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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.06995 |
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| _version_ | 1866929751321477120 |
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| author | Li, Haolin Chai, Yikang Lv, Bailin Ruan, Lecheng Zhao, Hang Zhao, Ye Luo, Jianwen |
| author_facet | Li, Haolin Chai, Yikang Lv, Bailin Ruan, Lecheng Zhao, Hang Zhao, Ye Luo, Jianwen |
| contents | This study introduces a unified control framework that addresses the challenge of precise quadruped locomotion with unknown payloads, named as online payload identification-based physics-informed neural network predictive control (OPI-PINNPC). By integrating online payload identification with physics-informed neural networks (PINNs), our approach embeds identified mass parameters directly into the neural network's loss function, ensuring physical consistency while adapting to changing load conditions. The physics-constrained neural representation serves as an efficient surrogate model within our nonlinear model predictive controller, enabling real-time optimization despite the complex dynamics of legged locomotion. Experimental validation on our quadruped robot platform demonstrates 35% improvement in position and orientation tracking accuracy across diverse payload conditions (25-100 kg), with substantially faster convergence compared to previous adaptive control methods. Our framework provides a adaptive solution for maintaining locomotion performance under variable payload conditions without sacrificing computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed Neural Network Predictive Control for Quadruped Locomotion Li, Haolin Chai, Yikang Lv, Bailin Ruan, Lecheng Zhao, Hang Zhao, Ye Luo, Jianwen Robotics This study introduces a unified control framework that addresses the challenge of precise quadruped locomotion with unknown payloads, named as online payload identification-based physics-informed neural network predictive control (OPI-PINNPC). By integrating online payload identification with physics-informed neural networks (PINNs), our approach embeds identified mass parameters directly into the neural network's loss function, ensuring physical consistency while adapting to changing load conditions. The physics-constrained neural representation serves as an efficient surrogate model within our nonlinear model predictive controller, enabling real-time optimization despite the complex dynamics of legged locomotion. Experimental validation on our quadruped robot platform demonstrates 35% improvement in position and orientation tracking accuracy across diverse payload conditions (25-100 kg), with substantially faster convergence compared to previous adaptive control methods. Our framework provides a adaptive solution for maintaining locomotion performance under variable payload conditions without sacrificing computational efficiency. |
| title | Physics-informed Neural Network Predictive Control for Quadruped Locomotion |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.06995 |