Peitz, S., Stenner, J., Chidananda, V., Wallscheid, O., Brunton, S. L., &; Taira, K. (2024). Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena, 461, 134096.https://doi.org/10.1016/j.physd.2024.134096 
LibreCat: /record/40171
DOI: 10.1016/j.physd.2024.134096

Hölsch, L., Brosch, A., Steckel, R., Braun, T., Wendel, S., Böcker, J., &; Wallscheid, O. (2024). Insights and Challenges of Co-Simulation-based Optimal Pulse Pattern Evaluation for Electric Drives. IEEE Transactions on Energy Conversion, 1–12.https://doi.org/10.1109/tec.2024.3374962 
LibreCat: /record/53309
DOI: 10.1109/tec.2024.3374962

Brosch, A., Wallscheid, O., &; Böcker, J. (2023). Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints. IEEE Transactions on Power Electronics, 1–14.https://doi.org/10.1109/tpel.2023.3265705 
LibreCat: /record/43456
DOI: 10.1109/tpel.2023.3265705

Brosch, A., Tinazzi, F., Wallscheid, O., Zigliotto, M., &; Böcker, J. (2023). Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent Magnet Synchronous Motors. IEEE Transactions on Power Electronics.https://doi.org/10.1109/tpel.2023.3294557 
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DOI: 10.1109/tpel.2023.3294557

Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., &; Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616.https://doi.org/10.21105/joss.05616 
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DOI: 10.21105/joss.05616

Winkel, F., Wallscheid, O., Scholz, P., &; Böcker, J. (2023). Pseudo-Labeling Machine Learning Algorithm for Predictive Maintenance of Relays. IEEE Open Journal of the Industrial Electronics Society, 1–14.https://doi.org/10.1109/ojies.2023.3323870 
LibreCat: /record/48059
DOI: 10.1109/ojies.2023.3323870

Pena, M., Meyer, M., Wallscheid, O., &; Böcker, J. (2023). Fade-Over Strategy for use of Model Predictive Direct Self-Control with Field-Oriented Control. 2023 IEEE International Electric Machines and Drives Conference (IEMDC). 2023 IEEE International Electric Machines and Drives Conference (IEMDC), San Francisco.https://doi.org/10.1109/iemdc55163.2023.10239056 
LibreCat: /record/48093
DOI: 10.1109/iemdc55163.2023.10239056

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
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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
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Pena, M., Meyer, M., Wallscheid, O., &; Böcker, J. (2023). Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines. IEEE Transactions on Power Electronics, 38(10), 12416–12429.https://doi.org/10.1109/tpel.2023.3286713 
LibreCat: /record/48092
DOI: 10.1109/tpel.2023.3286713

Gedlu, E. G., Wallscheid, O., Böcker, J., &; Nelles, O. (2023). Online system identification and excitation for thermal monitoring of electric machines using machine learning and model predictive control. 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED).https://doi.org/10.1109/sdemped54949.2023.10271427 
LibreCat: /record/53310
DOI: 10.1109/sdemped54949.2023.10271427

Winkel, F., Scholz, P., Wallscheid, O., &; Böcker, J. (2023). Reducing Contact Bouncing of a Relay by Optimizing the Switch Signal During Run-Time. IEEE Transactions on Automation Science and Engineering, 1–11.https://doi.org/10.1109/tase.2023.3322762 
LibreCat: /record/53543
DOI: 10.1109/tase.2023.3322762

Förster, N., Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (n.d.). An Open-Source Transistor Database and Toolbox as an Unified Software Engineering Tool for Managing and Evaluating Power Transistors. Proc. 37th IEEE Applied Power Electronics Conference (APEC). 37th IEEE Applied Power Electronics Conference (APEC 22), Houston, TX, USA.
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Brosch, A., Wallscheid, O., &; Böcker, J. (2022). Model Predictive Torque Control for Permanent Magnet Synchronous Motors Using a Stator-Fixed Harmonic Flux Reference Generator in the Entire Modulation Range. IEEE Transactions on Power Electronics.https://doi.org/10.1109/tpel.2022.3229619 
LibreCat: /record/34533
DOI: 10.1109/tpel.2022.3229619

