Robotic constant force grinding control based on grinding model and iterative algorithm

The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.

The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.

The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.

As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.