Event-triggered state estimation for networked control systems with silent packet loss and coloured noise

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Chang Zhao , Yamin Wang , Ka-Wai Kwok , Xiaoling Liu
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引用次数: 0

Abstract

This paper focuses on jointly designing a scheduler, detector, and estimator for networked control systems with silent packet loss (SPL) and coloured noise. A truncated Gaussian distribution emerges in the state estimator calculating process due to the event-triggered scheduling mechanism. Unfortunately, this distribution leads to the absence of an analytical expression in the derivation process, necessitating approximating the truncated Gaussian distribution as a Gaussian distribution within the design of the optimal estimator (OE). To overcome this issue, this paper implements a stochastic event-triggered scheduling mechanism. Moreover, a detector is devised to identify packet loss occurrences, thereby improving the estimation performance. Built upon the framework, an OE estimator is formulated. Then, a lower bound is established for the communication rate, and a necessary condition is obtained for the stability of the OE estimator in stable and unstable systems. In the end, numerical examples are provided to verify the effectiveness of theoretical results.
具有无声丢包和彩色噪声的网络控制系统的事件触发状态估计
本文重点研究了一种具有无声丢包和彩色噪声的网络控制系统的调度、检测器和估计器的联合设计。由于事件触发调度机制,在状态估计器的计算过程中出现了截断的高斯分布。不幸的是,这种分布导致在推导过程中缺乏解析表达式,需要在最优估计器(OE)的设计中将截断的高斯分布近似为高斯分布。为了克服这一问题,本文实现了一种随机事件触发调度机制。此外,还设计了一个检测器来识别丢包事件,从而提高了估计性能。建立在框架之上的OE估计器是公式化的。然后,建立了通信速率的下界,得到了在稳定和不稳定系统中OE估计量稳定的必要条件。最后通过数值算例验证了理论结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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