液压与气动2026，Vol.50Issue(2)：84-93,10.DOI:10.11832/j.issn.1000-4858.2026.02.009
基于物理信息神经网络的电液执行器建模方法
Modeling Method of Electro-hydraulic Actuators Based on Physics-informed Neural Networks
摘要
Abstract
Modeling electro-hydraulic actuators is challenging due to their strong nonlinearities and unobservable internal states.To address issues such as low modeling accuracy and poor generalization,we propose an improved physics-informed neural network modeling method.First,a one-dimensional convolutional neural network module is employed to extract temporal features from sensor data.Subsequently,the force balance equation derived from electro-hydraulic actuator dynamics is embedded into the loss function as a physical constraint.This mechanism compensates for the poor interpretability of pure data-driven models and accelerates convergence.Furthermore,to mitigate the interference of sensor noise on physical constraint calculations,a signal smoothing strategy based on local linear fitting is designed.The multi-condition experiments demonstrate that this method effectively balances data fitting with physical consistency.Compared with traditional models,the proposed approach significantly improves prediction accuracy and robustness under limited data conditions.关键词
电液执行器/物理信息神经网络/一维卷积/力平衡方程/非线性建模Key words
electro-hydraulic actuator/physics-informed neural network/one-dimensional convolutional/force balance equation/nonlinear modeling分类
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林子彦,李晓明..基于物理信息神经网络的电液执行器建模方法[J].液压与气动,2026,50(2):84-93,10.基金项目
浙江省重点研发计划(2025C03013) （2025C03013）
