Stubborn State and Disturbance Observer Co-Design for Nonlinear Descriptor Systems With δ QC $$ \delta \mathrm{QC} $$ via a Dynamic Event-Triggered Mechanism

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-10-15 DOI:10.1002/rnc.7667
Leipo Liu, Qiaofeng Wen, Yanan Li, Dexin Fu, Xiushan Cai
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Abstract

This article studies the state and disturbance simultaneous estimation problem for a class of nonlinear descriptor systems with δ QC $$ \delta \mathrm{QC} $$ using a dynamic event-triggered mechanism. δ QC $$ \delta \mathrm{QC} $$ refers to incremental quadratic constraints, which can provide a unified description of many types of common nonlinear functions. To reduce the negative impact of measurement outliers on the identification estimation, a stubborn state and disturbance observer co-design scheme is proposed for the first time by embedding dynamic saturation output estimation errors. Meanwhile, a dynamic event-triggered mechanism is introduced to avoid the need for continuously available output information, which can reduce the pressure on communication resources. By constructing a Lyapunov function, existence conditions of the stubborn state and disturbance observer are obtained in the form of a convex optimization problem so that the error estimation maintains an acceptable estimation performance. Finally, simulation examples illustrate the universality and stubbornness of the proposed observer.

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基于动态事件触发机制的δ QC $$ \delta \mathrm{QC} $$非线性广义系统顽固状态与扰动观测器协同设计
本文利用动态事件触发机制研究了一类具有δ QC $$ \delta \mathrm{QC} $$的非线性广义系统的状态和扰动同时估计问题。δ QC $$ \delta \mathrm{QC} $$指增量二次约束,可以对多种常见的非线性函数提供统一的描述。为了减少测量异常值对辨识估计的负面影响,首次提出了一种嵌入动态饱和输出估计误差的顽固状态与干扰观测器协同设计方案。同时,引入动态事件触发机制,避免了对连续可用输出信息的需求,减少了通信资源的压力。通过构造Lyapunov函数,以凸优化问题的形式得到了顽固性状态和扰动观测器的存在条件,使误差估计保持可接受的估计性能。最后,仿真实例说明了所提观测器的通用性和顽固性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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