工业机器人负载动力学参数辨识方法_陈友东.pdf

the finite Fourier series for payload identification based on the analysis of the effect of the payload parameters on each joint

torque, after selecting corresponding joint axis. Then the data of joint angle and joint torque are collected respectively while

the excitation trajectory is run in conditions of no payload and other three different payloads, before they are processed by

the low-pass filter. Finally, the weighted least square method is used to identify the payload dynamics parameters based

on dynamic linear model. After the robot runs the verification trajectory, the payload identification result is evaluated by

calculating the root mean square (RMS) of the difference between the calculated payload torque and the measured payload

torque. At the same time, this method is compared with CAD (computer aided design) method. Results show that the former

method can reduce the RMS value of the latter up to 16%, and that it is stable and effective under different payloads. Using

this method, the way to drive all the joint axes is avoided, and thus not only the error caused by the coupling of the robot, but

also the optimization time of the excitation trajectory are reduced. In conclusion, it effectively improves the efficiency and