Adaptive Line Trajectory Identification of Industrial 5-DOF Robot Arm Using Neural MIMO NARX Model

Abstract

This paper investigates a novel forward adaptive neural MIMO NARX model which is applied for modeling and identifying the forward kinematics of the industrial 5-DOF robot arm system. The nonlinear features of the forward kinematics of the industrial 5-DOF robot arm drive are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the forward neural MIMO NARX (FNMN) model for the forward kinematics of the industrial 5-DOF robot arm. The results show that the proposed adaptive neural NARX model trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.