The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filte...The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.展开更多
基金supported by Aeronautical Science Foundation of China(No.201916052001)China National Key R&D Program(No.2018YFB1309203)Foundation of the Graduate Innovation Center,Nanjing University of Aeronautics and Astronautics(No.xcxjh20210501)。
文摘The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.
