Protocol-based set-membership state estimation for linear repetitive processes with uniform quantization: a zonotope-based approach

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Minghao Gao, Pengfei Yang, Hailong Tan, Qi Li
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引用次数: 0

Abstract

This paper is concerned with the zonotopic state estimation problem for a class of linear repetitive processes (LRPs) with weighted try-once-discard protocols (WTODPs) subject to uniform quantization. In such a system, the process disturbance and measurement noise are generally assumed to be unknown but bounded in certain zonotopes. The measurement data are uniformly quantized prior to entering the network. In order to effectively curb data collision, a WTODP is considered, based on which only the selected sensor is allowed to transmit the data through network. The aim of this paper is to find a zonotope that covers all possible states consistent with the system model and WTODP-based measured outputs. By using the zonotope properties, a zonotope containing all possible states is first constructed whose size is then minimized by designing an appropriate correlation matrix. Moreover, a sufficient condition is offered for the existence of an upper bound on the size of this zonotope. At last, we valid the efficacy of the developed estimation approach via an illustrate example.

基于协议的均匀量化线性重复过程的集合成员状态估计:一种基于区顶的方法
本文研究的是一类线性重复过程(LRPs)的区位点状态估计问题,该过程采用加权试一弃协议(WTODPs),受均匀量化约束。在这种系统中,过程干扰和测量噪声通常被假定为未知的,但在某些区位上是有界的。测量数据在进入网络之前被统一量化。为了有效遏制数据碰撞,本文考虑了一种 WTODP,在此基础上,只允许选定的传感器通过网络传输数据。本文的目的是找到一个能涵盖与系统模型和基于 WTODP 的测量输出相一致的所有可能状态的 zonotope。首先利用区带特性构建一个包含所有可能状态的区带,然后通过设计适当的相关矩阵使其大小最小化。此外,我们还提出了一个充分条件,即这个带状区域的大小存在一个上限。最后,我们通过一个示例验证了所开发的估算方法的有效性。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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