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Task Space Contouring Error Estimation and Precision Iterative Control of Robotic Manipulators

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The task space contouring performance is significant for the machining accuracy of industrial robotic manipulators, but the contouring control of end-effector which is important in the industry has received scant… Click to show full abstract

The task space contouring performance is significant for the machining accuracy of industrial robotic manipulators, but the contouring control of end-effector which is important in the industry has received scant attention. In this letter, a novel task space contouring error estimation and control scheme is developed to address the end-effector precision contouring problem of robotic manipulators. In order to quantify manipulator contouring performance, a 6-DOF synchronized contouring error is defined in the task space. Based on the definition, an optimization problem is constructed and solved to achieve high precision contouring error estimation. The proposed method shows excellent estimation accuracy even under extreme contouring conditions, which is the prerequisite for accurate contouring control. Then the solved contouring error is iteratively compensated into the end-effector reference in a feedforward way to achieve contouring error reduction. The effectiveness of the proposed method is derived theoretically under the assumption of small error and nonsingularity. Comparative experiments are conducted on an industrial manipulator. Quantitative results on different extreme trajectories demonstrate that the proposed control scheme not only has advantages in control stability and implementation simplicity, but also outperforms traditional joint space iterative learning control and cross-coupled iterative learning control on the end-effector contouring accuracy. The proposed method actually provides a practical solution to high-precision contouring applications for industrial manipulators.

Keywords: estimation; contouring error; control; task space

Journal Title: IEEE Robotics and Automation Letters
Year Published: 2022

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