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

Autor:	G. Jiji, M. Rajaram
Rok vydání:	2014
Předmět:	Engineering General Computer Science Artificial neural network Neuro-fuzzy business.industry General Engineering Fault tolerance Control engineering Robotics Fault detection and isolation Computer Science::Robotics Hybrid neural network Control theory Robot Artificial intelligence business
Zdroj:	Research Journal of Applied Sciences, Engineering and Technology. 7:2863-2867
ISSN:	2040-7467 2040-7459
DOI:	10.19026/rjaset.7.611
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a3b6ab1fb8f386fc37c2ce81789b0791 https://doi.org/10.19026/rjaset.7.611 Zobrazit plný text záznamu