Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking

被引：34
作者：
Wang, Cong [1 ]
Zhao, Yu [1 ]
Chen, Yubei [2 ]
Tornizuka, Masayoshi [1 ]
机构：
[1] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
来源：
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING | 2015年 / 35卷
关键词：
Trajectory tracking; Contour tracking; Torque compensation; Reference compensation; Learning control; Nonparametric statistical learning; Gaussian process regression; TIME; REGRESSION; SYSTEMS;
D O I：
10.1016/j.rcim.2015.03.002
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents a method of precision tracking control for industrial robot manipulators. For robotic laser and plasma cutting tasks, the required tracking performance is much more demanding than that for material handling, spot welding, and machine tending tasks. Challenges in control come from the nonlinear coupled multi-body dynamics of robot manipulators, as well as the transmission error in the geared joints. The proposed method features data-driven iterative compensation of torque and motor reference. Motor side tracking and transmission error are handled by separate learning modules in a two-part compensation structure. Depending on the specific setup of end-effector sensing, the method can utilize either timed trajectory measurement or untimed two-dimensional contour inspection. Nonparametric statistical learning is used for the compensation. Considerations on incorporating analytical models and selecting data subsets for more efficient learning are discussed. The method is validated using a six-axis industrial robot. (C) 2015 Elsevier Ltd. All rights reserved.
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页码：96 / 103
页数：8
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