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

Haucke-Korber B, Schenke M, Wallscheid O. Deep Q Direct Torque Control with a Reduced Control Set Towards Six-Step Operation of Permanent Magnet Synchronous Motors. In: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC). IEEE; 2023. doi: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. Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives. In: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC). IEEE; 2023. doi:10.1109/iemdc55163.2023.10239044
LibreCat: /record/46864
DOI: 10.1109/iemdc55163.2023.10239044

Haucke-Korber B, Schenke M, Wallscheid O. 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. ; 2022:1-6.
LibreCat: /record/40212

Book G, Traue A, Balakrishna P, et al. Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments. IEEE Open Journal of Power Electronics. Published online 2021:187-201. doi: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. gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems. Journal of Open Source Software. Published online 2021. doi:10.21105/joss.02498
LibreCat: /record/21254
DOI: 10.21105/joss.02498

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

Schenke M, Wallscheid O. Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. arXiv preprint arXiv:210508990. Published online 2021.
LibreCat: /record/29662

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

Wallscheid O, Schenke M, Böcker J. 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). ; 2018:1181–1186.
LibreCat: /record/29628

Wallscheid O, Schenke M, Böcker J. 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). ; 2018:793–799.
LibreCat: /record/29625