A Dual Neural Network as an Identifier for a Robot Arm. (17th April 2015)

• Record Type: Journal Article
• Authors: Rodríguez, Sergio Alvarez; Hernández, Carlos E. Castañeda
• Is Part Of: International journal of advanced robotic systems. Volume 12:Number 4(2015)
• Publication Date: 2015-04-17
• Subjects: Robot arm -- dual recurrent neural network -- standard deviation for the identification error -- nonlinear block control transformation form; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/58760
• Languages: English
• ISSNs: 1729-8806

Abstract

A novel dual recurrent neural network is presented and is used to identify the dynamics for a robot arm with three-Degrees of freedom (DoF) and trained with a filtered error algorithm. The dual neural network has a structure of two recurrent neural networks working simultaneously fighting each other to obtain the best identification values, being the criteria for the selection of the vest values: the standard deviation for the identification error. The neural identifier provides important information to a nonlinear block control transformation form acting as a control law to solve the trajectory tracking problem for the robotic plant's behavior.