Active Compliant PID Learning Control for Grinding Robots

Lubna Farhi,Farhan Ur Rehman

doi:10.22581/muet1982.2104.05

Lubna Farhi, Farhan Ur Rehman

Open Access

https://doi.org/10.22581/muet1982.2104.05

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Abstract

This article proposes a Proportional, Integral, and Derivative (PID) learning controller for rigid robotic disk grinding mechanism. It has been observed that the stiffness of the robotic arm for a grinder has a direct correlation with the sensitivity of the grinding forces. It is also drastically influenced by the end-effector path tracking error resulting in limited accuracy of the robot. The error in robot’s accuracy is also increased by external interferences, such as surface imperfections and voids in the subject material. These errors can be mitigated via efficient feedback. In the proposed methodology, the controller gain is tuned by implementing a learning-based methodology to PID controllers. The learning control for the robotic grinding system helps by progressively decreasing error between the actual grinded paths and required trace. Experimental results demonstrate that as the grinder machines the required path iteratively, its grinding accuracy improves due to the learning algorithm.

Highlights

Grinding is a mechanical process intended to remove a very thin and even layer of material on the outer edge of the workpiece
It must be noted that controlling the repeated tracking behavior on a desired trajectory is generally an extremely difficult task. This difficulty can be attributed to the empirical dynamic interferences, perturbative torques and naturally inherent nonlinearities in the robot
The proposed active compliant PID learning controller for grinding Robot promises the convergence of the output even when the system parameters are ambiguous due to material disturbances that cannot be predicted

Summary

Introduction

Grinding is a mechanical process intended to remove a very thin and even layer of material on the outer edge of the workpiece. The desired surface morphology of these workpieces demands that the end-effector of the manipulator keeps a stable contact force with the environment, while the grinding tool moves along the profile of the workpiece To perform such a task, concurrently, both force and position of the robot should be controlled [1]. It must be noted that controlling the repeated tracking behavior on a desired trajectory is generally an extremely difficult task This difficulty can be attributed to the empirical dynamic interferences, perturbative torques and naturally inherent nonlinearities in the robot. Despite these uncertainties, grinding robots are approximated to linear systems in controller design This leads to periodic errors during repetitive machining motions, caused by inherent structural and dynamic interferences. It is because of this uncertainty, improving machining accuracy of repetitive tasks via learning through trial is highly desirable

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Conclusion