用伺服机制驱动的物理系统，可精确建模动力学行为。
ServoLNN: Lagrangian Neural Networks Driven by Servomechanisms
- 将伺服控制机制嵌入拉格朗日神经网络，实现动态系统建模。
- 可同时预测能量、功率、加速度及驱动力等关键物理量。
- 适合实时控制场景，支持即刻获取驱动信号的应用。
将深度学习与经典物理结合，可高效构建精确的动力学模型。近年来一类神经网络将拉格朗日力学硬编码于架构中，训练过程可学习系统行为。然而现有架构难以建模由伺服机制（如伺服电机、步进电机、电流源、容积泵）驱动的动力系统。本文提出ServoLNN，一种专为伺服驱动系统设计的新架构，兼容实时应用，可在驱动运动仅即时可知时进行建模。提供了PyTorch实现。推导与结果揭示训练可能收敛至一类解，分析了该解族对预测物理量的影响，并提出方法将其缩减为唯一解。最终架构能同步准确求解能量、功率、做功速率、质量矩阵、广义加速度、广义力以及驱动伺服系统的广义力。
原文摘要 · Abstract (English)
Combining deep learning with classical physics facilitates the efficient creation of accurate dynamical models. In a recent class of neural network, Lagrangian mechanics is hard-coded into the architecture, and training the network learns the given system. However, the current architectures do not facilitate the modelling of dynamical systems that are driven by servomechanisms (e.g. servomotors, stepper motors, current sources, volumetric pumps). This article presents ServoLNN, a new architecture to model dynamical systems that are driven by servomechanisms. ServoLNN is compatible for use in real-time applications, where the driving motion is known only just-in-time. A PyTorch implementation of ServoLNN is provided. The derivations and results reveal the occurrence of a possible family of solutions that the training may converge on. The effect of the family of solutions on the predicted physical quantities is explored, as is the resolution to reduce the family of solutions to a single solution. Resultantly, the architecture can simultaneously accurately find the energies, power, rate of work, mass matrix, generalised accelerations, generalised forces, and the generalised forces that drive the servomechanisms.
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