EHB用无刷直流电机齿槽转矩电流补偿控制策略研究

电子液压制动系统现已普遍使用无刷直流电机,但其齿槽转矩会影响电机伺服控制品质,进而阻碍EHB综合性能的提升。为此,开展EHB用无刷直流电机齿槽转矩的电流补偿控制策略研究。首先,通过解析分析和有限元方法获取齿槽转矩变化规律;其次,根据齿槽转矩与转子位置的映射关系,提出基于电机位置信号的转矩实时电流补偿控制策略;最后,搭建电机的控制模型和有限元模型,并进行联合仿真。研究结果表明:加入电流补偿控制策略后,电机转速和转矩波动明显降低,位置伺服精度得到提升,有效抑制了齿槽转矩对电机伺服控制品质的影响。

基于PLL自适应滑模观测器的PMSM无传感器控制

针对传统滑模观测器无传感器控制方法反电动势基波中高频谐波含量高、抖振严重以及转子位置估计误差大等问题,提出了一种锁相环结构(PLL)自适应滑模观测器永磁同步电机无传感器控制方法。首先,在满足Lyapunov稳定性的前提下,推导并建立了反电动势的自适应律,通过构造自适应滑模观测器,使得反电动势观测误差迅速衰减;同时采用带有消除旋转影响环节的锁相环得到转子位置,消除了转速变化的影响,进一步提高了观测精度;最后,在一台2.9 kW表贴式永磁同步电机上进行了实验验证。实验结果表明,该方法有效地抑制了滑模抖振,降低了反电动势中的高频谐波,提高了转子位置的观测精度。

3D Point Cloud Semantic Segmentation Based PAConv and SE_variant

With the increasing popularity of 3D sensors(e.g.,Kinect)and light field cameras,technologies such as driverless,smart home and virtual reality have become hot spots for engineering applications.As an important part of 3D vision tasks,point cloud semantic segmentation has received a lot of attention from researchers.In this work,we focus on realistically collected indoor point cloud data and propose a point cloud semantic segmentation method based on PAConv and SE_variant.The SE_variant module captures global perception from a broad perspective of feature space by fusing different pooling methods,which fully utilize the channel information of point clouds.The effectiveness of the method is verified by comparing with other methods on S3DIS and ScanNetV2 semantic tagging benchmarks,and achieving 65.3%mIoU in S3DIS,47.6%mIoU in ScanNetV2.The results of the ablation experiments verify the effectiveness of the key modules and analyze how to use the attention mechanism to improve the 3D semantic segmentation performance.