Design of adaptive robot control system using recurrent neural network

Yildirim S.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, vol.44, no.3, pp.247-261, 2005 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 3
  • Publication Date: 2005
  • Doi Number: 10.1007/s10846-005-9012-6
  • Journal Name: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.247-261
  • Keywords: back-propagation, diagonal recurrent network, PID-robust controller, recurrent neural network, robot manipulator
  • Erciyes University Affiliated: Yes

Abstract

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.