synchronous Motor,” IEEE Transactions on Power Electronics, pp. 1–16, 2023, doi: 10.1109/tpel.2023.3303651.
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| DOI
2023 | Conference Paper | LibreCat-ID: 46865
B. Haucke-Korber, M. Schenke, and O. Wallscheid, “Deep Q Direct Torque Control with a Reduced Control Set Towards Six-Step Operation of Permanent Magnet Synchronous Motors,” 2023, doi: 10.1109/iemdc55163.2023.10239018.
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| DOI
2023 | Conference Paper | LibreCat-ID: 46864
F. Book, A. Traue, M. Schenke, B. Haucke-Korber, and O. Wallscheid, “Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives,” 2023, doi: 10.1109/iemdc55163.2023.10239044.
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| DOI
2022 | Conference Paper | LibreCat-ID: 40212
B. Haucke-Korber, M. Schenke, and O. Wallscheid, “Reinforcement Learning-Based Deep Q Direct Torque Control with Adaptable Switching Frequency Towards Six-Step Operation of Permanent Magnet Synchronous Motors,” in IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.
LibreCat
2021 | Journal Article | LibreCat-ID: 22162
G. Book et al., “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments,” IEEE Open Journal of Power Electronics, pp. 187–201, 2021, doi: 10.1109/ojpel.2021.3065877.
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| DOI
2021 | Journal Article | LibreCat-ID: 21254
P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, and O. Wallscheid, “gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems,” Journal of Open Source Software, Art. no. 2498, 2021, doi: 10.21105/joss.02498.
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| DOI
2021 | Journal Article | LibreCat-ID: 25031
M. Schenke and O. Wallscheid, “A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors,” IEEE Open Journal of the Industrial Electronics Society, pp. 388–400, 2021, doi: 10.1109/ojies.2021.3075521.
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| DOI
2021 | Journal Article | LibreCat-ID: 29662
M. Schenke and O. Wallscheid, “Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning,” arXiv preprint arXiv:2105.08990, 2021.
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2019 | Journal Article | LibreCat-ID: 25030
M. Schenke, W. Kirchgässner, and O. Wallscheid, “Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept,” IEEE Transactions on Industrial Informatics, pp. 4650–4658, 2019, doi: 10.1109/tii.2019.2948387.
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| DOI
2018 | Conference Paper | LibreCat-ID: 29628
O. Wallscheid, M. Schenke, and J. Böcker, “Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines,” in 2018 21st International Conference on Electrical Machines and Systems (ICEMS), 2018, pp. 1181–1186.
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2018 | Conference Paper | LibreCat-ID: 29625
O. Wallscheid, M. Schenke, and J. Böcker, “A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation,” in 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), 2018, pp. 793–799.
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