学习无需加速度测量的物理一致拉格朗日控制模型
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
AI总结：
本文提出了一种无需加速度测量的学习方法，用于提高拉格朗日系统模型的物理一致性，以提升基于模型的非线性控制性能。
摘要：
本文研究了涉及非保守力的拉格朗日系统建模与控制，采用一种不需要计算加速度的混合方法。特别关注物理一致模型的推导和识别，这对基于模型的控制合成至关重要。拉格朗日或哈密顿神经网络提供有用的结构保证，但此类模型的学习往往导致不一致的模型，尤其是在现实物理系统中，训练数据有限、不完整且嘈杂。受此观察以及利用这些模型进行基于模型非线性控制的目标启发，提出了一种依赖于原创损失函数的学习算法，以提高拉格朗日系统的物理一致性。对不同基于学习的建模方法与所提方案的比较分析显示，在模拟和实验系统中，所学模型的物理一致性显著提高。然后利用模型的一致性，在实验基准上展示所提方法在反馈线性化和基于能量的控制技术中的实际相关性。
英文摘要：
This article investigates the modeling and control of Lagrangian systems involving non-conservative forces using a hybrid method that does not require acceleration calculations. It focuses in particular on the derivation and identification of physically consistent models, which are essential for model-based control synthesis. Lagrangian or Hamiltonian neural networks provide useful structural guarantees but the learning of such models often leads to inconsistent models, especially on real physical systems where training data are limited, partial and noisy. Motivated by this observation and the objective to exploit these models for model-based nonlinear control, a learning algorithm relying on an original loss function is proposed to improve the physical consistency of Lagrangian systems. A comparative analysis of different learning-based modeling approaches with the proposed solution shows significant improvements in terms of physical consistency of the learned models, on both simulated and experimental systems. The model's consistency is then exploited to demonstrate, on an experimental benchmark, the practical relevance of the proposed methodology for feedback linearization and energy-based control techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
