具有传感器故障的离散时间多代理系统的故障检测和容错控制:数据驱动方法

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ji Zhang;Linlin Ma;Jingbo Zhao;Yanzheng Zhu
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引用次数: 0

摘要

本文研究了具有传感器故障的离散时间多代理系统(MAS)的故障检测和容错控制(FTC)问题。首先,介绍了动态线性化方法来描述带有传感器故障的未知 MAS。随后,提出了一种基于数据驱动观测器的分散式故障检测方法。并设计了一种基于 RBF 神经网络的故障估计器,用于估计多个传感器故障。然后,在所设计的估计器基础上,提供了一种分布式无模型滑模 FTC 策略,以确保所考虑的 MAS 在遭受特定传感器故障时的稳定性。最后,通过一个仿真实例说明了所提方法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Detection and Fault-Tolerant Control for Discrete-Time Multiagent Systems With Sensor Faults: A Data-Driven Method
This article investigates the fault detection and fault-tolerant control (FTC) problems for discrete-time multiagent systems (MASs) with sensor faults. First, the dynamic linearization method is introduced to describe the unknown MASs with sensor faults. Afterward, a decentralized fault detection method based on data-driven observers is proposed. And a fault estimator based on RBF neural networks for estimating multiple sensor faults is designed for estimating multiple sensor faults. Then, on the basis of the designed estimator, a distributed model-free sliding mode FTC strategy is provided to ensure the stability of the considered MASs when suffering from certain sensor faults. Finally, a simulated example is used to illustrate the efficiency of the proposed method.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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