Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

  • Authors:

  • Haitao Liu;Tie Zhang

  • Affiliations:

  • South China University of Technology, Guangzhou 510640, Guangdong, China and Guangdong Ocean University, Zhanjiang 524088, Guangdong, China;South China University of Technology, Guangzhou 510640, Guangdong, China

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.