极小极大
估计员
稳健性(进化)
数学优化
计算机科学
控制理论(社会学)
观察员(物理)
非线性系统
噪音(视频)
情态动词
数学
人工智能
统计
基因
图像(数学)
物理
量子力学
生物化学
化学
高分子化学
控制(管理)
作者
Jean-Sébastien Brouillon,Florian Dörfler,Giancarlo Ferrari-Trecate
出处
期刊:IEEE Control Systems Letters
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/lcsys.2023.3287803
摘要
The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfill the assumptions of existing approaches. We provide a direct method using samples of the noise to create a moving horizon observer for linear time-varying and nonlinear systems, which is optimal under the empirical noise distribution. Moreover, we show how to enhance the observer with distributional robustness properties in order to handle unmodeled components in the noise profile, as well as different noise realizations. We prove that, even though the design of distributionally robust estimators is a complex minmax problem over an infinite-dimensional space, it can be transformed into a regularized linear program using a system level synthesis approach. Numerical experiments with the Van der Pol oscillator show the benefits of not only using empirical samples of the noise to design the state estimator, but also of adding distributional robustness. We show that our method can significantly outperform state-of-the-art approaches under challenging noise distributions, including multi-modal and deterministic components.
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