Integrated Fault Estimation and Fault-Tolerant Control for Switched Stochastic Systems With Unknown Input

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-11-21 DOI:10.1002/rnc.7726
Jiuling Zhang, Jian Han, Xiuhua Liu, Xinjiang Wei, Xiaodan Zhu
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Abstract

In this paper, fault estimation (FE) and fault-tolerant control (FTC) are studied for switched stochastic nonlinear systems with unknown input and faults. The dynamic unknown input observer (DUIO) and disturbance observer are designed, and the system state, faults, and disturbances can be reconstructed. The DUIO designed in this paper is the extension of the traditional unknown input observer (UIO) and the proportional-integral observer. The unknown input, which contains unmodeled disturbance, uncertainty, can be decoupled by the estimation error system. A dynamic output feedback controller based on observers is designed to compensate for actuator fault and modelable disturbance. The integration scheme of FE and FTC is designed, and the one-step linear matrix inequalities (LMIs) conditions are constructed, which avoids the coupling of observer and controller parameters. The root-mean-square (RMS) gain is used to attenuate the unknown input and unmodelable disturbances in the closed-loop systems, which can remove some common assumptions. Finally, the feasibility of the proposed method is demonstrated through simulations.

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未知输入切换随机系统的综合故障估计与容错控制
研究了具有未知输入和故障的切换随机非线性系统的故障估计和容错控制问题。设计了动态未知输入观测器和扰动观测器,实现了系统状态、故障和扰动的重构。本文设计的未知输入观测器是传统未知输入观测器和比例积分观测器的扩展。未知输入包含未建模的干扰和不确定性,可以通过估计误差系统进行解耦。设计了一种基于观测器的动态输出反馈控制器来补偿执行器故障和可建模干扰。设计了FE与FTC的积分方案,构造了一步线性矩阵不等式(LMIs)条件,避免了观测器参数与控制器参数的耦合。均方根增益(RMS)用于衰减闭环系统中的未知输入和不可建模干扰,可以消除一些常见的假设。最后,通过仿真验证了所提方法的可行性。
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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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