Active Compliant PID Learning Control for Grinding Robots

  • Lubna Farhi Department of Electronic Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
  • Farhan Ur Rehman Department of Mechanical Engineering, University of Toronto, Canada.


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.

Oct 1, 2021
How to Cite
FARHI, Lubna; REHMAN, Farhan Ur. Active Compliant PID Learning Control for Grinding Robots. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 40, n. 4, p. 747 - 752, oct. 2021. ISSN 2413-7219. Available at: <>. Date accessed: 27 oct. 2021. doi:
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License