A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots

Authors:
"Ahin Yildirim
Affiliations:
Engineering Faculty, Erciyes University, Kayseri, Turkey 38039
Venue:
Journal of Intelligent and Robotic Systems
Year:
2005

Citing 6
Cited 0

Constrained relations between two coordinated industrial robots for motion control

International Journal of Robotics Research
Dynamics and stability in coordination of multiple robotic mechanisms

International Journal of Robotics Research
Path Planning and Control of a Cooperative Three-Robot System Manipulating Large Objects

Journal of Intelligent and Robotic Systems
Neural network inverse control techniques for PD controlled robot manipulator

Robotica
A methodology for fault diagnosis in robotic systems using neural networks

Robotica
Robust position/force control of multiple robots using neural networks

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses a model refernce adaptive (MRAC) position/force controller using proposed neural networks for two co-operating planar robots. The proposed neural network is a recurrent hybrid network. The recurrent networks have feedback connections and thus an inherent memory for dynamics, which makes them suitable for representing dynamic systems. A feature of the networks adopted is their hybrid hidden layer, which includes both linear and nonlinear neurons. On the other hand, the results of the case of a single robot under position control alone are presented for comparison. The results presented show the superior ability of the proposed neural network based model reference adaptive control scheme at adapting to changes in the dynamics parameters of robots.