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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
Summary
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.
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.
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