Brosch, A., Wallscheid, O., &; Böcker, J. (2022). Long-Term Memory Recursive Least Squares Online Identification of Highly Utilized Permanent Magnet Synchronous Motors for Finite-Control-Set Model Predictive Control. IEEE Transactions on Power Electronics.https://doi.org/10.1109/tpel.2022.3206598 
LibreCat: /record/33459
DOI: 10.1109/tpel.2022.3206598

Lange, J., Schmies, D., Stille, K. S. C., Böcker, J., &; Wallscheid, O. (2022). Experimental Comparison of FPGA-Implemented Model Predictive Voltage Control to Cascaded Proportional Resonant Control for a Three-Phase Four-Wire Three-Level Grid-Forming Inverter of 250 kVA. EPE’22 ECCE Europe. EPE’22 ECCE Europe, Hannover.
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Stender, M., Becker, M., Wallscheid, O., &; Böcker, J. (2022). Adaptive Operating Strategy for Induction Motors Under Changing Electrical-Thermal Conditions. 48th Annual Conference of the Industrial Electronics Society (IECON). 48th Annual Conference of the Industrial Electronics Society IECON 2022 Conference, Brüssel.https://doi.org/10.1109/IECON49645.2022.9968695 
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Förster, N., Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). An Open-Source Transistor Database and Toolbox as a Unified Software Engineering Tool for Managing and Evaluating Power Transistors. 2022 IEEE Applied Power Electronics Conference and Exposition (APEC).https://doi.org/10.1109/apec43599.2022.9773701 
LibreCat: /record/35125
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Förster, N., Piepenbrock, T., Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). An Open-Source FEM Magnetics Toolbox for Power Electronic Magnetic Components. PCIM Europe 2022; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, 1–10.https://doi.org/10.30420/565822103 
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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.
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Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2022). Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning. Engineering Applications of Artificial Intelligence, 117, Article 105537.https://doi.org/10.1016/j.engappai.2022.105537 
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Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2022). Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations. 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia).https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209 
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Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). LLC Resonant Converter Modulations for Reduced Junction Temperatures in Half-Bridge Mode and Transformer Flux in the On-the-Fly Morphing Thereto. IEEE Transactions on Power Electronics, 37(11), 13413–13427.https://doi.org/10.1109/tpel.2022.3180758 
LibreCat: /record/44163
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Kirchgässner, W., Wöckinger, D., Wallscheid, O., Bramerdorfer, G., &; Böcker, J. (2022). Application of Thermal Neural Networks on a Small-Scale Electric Motor. IKMT 2022; 13. GMM/ETG-Symposium, 1–6.
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Brosch, A., Rauhaus, J., Wallscheid, O., Böcker, J., &; Zimmer, D. (2022). Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems. IEEE Open Journal of Industry Applications.https://doi.org/10.1109/ojia.2022.3171333 
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DOI: 10.1109/ojia.2022.3171333

Förster, N., Hölscher, J., Piepenbrock, T., Rehlaender, P., Wallscheid, O., Schafmeister, F., &; Böcker, J. (2022). An Open-Source FEM Magnetic Toolbox for Calculating Electric and Thermal Behavior of Power Electronic Magnetic Components. 2022 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), P.1-P.9.
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Brosch, A., Wallscheid, O., &; Böcker, J. (2021). Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation. IEEE Open Journal of Industry Applications, 2, 47–63.https://doi.org/10.1109/OJIA.2021.3066105 
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Gedlu, E. G., Wallscheid, O., &; Böcker, J. (2021). Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks. 2021 IEEE International Electric Machines &; Drives Conference (IEMDC), 1–8.https://doi.org/10.1109/IEMDC47953.2021.9449548 
LibreCat: /record/29663
DOI: 10.1109/IEMDC47953.2021.9449548

