Soft Sensor Simulation of Minimum Energy Consumption of Joint Manipulator Drive System Based on Improved BP Neural Network

Abstract

The energy dissipation level of articulated mechanical arms directly affects the control stability of industrial robot systems. For a six-degree-of-freedom articulated robot driven by a DC servo motor and reduction gear, based on the Hamiltonian and Minimum theories, a model of the articulated manipulator drive system under load torque is established, and the expressions of optimal angular velocity and control current are obtained considering the viscous friction and coulomb factors. According to the analysis of actual parameter simulation experiments, it can be seen that the energy consumption of the joint manipulator drive system increases with the decrease of efficiency, the increase of the coulomb friction torque of the servo motor, and the increase of the load torque. Within the transit time, the total energy consumption of the drive system is smaller, so it is very important to obtain the transit time within the optimal area for the minimum energy consumption of the joint robotic arm drive system. At the same time, a soft neural network model based on the improved BP neural network built on the neural network toolbox for the energy consumption of the articulated mechanical arm is established. Finally, the experimental platform of servo motor drive system is built and used for the experiment of servo motor drive, and the purpose of closed-loop control is also achieved. Through experimental analysis, it can be seen that the proposed soft sensor model of joint minimum energy consumption based on improved BP neural network can more accurately realize the energy consumption prediction under unknown angular displacement and unknown load, and provide a theoretical basis for joint energy monitoring.