An Improved Deadbeat Predictive Current Control with Online Parameter Identification for Surface-Mounted PMSMs
Yu Yao (Southeast University)
Yunkai Huang (Southeast University)
Fei Peng (Southeast University)
J. Dong (TU Delft - DC systems, Energy conversion & Storage)
Hanqi Zhang (Southeast University)
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Abstract
In this article, an improved deadbeat predictive current control (DPCC) method with parameters identification for surface-mounted permanent magnet synchronous machines (SPMSMs) is proposed. With the proposed DPCC method, zero steady-state current error and deadbeat dynamic current response could be achieved, even with inaccurate initial motor parameters. On basis of the conventional DPCC method, a novel parameters identification for the stator resistance and inductance is developed, which is the main contribution of this article. The proposed parameters identification method works based on a reconstructed characteristic vector from the disturbance observer with current injection. Compared with traditional recursive-least-square methods, the proposed method can be implemented with greatly reduced computation burden. Additionally, since the design is established based on the fully discretized model, the effectiveness will be guaranteed on both low-frequency and high-frequency motors, which is a significant advantage of the proposed method.