TY - CHAP A1 - Broghammer, Lara A1 - Hufnagel, Dennis A1 - Schindler, Tobias A1 - Hoerner, Michael A1 - Karamanakos, Petros A1 - Dietz, Armin A1 - Kennel, Ralph T1 - Reinforcement Learning Control of Six-Phase Permanent Magnet Synchronous Machines T2 - 2023 13th International Electric Drives Production Conference (EDPC) N2 - 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. KW - Multi-phase machines KW - current control KW - permanent magnet synchronous machine (PMSM) KW - power electronics KW - deep reinforcement learning KW - deep deterministic policy gradient (DDPG) Y1 - 2023 SN - 979-8-3503-7049-2 U6 - https://doi.org/10.1109/edpc60603.2023.10372153 PB - IEEE ER -