Force sensorless control with VCM is thought to be challenging because its thrust constant fluctuation characteristic and the flexible flat cable connected to the moving coil degrade the estimation accuracy. Modeling methods of thrust constant fluctuation have been proposed in previous studies. However, these methods were time-consuming, easily affected by friction, and did not consider cable disturbance. This paper presents an identification and compensation methodology for nonlinear internal disturbances in a voice coil motor (VCM) to accomplish precise force control with a linear VCM stage. In this study, a novel compensator was constructed on basis of these deterioration mechanisms. To realize the compensator, the deteriorations had to be obtained. To that end, a novel approach to identify the parameter fluctuation and cable restoring force was developed, and the compensator was applied to sensorless force control. The proposed approaches were verified through experiments using the prototype of a linear VCM stage.

Automation of visual inspection is a critical aspect in industrial fields. Recently, research on anomaly detection using neural networks has been gaining increasing attention. In particular, approaches that use a pre-trained convolutional neural network have exhibited high performance. In this study, we focused on PatchCore, which is a high-performance model, and further improved it using two high-resolution images to accurately detect small anomalies. However, the extracted features (memory bank) consume a large amount of memory and storage; the memory bank is compressed by k-means clustering. Moreover, the inference time was reduced by an approximate nearest-neighbor search using an inverted index. Our method achieved an image-level AUROC of 0.994 on the MVTec anomaly detection dataset. In addition, a pixel-level AUROC of 0.984 was achieved, which is better than that of PatchCore. Furthermore, the compression time was reduced by more than 97% by clustering the memory bank using k-means while maintaining the performance.