未知のペイロードに適応する四足歩行の物理情報ニューラルネットワーク予測制御
Physics-informed Neural Network Predictive Control for Quadruped Locomotion
オンラインでペイロードを同定し、その質量パラメータを物理情報ニューラルネットワークの損失に組み込むことで、未知の積載物があっても四足歩行の追従精度を35%向上させる制御フレームワークを提案した。
著者: Haolin Li, Yikang Chai, Bailin Lv, Lecheng Ruan, Hang Zhao, Ye Zhao, Jianwen Luo
分類: cs.RO
原文アブストラクト
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.
