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arXiv 2401.04660eess.SYcs.SY
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分布式数据驱动未知输入观测器用于状态估计

Distributed Data-driven Unknown-input Observers for State Estimation

Yuzhou Wei, Giorgia Disarò, Wenjie Liu, Jian Sun, Maria Elena Valcher, Gang Wang

AI总结:

本文针对未知连续时间LTI系统提出一种仅依赖离线数据、无需系统矩阵先验知识的数据驱动分布式未知输入观测器(D-DUIO),给出其存在条件和矩阵构造方法,并通过数值算例验证其有效性。

摘要:

与传感器老化、建模误差或设备偏置等相关的未知输入是无线传感器网络中的一个主要问题,因为它们会降低状态估计性能。为了提高性能,人们提出了未知输入观测器(UIOs)。现有大多数UIO设计结果都基于显式系统模型,而在实际应用中这些模型可能难以获得甚至无法获得。另一方面,数据驱动技术已成为仅利用数据来设计和分析未知系统的可行替代方案。在此背景下,本文针对未知连续时间线性时不变(LTI)系统提出了一种新型的数据驱动分布式未知输入观测器(D-DUIO),该方法仅需要离线采集的一些数据,无需系统矩阵的任何先验知识。本文首先给出了一种基于模型的方法来设计分布式未知输入观测器(DUIO),回顾了此类DUIO存在的一个充分条件,并提出了一个新的充分条件,该条件易于进行数据驱动适配。转向数据驱动方法后,本文证明,在对从连续时间系统采集的输入/输出/状态数据作出适当假设的情况下,既可以断言D-DUIO的存在性,又可以根据预先采集数据的矩阵推导出D-DUIO的矩阵。最后,通过数值算例验证了D-DUIO的有效性。

英文摘要:

Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performance. To improve the performance, unknown-input observers (UIOs) have been proposed. Most of the results available to design UIOs are based on explicit system models, which can be difficult or impossible to obtain in real-world applications. Data-driven techniques, on the other hand, have become a viable alternative for the design and analysis of unknown systems using only data. In this context, a novel data-driven distributed unknown-input observer (D-DUIO) for unknown continuous-time linear time-invariant (LTI) systems is developed, which requires solely some data collected offline, without any prior knowledge of the system matrices. In the paper, first, a model-based approach to the design of a DUIO is presented. A sufficient condition for the existence of such a DUIO is recalled, and a new one is proposed, that is prone to a data-driven adaption. Moving to a data-driven approach, it is shown that under suitable assumptions on the input/output/state data collected from the continuous-time system, it is possible to both claim the existence of a D-DUIO and to derive its matrices in terms of the matrices of pre-collected data. Finally, the efficacy of the D-DUIO is illustrated by means of numerical examples.

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。

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