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Maximilian Schenke

Contact
Publications
 Maximilian Schenke

Power Electronics and Electrical Drives

Member - Research Associate - Reinforcement learning for electric drives

Phone:
+49 5251 60-5487
Fax:
+49 5251 60-3443
Office:
E4.130
Visitor:
Pohlweg 55
33098 Paderborn

Open list in Research Information System

2021

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments

G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021), pp. 187-201

DOI


gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems

P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid, Journal of Open Source Software (2021), 2498

DOI


A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

M. Schenke, O. Wallscheid, IEEE Open Journal of the Industrial Electronics Society (2021), pp. 388-400

DOI


Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning

M. Schenke, O. Wallscheid, arXiv preprint arXiv:2105.08990 (2021)


2019

Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept

M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial Informatics (2019), pp. 4650-4658

DOI


2018

Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines

O. Wallscheid, M. Schenke, J. Böcker, in: 2018 21st International Conference on Electrical Machines and Systems (ICEMS), 2018, pp. 1181–1186


A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation

O. Wallscheid, M. Schenke, J. Böcker, in: 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), 2018, pp. 793–799


Open list in Research Information System

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