Identification of Uncertain Parameter in Flight Vehicle Using Physics-Informed Deep Learning

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE Journal of Aerospace Information Systems Pub Date : 2023-10-26 DOI:10.2514/1.i011269
Kyung-Mi Na, Chang-Hun Lee
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Abstract

This paper presents the estimation method for uncertain parameters in flight vehicles, especially missile systems, based on physics-informed neural networks (PINNs) augmented with a novel integration-based loss. The proposed method identifies four types of structured uncertainty: burnout time, rocket motor tilt angle, location of the center of pressure, and control fin bias, which significantly affect the missile performance. In the estimation framework, as neural networks (NNs) are updated, these uncertainties are also identified simultaneously because they are also included in the structure of NNs. After testing 100 simulation data, the average estimation errors are within 1% of the mean value for each type of uncertainty. The methodology is able to identify the parameters despite noise corruption in the time-series data. Compared with the conventional PINNs, adding the new loss based on the integration of differential equations yields a more reliable estimation performance for all types of uncertainty. This approach can be effective for complex systems and ill-posed inverse problems, which makes it applicable to other aerospace systems.
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基于物理信息深度学习的飞行器不确定参数识别
本文提出了一种基于物理信息神经网络(pinn)和一种新的基于积分的损失的飞行器,特别是导弹系统中不确定参数的估计方法。该方法识别了四种结构不确定性:燃尽时间、火箭发动机倾斜角、压力中心位置和控制翼偏差,这四种结构不确定性对导弹性能有显著影响。在估计框架中,随着神经网络的更新,这些不确定性也被同时识别,因为它们也包含在神经网络的结构中。在测试了100个模拟数据后,每种不确定度的平均估计误差都在平均值的1%以内。该方法能够在时间序列数据噪声损坏的情况下识别参数。与传统的pinn相比,加入基于微分方程积分的新损失对所有类型的不确定性具有更可靠的估计性能。该方法对复杂系统和不适定逆问题具有较好的求解效果,适用于其他航空航天系统。
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来源期刊
CiteScore
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
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