| As an important automation equipment,industrial robots have been widely used in various industrial fields.But the industrial robot is a highly nonlinear and strong coupling system,and there are many uncertain factors in the actual operation,which have seriously affected the precision of trajectory tracking control of industrial robots.Therefore,it is of great value to research and improve the trajectory tracking accuracy of industrial robots.The contents and results of this paper are as follows:Taking the SCARA robot as the research object,in order to solve the problem of the low accuracy of the robot dynamic parameter identification,the five order polynomial is used to replace the constant term in the traditional Fourier series,so that the joint angle of the exciting trajectory satisfies the continuous periodicity,and the joint angular velocity and angular acceleration are zero at the start and stop of the excitation path.Taking the condition number of observation matrix in the identification model as the objective function,the genetic algorithm is used to optimize the coefficients of the trajectory.And considering the influence of measurement noise,the weighted least squares method is used to estimate the parameters.The experimental results show that when compared with the traditional method,the root mean square values of the measured values and predicted values of the two joint torques are reduced by 11.50% and 26.35%.To solve the problem of the dynamic model of the SCARA robot is not completely accurate,and it is easy to be affected by the external interference.Based on the method of computing torque,the adaptive fuzzy compensation strategy for the friction modeling error and external disturbance is studied.The idea is to decompose the robot system into nominal and uncertain parts.According to the identified dynamic model,the nominal part is compensated by the method of computing torque.And the uncertain part is compensated by the adaptive fuzzy compensation controller.The simulation results show that the maximum tracking error of the robot can reach 0.5220mm;when the robot's friction modeling error is compensated by the adaptive fuzzy compensation controller,the maximum tracking error is 0.1160mm;when the robot's friction modeling error and the external disturbance are compensated by the adaptive fuzzy compensation controller,the maximum tracking error is 0.0760 mm.The existing robot control system is simple,so it is not easy to design the controller based on the dynamic model.In order to improve the tracking accuracy of the industrial robot based on the motion controller and servo driver,a trajectory tracking control method for SCARA robot based on indirect iterative learning is proposed.Anticipatory-type iterative learning controller is designed to adjust the angle of the servo drive,which using the error output information at the sampling time t + D of the past run batch,to optimize the input angle of the servo driver of the next run at the sampling time t.The experimental results show that the maximum tracking error of the robot is 0.4669 mm when the servo driver is used as the independent controller of the robot joint;and after 20 times of iterative learning the maximum tracking error of the robot is 0.01 mm when A-ILC is used to optimize the input angle of the servo driver.In this paper,the trajectory tracking control strategy of industrial robot is studied from two aspects of dynamic control and static control.And more accurate dynamic parameters are identified,and the influence of friction modeling error and external disturbance are overcome by using the method of computing torque based on adaptive fuzzy compensation controller;and the indirect iterative learning control strategy is adopted to improve the tracking accuracy of industrial robot with the motion controller and servo driver. |
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