Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots
The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural netw...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/14/1/14 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589570961571840 |
|---|---|
| author | Tingfeng Li Tengfei Xiao |
| author_facet | Tingfeng Li Tengfei Xiao |
| author_sort | Tingfeng Li |
| collection | DOAJ |
| description | The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a loss function that follows both the physical model constraints and the vibration modal conditions, we identify optimal input-shaping parameters to minimize residual vibration. With the use of powerful computational resources to handle multimode information about the vibration, the PINN-based approach outperforms traditional input-shaping methods in terms of computational efficiency and performance. Extensive simulations are carried out to validate the effectiveness of the method and highlight its potential for complex control tasks in flexible robotic systems. |
| format | Article |
| id | doaj-art-c89481101bb54ad8ab39b4ebd9163029 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-c89481101bb54ad8ab39b4ebd91630292025-01-24T13:15:10ZengMDPI AGActuators2076-08252025-01-011411410.3390/act14010014Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link RobotsTingfeng Li0Tengfei Xiao1School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, ChinaThe vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a loss function that follows both the physical model constraints and the vibration modal conditions, we identify optimal input-shaping parameters to minimize residual vibration. With the use of powerful computational resources to handle multimode information about the vibration, the PINN-based approach outperforms traditional input-shaping methods in terms of computational efficiency and performance. Extensive simulations are carried out to validate the effectiveness of the method and highlight its potential for complex control tasks in flexible robotic systems.https://www.mdpi.com/2076-0825/14/1/14physics-informed neural network (PINN)input shapingflexible robotic armsvibration suppressionmodal analysis |
| spellingShingle | Tingfeng Li Tengfei Xiao Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots Actuators physics-informed neural network (PINN) input shaping flexible robotic arms vibration suppression modal analysis |
| title | Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots |
| title_full | Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots |
| title_fullStr | Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots |
| title_full_unstemmed | Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots |
| title_short | Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots |
| title_sort | physics informed neural network based input shaping for vibration suppression of flexible single link robots |
| topic | physics-informed neural network (PINN) input shaping flexible robotic arms vibration suppression modal analysis |
| url | https://www.mdpi.com/2076-0825/14/1/14 |
| work_keys_str_mv | AT tingfengli physicsinformedneuralnetworkbasedinputshapingforvibrationsuppressionofflexiblesinglelinkrobots AT tengfeixiao physicsinformedneuralnetworkbasedinputshapingforvibrationsuppressionofflexiblesinglelinkrobots |