@inproceedings{BroghammerHufnagelSchindleretal.2023, author = {Broghammer, Lara and Hufnagel, Dennis and Schindler, Tobias and Hoerner, Michael and Karamanakos, Petros and Dietz, Armin and Kennel, Ralph}, title = {Reinforcement Learning Control of Six-Phase Permanent Magnet Synchronous Machines}, series = {2023 13th International Electric Drives Production Conference (EDPC)}, booktitle = {2023 13th International Electric Drives Production Conference (EDPC)}, publisher = {IEEE}, isbn = {979-8-3503-7049-2}, doi = {10.1109/edpc60603.2023.10372153}, pages = {8}, year = {2023}, abstract = {Control of multi-phase machines is a challenging topic due to the high number of controlled variables. Conventional control methods, such as field-oriented control (FOC), address this issue by introducing more control loops. This, however, increases the controller design complexity, while the tuning process can become cumbersome. To tackle the above, this paper proposes a deep deterministic policy gradient algorithm based controller that fulfills all the control objectives in one computational stage. More specifically, the proposed approach aims to learn a suitable current control policy for six-phase permanent magnet synchronous machines to simplify the commissioning of the drive system. In doing so, physical limitations of the drive system can be accounted for, while the compensation of imbalances between the two three-phase subsystems is rendered possible. After validating the training results in a controller-in-the-loop environment, test bench measurements are provided to demonstrate the effectiveness of the proposed controller. As shown, favorable steady-state and dynamic performance is achieved that is comparable to that of FOC. Therefore, as indicated by the presented results, reinforcement learning-based control approaches for multi-phase machines is a promising research area.}, language = {en} } @inproceedings{SchindlerBroghammerKaramanakosetal.2023, author = {Schindler, Tobias and Broghammer, Lara and Karamanakos, Petros and Dietz, Armin and Kennel, Ralph}, title = {Deep Reinforcement Learning Current Control of Permanent Magnet Synchronous Machines}, series = {2023 IEEE International Electric Machines \& Drives Conference (IEMDC)}, booktitle = {2023 IEEE International Electric Machines \& Drives Conference (IEMDC)}, publisher = {IEEE}, isbn = {979-8-3503-9899-1}, doi = {10.1109/IEMDC55163.2023.10238988}, pages = {7}, year = {2023}, abstract = {This paper presents a current control approach for permanent magnet synchronous machines (PMSMs) using the deep reinforcement learning algorithm deep deterministic policy gradient (DDPG). The proposed method is designed by examining different training setups regarding the reward function, the observation vector, and the actor neural network. In doing so, the impact of the different design factors on the steady-state and dynamic behavior of the system is assessed, thus facilitating the selection of the setup that results in the most favorable performance. Moreover, to provide the necessary insight into the controller design, the entire path from training the agent in simulation, through testing the control in a controller-in-the-loop (CIL) environment, to deployment on the test bench is described. Subsequently, experimental results are provided, which show the efficacy of the presented algorithm over a wide range of operating points. Finally, in an attempt to promote open science and expedite the use of deep reinforcement learning in power electronic systems, the trained agents, including the CIL model, are rendered openly available and accessible such that reproducibility of the presented approach is possible.}, language = {en} }