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An Improvement of Trajectory Tracking Accuracy of Automatic Sewing Robot System by Variable Gain Learning Control

机译:可变增益学习控制改进自动缝纫机系统的轨迹跟踪精度

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In the sewing factory, non-routine tasks, especially curved surface sewing of three-dimensional products are still executed manually by human workers, because it is difficult to handle the sewing parts precisely by the automatic machine. Then, we are developing an automatic sewing robot system for their sewing. We evaluated the developed robot system and confirmed that the curved surface sewing motion is executed smoothly with low feeding speed. But, the trajectory tracking accuracy becomes bad when the feeding speed is high. Then, we applied learning control method to our robot system and confirmed that the trajectory tracking accuracy is improved sufficiently by this method even the sewing parts feeding speed is equal to human workers. However, we need much time to find suitable learning gains for getting the good result. So, we propose a variable gain learning control method which finds suitable learning gains automatically based on the trajectory tracking error of the robot arm. Finally, we confirmed that the enough trajectory tracking accuracy is achieved by the proposed method without much time and effort.
机译:在缝纫工厂,非常规任务,特别是三维产品的弯曲表面缝制仍然由人类工人手动执行,因为难以通过自动机器精确地处理缝纫部件。然后,我们正在开发一种自动缝纫机器人系统,用于缝纫。我们评估了开发的机器人系统,并确认弯曲表面缝纫运动以低馈送速度平稳地执行。但是,当喂料速度高时,轨迹跟踪精度变得差。然后,我们将学习控制方法应用于我们的机器人系统,并确认轨迹跟踪精度通过这种方法充分提高了这种方法,即使是缝纫部件进给速度等于人工。但是,我们需要很多时间来找到获得良好结果的合适学习收益。因此,我们提出了一种可变的增益学习控制方法,基于机器人臂的轨迹跟踪误差自动找到合适的学习收益。最后,我们确认,通过多长时间和努力,所提出的方法实现了足够的轨迹跟踪精度。

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