Purpose

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.

Design/methodology/approach

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.

Findings

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%.

Originality/value

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.

中文翻译：

---

基于磨削模型和迭代算法的机器人恒力磨削控制

目的

本文的目的是针对机器人磨削的冲击阶段和加工阶段提出一种机器人恒磨削力控制算法。

设计/方法/方法

机器人恒磨削力控制算法基于磨削模型和迭代算法。在冲击阶段，采用自抗扰控制规划机器人参考接触力，根据机器人实际接触力与机器人参考接触力的误差调整机器人速度。在处理阶段，利用RBF神经网络构建具有机器人位置偏移位移和受控输出的模型，并根据RBF神经网络模型估计控制参数的增量。通过不断在线迭代控制参数，接触力和预期力的误差逐渐收敛。

发现

实验结果表明，基于磨削模型和迭代算法的机器人法向力超调量小，处理收敛速度快。法向力与预期力的误差大多在±3 N以内。 基于力控制算法的法向力比基于位置控制的法向力更稳定，加工工件的表面粗糙度也得到了改善，与位置控制相比，Ra 值降低了 24.2%。

原创性/价值

由于该方法在机器人磨削的冲击阶段和加工阶段获得了恒定的效果，并经实验验证，该方法可用于机器人磨削，以提高加工精度。