具有物理一致性和残差学习功能的物理信息中性网络,用于挖掘机精确操作控制

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chenlong Feng , Jixin Wang , Yuying Shen , Qi Wang , Yi Xiong , Xudong Zhang , Jiuchen Fan
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引用次数: 0

摘要

数据驱动方法可以建立精确的挖掘机逆动力学模型(IDM),从而提高控制精度。然而,这些模型固有的黑箱性质往往会导致对数据集的过度拟合,从而导致预测偏离物理系统的约束条件。因此,这可能会导致控制器失灵,引入不可预测的行为,威胁操作精度。此外,外部干扰的不确定性也对控制器的精度提出了巨大挑战。本研究提出了一种物理信息神经网络,用于构建具有物理一致性的精确 IDM。挖掘机的刚体动力学(RBD)与深拉格朗日网络(DeLaN)相耦合,而卷积神经网络(CNN)和长短期记忆网络(LSTM)则用于同化残余非线性特性,如液压挠性和粘滑摩擦。针对外部干扰的不确定性,构建了与 DeLaN-CNN-LSTM 模型相结合的规定性能反动力学控制器(PPIDC-DCL),通过将控制误差限制在有限区域内实现精确控制。实验结果表明,该模型捕捉到了动态的基本结构,并构建了具有高精度和鲁棒性的 IDM。此外,PPIDC-DCL 控制器有效地限制了控制误差,实现了精确控制。所提出的方法具有潜在的应用价值,并为实现挖掘机的精确运行控制提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-informed neutral network with physically consistent and residual learning for excavator precision operation control
The data-driven methodologies can establish accurate Inverse Dynamics Model (IDM) of the excavator thus improving control precisions. However, the inherent black-box nature of these models often results in overfitting to the dataset, leading to predictions that deviate from the constraints of physical system. Consequently, this can lead to controller failures, introducing unpredictable behavior that threatens operation precision. In addition, the uncertainty of the external disturbance poses great challenge to the precision of controller. This study presents a physics-informed neural network to build accurate IDM with physical consistency. The Rigid Body Dynamics (RBD) of the excavator are coupled within a Deep Lagrangian Network (DeLaN), while a Convolutional Neural Network (CNN) and a Long Short-Term Memory Network (LSTM) are employed to assimilate the residual nonlinear characteristics, such as hydraulic flexibilities and stick–slip friction. To the uncertainty of the external disturbance, the Prescribed Performance Inverse Dynamics Controller combination with the DeLaN-CNN-LSTM model (PPIDC-DCL) is constructed for precise control by constraining the control error within a finite region. The experimental results demonstrate that the model captures the underlying structure of the dynamic and builds the IDM with high accuracy and robustness. Moreover, the PPIDC-DCL controller effectively constrains the control error and realizes precision control. The proposed method has potential applications and provides novel insights for achieving precise operation control of excavators.
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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