A Dual Neural Network as an Identifier for a Robot Arm

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.