本研究提出了一種用於內藏式永磁同步馬達驅動的新型模型預測電流控制。所提出的模型預測電流控制將滑模觀測器與適應性神經網路相結合，以提高控制效能。傳統的連續控制集模型預測電流控制對馬達參數變化高度敏感。為了克服這一問題，所提出的方法旨在增強連續控制集模型預測電流控制的強健性，同時加强電流控制中的抗干擾能力。 該研究首先製定了連續控制集模型預測電流控制的建模和控制策略，考慮了時間延遲對內藏式永磁同步馬達dq軸的影響。此外，對dq軸上的總集參數擾動進行了表徵。 然後對滑模觀測器進行了詳細分析，證明了其在dq軸電流控制中估計總集參數擾動的能力。為了進一步提高效能，將適應性神經網路集成到滑模觀測器中，以估計dq軸總集參數干擾，從而降低所需的開關增益。最後，實驗結果驗證了所提出的模型預測電流控制方法的有效性，該方法將連續控制集模型預測電流控制與基於適應性神經網路的滑模觀測器相結合，提高了內藏式永磁同步馬達驅動驅動在恒定扭矩區域運行的效能。;This study presents a novel model predictive current control (MPCC) strategy for an interior permanent magnet synchronous motor (IPMSM) drive. The proposed MPCC integrates a sliding mode observer (SMO) with a data-driven adaptive neural network (ANN) to enhance control performance. Traditional continuous control set model predictive current control (CCS-MPCC) is highly sensitive to motor parameter variations, necessitating improved robustness. To overcome this limitation, the proposed method aims to mitigate CCS-MPCC’s parameter sensitivity while strengthening disturbance rejection in current control. The study first formulates the modeling and control strategies for CCS-MPCC, incorporating the effects of time delay on the dq-axis of the IPMSM. Additionally, the lumped parameter disturbances in the dq-axis are characterized. A detailed analysis of ANN-based SMO is then presented, demonstrating its ability to estimate the lumped parameter disturbances in dq-axis current control. To further enhance performance, an ANN is integrated into the traditional SMO to estimate the dq-axis lumped parameters disturbance, thereby reducing the required switching gains. Finally, experimental results validate the effectiveness of the proposed MPCC approach, which integrates CCS-MPCC with ANN-based SMO, in improving the performance of IPMSM drives operating in the constant torque region.
