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

Cong Wang, Yu Zhao, Yubei Chen, Masayoshi Tomizuka

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)96-103
Number of pages8
JournalRobotics and Computer-Integrated Manufacturing
Volume35
DOIs
StatePublished - Oct 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Keywords

  • Contour tracking
  • Gaussian process regression
  • Learning control
  • Nonparametric statistical learning
  • Reference compensation
  • Torque compensation
  • Trajectory tracking

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