Improved Positioning Precision using a Multi-rate Multi-sensor in Industrial Motion Control Systems

Industrial motion control systems, e.g. pick-and-place tasks in semiconductor manufacturing equipment, require precise positioning for achieving high machine throughput. Linear encoders are the standard industrial sensors used for position feedback due to their relatively low cost, high resolution, and high operating frequency. The challenge is that the linear encoders measure the positions at the points-of-control of the equipment, e.g. motors, and not at the points-of-interest, e.g. pick-and-place positions. The coupling between a point-of-control and the point-of-interest is affected by external disturbances such as mechanical misalignment of the product, friction, and warping of the material, and linear encoders fail to sense these disturbances. Vision-based sensing is a potential alternative to achieve robust sensing and high-precision control. However, vision processing has a long computational delay and affects the machine throughput. In this paper, we propose a multi-rate multi-sensor fusion approach to improve the positioning accuracy of industrial motion control systems with different points-of-control and points-of-interest. We present a multi-rate Kalman filter with bias correction to fuse accurate but slow and delayed vision sensor data with fast but less accurate linear encoder data for high-precision position control. We validate the proposed method in an evaluation framework by considering an industrial case study of a semiconductor die-bonding machine. A design-space exploration is done to evaluate the performance of the proposed solution with respect to various relevant design parameters. The effectiveness of the proposed solution depends on the type of disturbances and vision processing delay. For the parameter range under consideration, we achieve a positioning accuracy of 1 mu m.