University of Technology Sydney. Faculty of Engineering and Information Technology.Permanent Magnet Synchronous Machines (PMSMs) are widely used in industry due to their high power density, high torque/current ratio, low power losses, and high efficiency. Model predictive control (MPC) is a popular control method for PMSMs, but conventional MPC methods have limitations in terms of unsatisfactory steady-state performance, variable switching frequency, and reliance on weighting factors. To overcome these drawbacks, two enhanced MPC methods based on current and torque control have been proposed. These approaches can eliminate weighting factors, generate two switching vectors per control cycle, and exhibit superior performance compared to the conventional MPC.
However, model uncertainties and parameter mismatching are unavoidable in PMSM drives, significantly affecting the control performance. To evaluate the robustness of a control system and determine the robustness level, a novel and systemic robustness evaluation method based on the concept of Six-Sigma methodology has been proposed. This method is validated based on the conventional MPC and five other robust predictive control methods.
Data-driven controls have emerged as a promising alternative to robust MPC, such as model-free predictive current control (MFPCC) for PMSM drives. However, inaccuracies in prediction and performance degradation can occur when the switching vectors remain unchanged for consecutive control cycles, causing unapplied switching to stagnate. To overcome this limitation, an adaptive MFPCC (A-MFPCC) has been proposed, which incorporates a modified current difference updating mechanism. By generating a reference vector based on current tracking error, the A-MFPCC method enforces the update of current differences, preventing stagnation and optimizing the current tracking performance.
Reinforcement learning (RL) based control is another data-driven method, but standard RL-based control usually is trained over a single training task with specific operating conditions and a fixed parameter set. To address this challenge, multi-set robust RL (MSR-RL) based current control of PMSM drives has been proposed. MSR-RL aims to learn a single optimal policy that remains robust across multiple parameter sets or contexts. The proposed A-MFPCC and MSR-RL methods have been validated through numerical simulations, experimental tests, and robustness evaluations, demonstrating superior performance across various operating conditions compared to their conventional counterparts
