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,”10.1109/tpel.2023.3303651__IEEE Transactions on Power Electronics, pp. 1–16, 2023, doi:10.1109/tpel.2023.3303651 .

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,”10.1109/iemdc55163.2023.10239018__2023, doi:10.1109/iemdc55163.2023.10239018 .

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,”10.1109/iemdc55163.2023.10239044__2023, doi:10.1109/iemdc55163.2023.10239044 .

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.

G. Book et al., “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments,”10.1109/ojpel.2021.3065877__IEEE Open Journal of Power Electronics, pp. 187–201, 2021, doi:10.1109/ojpel.2021.3065877 .

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,”10.21105/joss.02498__Journal of Open Source Software, Art. no. 2498, 2021, doi:10.21105/joss.02498 .

M. Schenke and O. Wallscheid, “A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors,”10.1109/ojies.2021.3075521__IEEE Open Journal of the Industrial Electronics Society, pp. 388–400, 2021, doi:10.1109/ojies.2021.3075521 .

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.

M. Schenke, W. Kirchgässner, and O. Wallscheid, “Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept,”10.1109/tii.2019.2948387__IEEE Transactions on Industrial Informatics, pp. 4650–4658, 2019, doi:10.1109/tii.2019.2948387 .

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.

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.