Improving tracking control for robots using neural networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)74-82
Journal / PublicationInternational Journal of Robotics and Automation
Volume11
Issue number2
Publication statusPublished - 1996
Externally publishedYes

Abstract

Tracking control of robots in joint space is studied in this paper. A new control algorithm is proposed based on the well-known computed torque method and a feedforward compensating controller. The function of the feedforward controller, which is realized using an RBF neural network, is to provide high tracking accuracy of robot path-following performance. The RBF neural network is trained offline or online to identify robot modelling error, and convergence of the neural network training is guaranteed based on Lyapunov theory. Through simulations, the proposed scheme is shown to be able to achieve much better tracking performance.

Research Area(s)

  • Convergence, Neural network, Robots, Simulation, Tracking control