Journal article 92 views
Torque tracking position control of DLR-HIT II robotic hand using a real-time Physics-informed neural network
,
Christian Griffiths,
Chunxu Li
Applied Mathematical Modelling, Start page: 116110
Swansea University Authors:
Ali Al-Shahrabi, MASOUD JAHANBAKHSHJAVID, Ashraf Fahmy Abdo , Christian Griffiths, Chunxu Li
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/j.apm.2025.116110
Abstract
This paper presents a novel approach for controlling the DLR-HIT II robotic hand by leveraging physics-informed neural networks (PINNs) for torque and position control. This method eliminates the need for additional control inputs or external controllers, achieving high precision and simplified dyna...
| Published in: | Applied Mathematical Modelling |
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| ISSN: | 0307-904X 1872-8480 |
| Published: |
Elsevier Inc
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69140 |
| Abstract: |
This paper presents a novel approach for controlling the DLR-HIT II robotic hand by leveraging physics-informed neural networks (PINNs) for torque and position control. This method eliminates the need for additional control inputs or external controllers, achieving high precision and simplified dynamics, which is validated through extensive simulations that closely replicate experimental conditions, demonstrating the system's ability to handle external disturbances and maintain accurate trajectory tracking. The strategy only requires time and joint position data as inputs, allowing the network to compute velocity and acceleration internally. Time normalization enhances the model's ability to generalize across different time scales and ensures stable training. The method demonstrates strong generalization from a limited training set and successfully performs across diverse trajectory types. This simplification significantly reduces computational complexity and facilitates real-time control in advanced robotic applications. |
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| Keywords: |
Machine Learning in Robotics; Computed torque control; Real-time Applications of Dexterous Manipulation; Neural Network-based Control; DLR-HIT II hand; Normalization method |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors express their sincere gratitude to the Iraqi Ministry of Higher Education for their support. Special thanks to Al-Nahrain University for the financial assistance that enabled this study. We also extend our appreciation to Swansea University, particularly to Professor Johann Sienz, Deputy Executive Dean of Science and Engineering, for providing essential facilities, workspace, and financial support. Additionally, we would like to thank Professor Dr. Dunhui Xiao and Associate Professor Dr. Rowan Brown for reviewing the mathematical aspects of our work and providing valuable feedback as experts in mathematics. |
| Start Page: |
116110 |