Book, Gerrit, 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, 2021, pp. 187–201, doi:10.1109/ojpel.2021.3065877.

Hanke, Sören, et al. Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor. IET Digital Library, 2021, doi:10.1049%2Ficp.2021.1122.

Brosch, Anian, et al. “Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors.”10.1109/tpel.2020.3006779__IEEE Transactions on Power Electronics, 2020, pp. 2179–90, doi:10.1109/tpel.2020.3006779.

Hanke, Sören, et al. “Data Set Description: Identifying the Physics Behind an Electric Motor–Data-Driven Learning of the Electrical Behavior (Part II).” ArXiv Preprint ArXiv:2003.06268, 2020.

Hanke, Sören, et al. “Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification.” 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019, pp. 1–6.

Hanke, Sören, et al. “Continuous-Control-Set Model Predictive Control with Integrated Modulator in Permanent Magnet Synchronous Motor Applications.” 2019 IEEE International Electric Machines &; Drives Conference (IEMDC), 2019, pp. 2210–2216.

Hanke, Sören, et al. “Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives.” ArXiv Preprint ArXiv:1804.00854, 2018.

Hanke, Sören, et al. “A Direct Model Predictive Torque Control Approach to Meet Torque and Loss Objectives Simultaneously in Permanent Magnet Synchronous Motor Applications.” 2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2017, pp. 101–106.