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Published on: May 8, 2021
Temporal compensation physics-informed neural network modeling repetitive small-range rotation inverse dynamics
Fangyu Li1, Man Li1, Honggui Han1
1School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China; Engineering Research Center of Digital Community Ministry of Education, Beijing University of Technology, Beijing, 100124, China.
Summary
This study introduces a temporal compensation physics-informed neural network (TC-PINN) to address dynamic hysteresis in robotic motion mapping. The novel TC-PINN model significantly enhances accuracy for high-precision robotic operations.
Area of Science:
- Robotics
- Control Systems
- Machine Learning
Background:
- High-fidelity motion mapping is crucial for precise robotic tasks.
- Existing models struggle with dynamic hysteresis during small-range rotations, reducing accuracy.
Purpose of the Study:
- To develop a novel method for mitigating dynamic hysteresis effects in robotic motion mapping.
- To improve the accuracy of torque mapping in robotic arms.
Main Methods:
- A temporal compensation physics-informed neural network (TC-PINN) was proposed.
- A rotational state trajectory with cumulative joint rotation features was constructed.
- A Linformer-based nonlinear error fitting structure was designed for compensation.
Main Results:
- TC-PINN effectively mitigates dynamic hysteresis effects.
- Hysteresis-aware torque mapping accuracy was improved on a 7-DOF robotic arm.
- The total mean absolute error was reduced to 9.65×10-4 Nm.
Conclusions:
- The proposed TC-PINN offers a viable solution for enhancing robotic motion mapping accuracy.
- This approach is particularly effective for repetitive small-range rotations with hysteresis.
- The method contributes to more reliable and precise robotic operations.
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