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基于滑模观测器和广义观测器的故障估计方法

文传博 邓露 吴兰

李娟, 王宇平. 基于样本密度和分类误差率的增量学习矢量量化算法研究. 自动化学报, 2015, 41(6): 1187-1200. doi: 10.16383/j.aas.2015.c140311
引用本文: 文传博, 邓露, 吴兰. 基于滑模观测器和广义观测器的故障估计方法. 自动化学报, 2018, 44(9): 1698-1705. doi: 10.16383/j.aas.2017.c160730
LI Juan, WANG Yu-Ping. An Incremental Learning Vector Quantization Algorithm Based on Pattern Density and Classification Error Ratio. ACTA AUTOMATICA SINICA, 2015, 41(6): 1187-1200. doi: 10.16383/j.aas.2015.c140311
Citation: WEN Chuan-Bo, DENG Lu, WU Lan. Fault Estimation Approaches With Sliding Mode Observer and Descriptor Observer. ACTA AUTOMATICA SINICA, 2018, 44(9): 1698-1705. doi: 10.16383/j.aas.2017.c160730

基于滑模观测器和广义观测器的故障估计方法

doi: 10.16383/j.aas.2017.c160730
基金项目: 

国家自然科学基金 61473159

上海市人才发展资金 201511

上海电机学院登峰计划 15DFXK01

国家自然科学基金 61374136

详细信息
    作者简介:

    邓露 上海电机学院电气学院硕士研究生.主要研究方向为基于模型的故障诊断.E-mail:18721615736@163.com

    吴兰 河南工业大学电气工程学院教授.主要研究方向为智能故障诊断, 系统建模与控制.E-mail:richod@126.com

    通讯作者:

    文传博 上海电机学院电气学院副教授.2015年获得上海交通大学控制理论与控制工程博士学位.主要研究方向为故障诊断, 数据融合.本文通信作者.E-mail:chuanbowen@163.com

Fault Estimation Approaches With Sliding Mode Observer and Descriptor Observer

Funds: 

National Natural Science Foundation of China 61473159

Developing Foundation for Talents of Shanghai 201511

Climbing Peak Discipline Project of Shanghai Dianji University 15DFXK01

National Natural Science Foundation of China 61374136

More Information
    Author Bio:

    Master student at the College of Electrical Engineering, Shanghai Dianji University. Her reserach interest covers model-based fault diagnosis

    Professor at the College of Electrical Engineering, Henan University of Technology. Her research interest covers intelligent fault diagnosis, system modeling, and control

    Corresponding author: WEN Chuan-Bo Associate professor at the College of Electrical Engineering, Shanghai Dianji University. He received his Ph. D. degree from Shanghai Jiao Tong University in 2015. His research interest covers fault diagnosis and data fusion. Corresponding author of this paper
  • 摘要: 针对受未知干扰影响的一类非线性系统,提出一种基于滑模观测器和广义观测器的执行器故障和传感器故障估计方法.首先通过线性变换将原系统解耦为两个降阶的子系统,其中一个子系统受执行器故障和干扰的影响,另一个含有传感器故障和干扰,进一步将后一个子系统转化为广义系统.对两类子系统分别设计滑模观测器和广义观测器,给出估计误差一致最终有界的条件,得到系统状态和未知干扰的估计值.然后,利用等效输出控制原理重构执行器故障,引入干扰补偿保证重构算法的鲁棒性,再根据广义观测器的结果获得传感器故障的估计值.最后,通过计算机仿真验证了本文方法的有效性.

  • 本文责任编委 文成林
  • 图  1  状态$x_1$ 的真实值与估计值

    Fig.  1  The actual state and the estimate of $x_1$

    图  2  状态$x_2$ 的真实值与估计值

    Fig.  2  The actual state and the estimate of $x_2$

    图  3  状态$x_3$ 的真实值与估计值

    Fig.  3  The actual state and the estimate of $x_3$

    图  4  故障$f_s$ 的真实值与估计值

    Fig.  4  The actual state and the estimate of fault $f_s$

    图  5  干扰$\zeta$ 的真实值与估计值

    Fig.  5  The actual state and the estimate of disturbance $\zeta$

    图  6  故障$f_a$ 的真实值与估计值

    Fig.  6  The actual state and the estimate of fault $f_a$

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