Adaptive MIMO Neural Network Model Optimized by Differential Evolution Algorithm for Manipulator Kinematic System Identification

Abstract

Abstract-In this paper, an adaptive MIMO neural network model is used for simultaneously modeling and identifying the forward kinematics of a 3-DOF robot manipulator. The nonlinear features of the robot manipulator kinematics system are modeled by an adaptive MIMO neural network model based on differential evolution algorithm. A differential evolution algorithm is used to optimally generate the appropriate neural weights so as to perfectly characterize the nonlinear features of the forward kinematics of a 3-DOF robot manipulator. This paper supports the performance of the proposed differential evolution algorithm in comparison with the conventional back-propagation algorithm. The results show that the proposed adaptive MIMO neural network model trained by the differential evolution algorithm for identifying the forward kinematics of a 3-DOF robot manipulator is successfully modeled and performed well.