A proposed neural internal model control for robot manipulators

Yildirim S.

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, vol.65, no.9, pp.713-720, 2006 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 65 Issue: 9
  • Publication Date: 2006
  • Journal Name: JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.713-720
  • Keywords: alopex learning algorithm, back propagation, diagonal recurrent network, feed forward neural network, internal model control, recurrent hybrid network, NETWORK CONTROLLER, SYSTEMS
  • Erciyes University Affiliated: Yes

Abstract

This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based oil Recurrent Hybrid Networks (RHNs). The NIMC, all alternative to the basic inverse control scheme, consists of a forward internal neural model of robot, a neural controller and a conventional feedback controller. An Alopex Learning Algorithm (ALA) was used to adjust weights of the proposed neural network. Backpropagation (BP) algorithm is also employed for comparison. Diagonal Recurrent Networks (DRNs) and Feedforward Neural Networks (FNNs) controllers were used for comparison. The robot in this study was adept one SCARA type robot manipulator.