Modeling and Active Vibration Control of Intelligent Flexible Manipulator Based on Deep Learning

Abstract

Robotic arms have been widely used in industrial production for a long time. With the advent of the era of intelligent manufacturing, intelligent flexible robotic arms have received more and more attention in both theoretical research and actual production. As a typical representative of rigid-flexible coupled multi-body dynamic systems, flexible manipulators are widely used in aerospace, robotics and modern intelligent manufacturing fields. Compared with the bulky rigid manipulator, the flexible manipulator has the advantages of low energy consumption, large operating space, and high load-to-weight ratio. A key issue related to the modeling of flexible manipulators is the approximate method for analyzing the structure of flexible manipulators. In the actual operation process, the motion of the flexible manipulator is often composed of a wide range of rigid body motion and elastic vibration caused by the elastic deformation of the flexible body. Due to the existence of elastic vibration, the positioning accuracy of the flexible arm during the movement process is reduced, and even the reliability of the system is reduced. In order to achieve accurate identification and positioning of parts in complex situations, combined with the advantages of deep learning having powerful representation and modeling capabilities, deep learning technology is applied to the robotic arm to improve reliability.