Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm

Abstract

This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results. KeywordsAdvanced differential evolution, adaptive neural networks model, robot arm, modified back-propagation algorithm, nonlinear system identification, autoregressive with exogenous input model Date

Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

“…A robot manipulator's dynamic behavior is entirely nonlinear, coupled, and time-variant, which causes several challenges in fault detection, estimation, identification, and tolerant control. Heavy-duty cycles, overloading, poor installation, and operator errors can be caused by various defects: Sensor faults, actuator failures, and plant faults [1][2][3].…”