拉格朗日动态系统的物理信息深度库普曼算子

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xuefeng Wang, Yang Cao, Shaofeng Chen, Yu Kang
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引用次数: 0

摘要

精确的机械系统模型对于安全稳定的控制至关重要。与线性系统不同,拉格朗日系统具有高度的非线性,并且由于其未知的系统模型而难以优化。因此,最近的研究利用深度神经网络,通过库普曼可观测函数编码器将非线性动态系统映射到线性空间,从而生成原始系统的线性模型。然后,控制器依赖于 Koopman 线性模型。然而,在没有物理信息约束的情况下,要确保原始非线性系统和 Koopman 系统之间的控制一致性非常困难,因为 Koopman 观察函数的学习过程是无监督的。因此,本文提出了一种两阶段学习算法,利用结构子网络构建物理信息网络拓扑,同时学习 Koopman 观察函数和系统能量表示。在 Koopman 矩阵学习环节,需要解决一个二次约束优化问题,以确保 Koopman 表示满足能量差匹配硬约束。所提出的能量守恒深拉格朗日 Koopman(EPDLK)框架能有效地表示拉格朗日系统的动态,同时确保控制的一致性。在多步预测和轨迹跟踪任务中,比较了 EPDLK 与各种 Koopman 可观测函数构建方法的有效性。EPDLK 通过保证能量差匹配实现了更好的控制一致性,这有助于将 Koopman 系统生成的控制法则直接应用于原始非线性拉格朗日系统。
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 deep Koopman operator for Lagrangian dynamic systems

Accurate mechanical system models are crucial for safe and stable control. Unlike linear systems, Lagrangian systems are highly nonlinear and difficult to optimize because of their unknown system model. Recent research thus used deep neural networks to generate linear models of original systems by mapping nonlinear dynamic systems into a linear space with a Koopman observable function encoder. The controller then relies on the Koopman linear model. However, without physical information constraints, ensuring control consistency between the original nonlinear system and the Koopman system is tough, as the learning process of the Koopman observation function is unsupervised. This paper thus proposes a two-stage learning algorithm that uses structural subnetworks to build a physics-informed network topology to simultaneously learn the Koopman observable functions and the system energy representation. In the Koopman matrix learning session, a quadratic-constrained optimization problem is solved to ensure that the Koopman representation satisfies the energy difference matching hard constraint. The proposed energy-preserving deep Lagrangian Koopman (EPDLK) framework effectively represents the dynamics of the Lagrangian system while ensuring control consistency. The effectiveness of EPDLK is compared with those of various Koopman observable function construction methods in multistep prediction and trajectory tracking tasks. EPDLK achieves better control consistency by guaranteeing energy difference matching, which facilitates the application of the control law generated on the Koopman system directly to the original nonlinear Lagrangian system.

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来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
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