Industrial robot accurate trajectory generation by nested loop iterative learning control

Yu Hsiu Lee, Sheng Chieh Hsu, Tien Yun Chi, Yan Yi Du, Jwu Sheng Hu, Tsu Chin Tsao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Common error sources of industrial robot manipulators include joint servoing error, imprecise kinematics, mechanical compliance, and transmission error. In this work we present a nested loop iterative learning control (ILC) feedforward structure: an inner loop that compensates for motor dynamics, and an outer loop that corrects the deviation along the path tracked, that features practically efficient implementation. Taking advantage of industrial robot's speed reduction transmission, single-input-single-output method is demonstrated effective for the nonlinear coupled robot dynamics. Data-based inversion technique that incorporates motion constraint is used for fast inner loop convergence. The outer loop utilizes inverse Jacobian matrix for joint reference modification. For nonlinear static friction that is difficult to be compensated for with only joint command, notch filtering is utilized in the learning process to avoid exciting vibration inherently exists in the robot. The proposed nested loop ILC requires only the nominal kinematic parameters from the robot manufacturer, and can be readily implemented without modifying the existing robot controllers. The effectiveness of the proposed method is experimentally verified on a six degree-of-freedom robot manipulator.

Original languageEnglish
Article number102487
JournalMechatronics
Volume74
DOIs
StatePublished - Apr 2021

Keywords

  • Data-based dynamic inversion
  • Iterative learning control (ILC)
  • Robot manipulator
  • Trajectory tracking

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