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arXiv · 2503.06995

Physics-informed Neural Network Predictive Control for Quadruped Locomotion

Haolin Li·Yikang Chai·Bailin Lv·Lecheng Ruan·Hang Zhao·Ye Zhao·Jianwen Luo
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Abstract

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.

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Haolin Li, Yikang Chai, Bailin Lv, Lecheng Ruan, Hang Zhao, Ye Zhao, Jianwen Luo. 2025-03-10. Physics-informed Neural Network Predictive Control for Quadruped Locomotion. https://arxiv.org/abs/2503.06995

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