Fault Detection and Tolerance on Robot Manipulators Locked Joint Failure Using Anfis

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 paper, 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 nonlinear signal are used for complete process and treatment, the artificial neural networks (ANNs) are used to detect the freeswinging 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.