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 
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Weber, D., Heid, S., Bode, H., Lange, J., Hüllermeier, E., &; Wallscheid, O. (2021). Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. IEEE Access, 9, 35654–35669.https://doi.org/10.1109/ACCESS.2021.3062144 
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DOI: 10.1109/ACCESS.2021.3062144

Stender, M., Wallscheid, O., &; Böcker, J. (2021). Accurate Torque Control for Induction Motors by Utilizing a Globally Optimized Flux Observer. IEEE Transactions on Power Electronics, 36(11), 13261–13274.https://doi.org/10.1109/tpel.2021.3080129 
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Stender, M., Wallscheid, O., &; Böcker, J. (2021). Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC).https://doi.org/10.1109/pemc48073.2021.9432615 
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Stender, M., Wallscheid, O., &; Böcker, J. (2021). Combined Electrical-Thermal Gray-Box Model and Parameter Identification of an Induction Motor. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society.https://doi.org/10.1109/iecon48115.2021.9589225 
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Stender, M., Wallscheid, O., &; Böcker, J. (2021). Gray-Box Loss Model for Induction Motor Drives. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC).https://doi.org/10.1109/pemc48073.2021.9432491 
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Hanke, S., Wallscheid, O., &; Böcker, J. (2021). Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor.https://doi.org/10.1049%2Ficp.2021.1122 
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Wallscheid, O. (2021). Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. IEEE Open Journal of Industry Applications.
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Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2021). Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark. IEEE Transactions on Energy Conversion, 36(3), 2059–2067.https://doi.org/10.1109/tec.2021.3052546 
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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 
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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 
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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.
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Brosch, A., Wallscheid, O., &; Böcker, J. (2021). Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors. IEEE Transactions on Industrial Informatics.https://doi.org/10.1109/tii.2021.3060469 
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Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2021). Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning. In arXiv preprint arXiv:2103.16323.
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Traue, A., Book, G., Kirchgässner, W., &; Wallscheid, O. (2020). Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control. IEEE Transactions on Neural Networks and Learning Systems, 1–10.https://doi.org/10.1109/tnnls.2020.3029573 
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Kirchgässner, W., Wallscheid, O., &; Böcker, J. (2020). Estimating Electric Motor Temperatures with Deep Residual Machine Learning. IEEE Transactions on Power Electronics, 36(7), 7480–7488.https://doi.org/10.1109/tpel.2020.3045596 
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Brosch, A., Hanke, S., Wallscheid, O., &; Böcker, J. (2020). Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors. IEEE Transactions on Power Electronics, 2179–2190.https://doi.org/10.1109/tpel.2020.3006779 
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Stille, K. S. C., Weber, D., Lange, J., Vogt, T., Wallscheid, O., &; Böcker, J. (2020). Emulation of Microgrids for Research and Validation of Control and Operation Strategies. 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). SPEEDAM, Sorrent, Italy.https://doi.org/10.1109/speedam48782.2020.9161971 
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Stender, M., Wallscheid, O., &; Böcker, J. (2020). Comparison of Gray-Box and Black-Box Two-Level Three-Phase Inverter Models for Electrical Drives. IEEE Transactions on Industrial Electronics, 68(9), 8646–8656.https://doi.org/10.1109/tie.2020.3018060 
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Stender, M., Wallscheid, O., &; Böcker, J. (2020). Accurate Torque Estimation for Induction Motors by Utilizing Globally Optimized Flux Observers. 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM).https://doi.org/10.1109/speedam48782.2020.9161955 
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Heid, S., Weber, D., Bode, H., Hüllermeier, E., &; Wallscheid, O. (2020). OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control. Journal of Open Source Software, 5(54), 2435.
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