Wave friction correction method for a robot based on BP neural network

For sensorless force control of a robot such as by drag-teaching and collision detection, the control accuracy depends on the accuracy of the robot dynamics model. The error of the robot dynamics model comes from two aspects, modeling and identification errors and from unmodeled dynamics. Among the unmodeled dynamics, one of the important sources of unmodeled dynamic is the friction inside the robot reducer. When the reducer rotates, there is mutual extrusion and friction between the internal components of the reducer. This kind of friction will change as the gear meshing state transforms, resulting in the phenomenon of wave friction torque. A remarkable feature of wave friction torque is that it has a periodic relationship with the joint location and it is often modeled by the Fourier series function. Wave friction torque is obvious when the rotational speed of the joint is low and decreases with the increase in rotational speed. In order to improve the accuracy of the robot dynamics model, the wave friction torque needs to be modeled and eliminated. Aiming at the wave friction of the robot harmonic joint during the rotation process, a modeling method based on a Fourier series function and BP neural network was proposed, the dynamic model of the robot was optimized, and the calculation error of the joint torque caused by the wave friction was corrected. By studying the variation characteristics of the wave friction of the harmonic reducer joint under different influencing factors, the combination of the Fourier series and BP neural network was used to model the wave friction. By adding the Fourier series function as the auxiliary input of the BP neural network, the difficulty of fitting the torque error curve due to the presence of high frequency periodic fluctuations was overcome. The neural network was trained in the off-line environment to complete the modeling of the wave friction, and then to improve the dynamic model of the robot and correct the wave friction. The experimental results show that the improved dynamic model can effectively predict the wave friction of the harmonic reducer joint and keep the corrected torque error within the range of-0.5, 0.5 N·m, and the variance is 0.1659 N²·m², which is 24.23% before the correction.