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    Abstract
2014 (Vol. 7, Issue: 14)
Article Information:

A Dynamic Effective Fault Tolerance System in Robotic Manipulator using a Hybrid Neural Network based Controller

G. Jiji and M. Rajaram
Corresponding Author:  M. Rajaram 

Key words:  ANN, co-operation, fault tolerance, fuzzy SVM, neural network, robotics ,
Vol. 7 , (14): 2863-2867
Submitted Accepted Published
February 02, 2013 March 21, 2013 April 12, 2014
Abstract:

Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs). In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs) are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.
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  Cite this Reference:
G. Jiji and M. Rajaram, 2014. A Dynamic Effective Fault Tolerance System in Robotic Manipulator using a Hybrid Neural Network based Controller.  Research Journal of Applied Sciences, Engineering and Technology, 7(14): 2863-2867.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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