Joint unknown input observer for descriptor system based on interval observer

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

Abstract

In this paper, a novel joint unknown input observer (JUIO) is proposed for a class of descriptor systems. The unknown input (UI) to be estimated injects additively into both the state and output equations in a state space model. To the best of our knowledge, only a few contributions in existing work address this problem directly. To begin with, by introducing an auxiliary UI, the original system is transformed into a normal form in which the output is no longer affected by UI. In this way, the negative effect brought by the UI occurring in the output measurement is removed. An interval observer is developed to obtain upper and lower boundary estimates of the output of the reformulated system. After that, an algebraic relationship between the auxiliary UI and the states is established, and a UI reconstruction (UIR) method is developed. Based on the UIR, a JUIO comprising the UIR and a Luenberger-like state observer is developed to achieve asymptotic estimations of the UI and state simultaneously. Verifiable conditions for the existence of the proposed JUIO are given with respect to the original descriptor system. Finally, a simulation example is presented to verify the effectiveness of the proposed method.

基于区间观测器的描述符系统联合未知输入观测器
本文针对一类描述符系统提出了一种新型联合未知输入观测器(JUIO)。需要估算的未知输入(UI)以加法形式注入状态空间模型中的状态方程和输出方程。据我们所知,现有研究中只有少数几项成果直接解决了这一问题。首先,通过引入辅助 UI,原始系统被转换为正常形式,其中输出不再受 UI 影响。这样,输出测量中出现的 UI 带来的负面影响就被消除了。为了获得重构系统输出的上下界估计值,我们开发了一种区间观测器。然后,建立辅助 UI 与状态之间的代数关系,并开发出 UI 重构(UIR)方法。在 UIR 的基础上,开发了一种由 UIR 和类卢恩贝格尔状态观测器组成的 JUIO,以同时实现对 UI 和状态的渐近估计。针对原始描述符系统,给出了所提出的 JUIO 存在的可验证条件。最后,介绍了一个仿真实例,以验证所提方法的有效性。
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
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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