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A Task-Level Iterative Learning Control Algorithm for Accurate Tracking in Manipulators With Modeling Errors and Stringent Joint Position Limits

[+] Author Affiliations
Pranav A. Bhounsule, Abhishek A. Bapat

University of Texas at San Antonio, San Antonio, TX

Katsu Yamane

Disney Research, Pittsburgh, PA

Paper No. DSCC2016-9759, pp. V002T27A002; 8 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME


We present an iterative learning control algorithm for accurate task space tracking of kinematically redundant robots with stringent joint position limits and kinematic modeling errors. The iterative learning control update rule is in the task space and consists of adding a correction to the desired end-effector pose based on the tracking error. The new desired end-effector pose is then fed to an inverse kinematics solver that uses the redundancy of the robot to compute feasible joint positions. We discuss the stability, the rate of convergence and the sensitivity to learning gain for our algorithm using quasi-static motion examples. The efficacy of the algorithm is demonstrated on a simulated four link manipulator with joint position limits that learns the modeling error to draw the figure eight in 4 trials.

Copyright © 2016 by ASME



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