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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Updated: Jun 23, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Constant force grinding controller for robots based on SAC optimal parameter finding algorithm.

Chosei Rei1, Qichao Wang2, Linlin Chen3

  • 1Nobot Intelligent Equipment (Shandong) Co., Ltd, Liaocheng, Shandong, China.

Scientific Reports
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Soft-Actor-Critic (SAC) algorithm to optimize Proportional-Integral-Derivative (PID) controllers for robotic grinding. The enhanced controller achieves stable constant force grinding despite environmental changes.

Keywords:
Constant force grindingOptimal parameter finding algorithmRobotsSimulation modelSoft actor critic

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Conventional Proportional-Integral-Derivative (PID) controllers struggle with maintaining constant force grinding under dynamic environmental conditions.
  • The precise tuning of compensation terms for PID controllers in robotic applications is challenging.
  • Adapting robotic grinding forces to environmental variations requires advanced control strategies.

Purpose of the Study:

  • To develop an optimal parameter-finding algorithm for PID controller compensation terms using Soft-Actor-Critic (SAC).
  • To enable stable and adaptive constant force grinding control in robots.
  • To address the difficulties in obtaining optimal compensation parameters for PID controllers.

Main Methods:

  • An optimal parameter-finding algorithm based on Soft-Actor-Critic (SAC) was developed, incorporating state-action training, normalization, reward function design, and deep neural network architecture.
  • The SAC algorithm was employed to determine optimal compensation parameters for the PID controller.
  • A simulation model of a grinding robot with force feedback was created to validate the algorithm's performance.

Main Results:

  • The trained controller demonstrated the ability to achieve constant force grinding in simulations.
  • The algorithm successfully identified optimal compensation parameters for the PID controller.
  • Experimental validation on a physical robot confirmed the effectiveness and environmental adaptability of the proposed control strategy.

Conclusions:

  • The Soft-Actor-Critic (SAC) based optimal parameter-finding algorithm effectively enhances PID controllers for constant force grinding.
  • The developed method provides a robust solution for achieving stable robotic grinding under changing environmental conditions.
  • The approach shows significant potential for improving precision and adaptability in robotic manufacturing processes.