补偿(心理学)
计算机科学
控制理论(社会学)
人工智能
心理学
控制(管理)
精神分析
作者
Shuhao Yan,Zhiguang Hua,Manfeng Dou,Changliang Dang,Yuanlin Wang,Dongdong Zhao
标识
DOI:10.1109/tpel.2025.3575286
摘要
The deadbeat predictive current control (DPCC) method for SPMSM predicts the current by an accurate model, which suffers from parameter mismatch and harmonic disturbance. To enhance the dynamic tracking and steady-state predictive precision of DPCC under disturbance, a robust incremental model-based DPCC with a hybrid compensation method (HC-IDPCC) is proposed. First, the IDPCC dynamic model considering disturbances is derived, and the parameter sensitivity is analyzed. Then, a harmonic suppression extended state observer (HSESO) is designed to enhance the harmonic suppression ability and parameter robustness. In addition, an improved prediction error correction method (IPEC) is designed to enhance the dynamic performance, which utilizes the current and cumulative predictive errors to compensate for the disturbances. An improved switching method with a sliding window is designed to identify the dynamic and steady-state of the q-axis current and maintain a smooth switch between IPEC and HSESO compensation methods. HC-IDPCC integrates the advantages of IPEC and HSESO, which improves the parameter robustness and harmonic suppression ability while maintaining superior dynamic and steady-state performance. Finally, the effectiveness of the HC-IDPCC is verified by steady-state, dynamic, and switching method experiments.
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