Please use this identifier to cite or link to this item:
Title: A proposed neural internal model control for robot manipulators
Authors: Yıldırım, Şahin
Keywords: Alopex learning algorithm
Back propagation
Diagonal recurrent network
Feed forward neural network
Internal model control
Recurrent hybrid network
Issue Date: Sep-2006
Publisher: CSIR
Series/Report no.: B25J9/00; G05B13/04
Abstract: This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based on Recurrent Hybrid Networks (RHNs). The NIMC, an 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.
Description: 713-720
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.65(09) [September 2006]

Files in This Item:
File Description SizeFormat 
JSIR 65(9) 713-720.pdf667.33 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.