REPETITIVE CONTROL OF ROBOTIC MANIPULATORS IN OPERATIONAL SPACE: A NEURAL NETWORK-BASED APPROACH

Cobanoglu N., Yilmaz B. M., TATLICIOĞLU E., Zergeroglu E.

International Journal of Robotics and Automation, vol.37, no.3, pp.302-309, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.2316/j.2022.206-0654
  • Journal Name: International Journal of Robotics and Automation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.302-309
  • Keywords: Repetitive control, neural networks, operational space control, robotic manipulators, LEARNING CONTROL, TRACKING CONTROL
  • Kocaeli University Affiliated: No

Abstract

© 2022 Acta Press. All rights reserved.This work tackles the control problem for robotic manipulators with kinematic and dynamical uncertainties where the end-effector robot is required to perform repetitive tasks. Specifically, a neural network-based estimator and an adaptive component have been fused with a repetitive learning controller-based update rule to compensate for the uncertainties in the robot dynamics and parametrically uncertain kinematics. The closed-loop system stability and tracking of periodic desired operational space position vector are ensured via Lyapunov-type analysis. Experiment results obtained from a planar robotic manipulator are presented to demonstrate the feasibility of the proposed control methodology.