Study On Improving The Tracking Accuracy Of Motion Trajectory Of Industrial Robot

Industrial robots have been widely used in industrial production,intelligent manufacturing and other fields because of their high flexibility and efficiency,high repeatable positioning accuracy and good stability.The current application of industrial robots generally have high repeat positioning accuracy,but absolute positioning accuracy is low,make the motion trajectory of robot end precision cannot get effective guarantee,it is hard to meet some task of robot trajectory precision application requirements,such as precision laser welding,laser cutting applications.Therefore,how to improve the accuracy of the motion trajectory of industrial robots has become one of the important directions in the field of robot study.The trajectory accuracy of industrial robot is an important performance index in its practical application.This thesis studies the method to improve the trajectory tracking accuracy of industrial robot from the aspects of scheme design and theoretical study,feasibility analysis and simulation and experimental study.SR4C industrial robot as the study object,the kinematics and dynamics model was set up by improving the optimization probability of crossover and mutation genetic algorithm neural network to establish the inverse kinematics model,and puts forward a kind of neural network iteration error compensation method to improve the inverse kinematics accuracy of industrial robots,decrease the error of each joint effect on the trajectory precision.The common arc and straight trajectory of robots were planned.Based on the trajectory planning,a PID control method with improved Sigmoid function was proposed.The standard Sigmoid function in neural network PID control was improved by using proportional displacement factor to improve the trajectory tracking accuracy of industrial robots.The experimental system of industrial robot trajectory tracking was built,and the related experimental study was carried out.The experimental results verified the effectiveness of the proposed method to improve the trajectory tracking accuracy.In this thesis,the study on improving the tracking accuracy of industrial robot's motion trajectory is carried out.The main study contents include the following aspects:(1)Overall scheme design and theoretical study.The overall scheme of improving the tracking accuracy of industrial robot was designed.Taking SR4C robot as the study object,the kinematics model of the robot was established by using D-H method,and the forward and inverse kinematics of the robot was analyzed and solved.The 3D model of SR4C robot is established in Solid Works,and the model verification and kinematics analysis are co-simulated by MATLAB and Simulink.The dynamics of SR4C robot was modeled and analyzed based on Lagrange equation,and the variation of driving torque of each joint of SR4C robot was obtained by ADAMS.(2)Study on improved neural network inverse solution method and trajectory planning.Based on the SR4C robot workspace,the inverse kinematics model of the neural network was established and simulated.Aiming at the problem of BP neural network easily falling into local optimal defects,the improved genetic algorithm crossover and mutation probability was used to optimize the network weights and thresholds.In order to further improve the accuracy of inverse solution of robot,a neural network error iterative compensation method was proposed for theoretical study and simulation experiments.Finally,trajectory planning is carried out in Cartesian space and joint space respectively,and related simulation is carried out based on the theory of trajectory planning.(3)Study on improving the trajectory tracking method of Sigmoid function.Firstly,the robot PID control theory is introduced and the robot PID control law is deduced.In order to verify the feasibility of PID control,the SR4C robot PID trajectory tracking simulation experiment is carried out in Simulink.Then the neural network PID control method of robot is studied and the simulation experiment is carried out for the PID parameter adjustment problem.Finally,aiming at the defects of the standard Sigmoid function,a proportional displacement parameter factor was proposed to improve the Sigmoid transformation function,and the improved Sigmoid function PID control system was compared and simulated.The experimental results show that the improved Sigmoid function PID control can improve the accuracy of robot motion trajectory tracking.(4)Experimental study.Based on the SR4C robot and AT960 laser tracker and other equipment,the robot trajectory tracking experimental system was constructed,and the robot neural network error iterative compensation experiment was carried out.The neural network error iterative compensation method and the improved Sigmoid function PID control method were used to carry out the robot joint and end trajectory.In the tracking compensation experiment,experiments on the influence of different speeds on trajectory tracking accuracy were carried out.The experimental results show that the average position error of the robot after iterative compensation is reduced from 3.1525 mm to0.153 mm,and the arc and linear trajectory accuracy of the robot after tracking compensation are increased by 60.2%and 69%,respectively.The tracking and compensation effects of each joint error are better than 2.05×10^-2 rad.Experimental results show that the proposed trajectory tracking method can effectively improve the trajectory tracking accuracy of industrial robots.