引用本文:杨俊起,朱芳来.未知输入和可测噪声重构之线性矩阵不等式非线性系统观测器设计[J].控制理论与应用,2014,31(4):538~544.[点击复制]
YANG Jun-qi,ZHU Fang-lai.Linear-matrix-inequality observer design of nonlinear systems with unknown input and measurement noise reconstruction[J].Control Theory and Technology,2014,31(4):538~544.[点击复制]
未知输入和可测噪声重构之线性矩阵不等式非线性系统观测器设计
Linear-matrix-inequality observer design of nonlinear systems with unknown input and measurement noise reconstruction
摘要点击 2546  全文点击 1973  投稿时间：2013-05-15  修订日期：2013-11-09
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DOI编号  10.7641/CTA.2014.30481
  2014,31(4):538-544
中文关键词  未知输入观测器  广义系统  滑模微分器  线性矩阵不等式
英文关键词  unknown input observer  generalized system  sliding mode differentiator  linear-matrix-inequality
基金项目  国家自然科学基金资助项目(61074009); 高等学校博士学科点专项科研基金资助项目(20110072110015); 广西制造系统与先进制造技术重点实验室资助项目(PF110289); 上海市重点学科资助项目(B004); 河南省教育厅科学技术研究重点项目(13B413035,13B413028).
作者单位E-mail
杨俊起 同济大学 电子与信息工程学院
河南理工大学 电气工程与自动化学院 		yjq@hpu.edu.cn 
朱芳来* 同济大学 电子与信息工程学院 zhufanglai@tongji.edu.cn 
中文摘要
      针对一类同时具有未知输入和输出可测噪声的Lipschitz非线性系统, 讨论了状态估计、未知输入与可测噪声重构的问题．首先, 基于广义系统和线性矩阵不等式的方法设计滑模未知输入观测器, 不仅对原系统状态进行渐近估计, 而且实现了对系统输出可测噪声的重构; 其次, 考虑一种鲁棒滑模微分器, 实现了广义系统输出向量微分的精确估计, 并在此基础上, 提出了一种未知信息重构方法, 该方法具有避免直接使用系统输出微分信息的优点. 最后, 对火车牵引拖动系统模型仿真, 结果表明该方法不但能够实现对系统状态的估计, 而且可以有效重构未知信息.
英文摘要
      This paper investigates the unknown-input observer design for a class of nonlinear systems with both unknown inputs and measurement noise. First, based on the techniques of linear matrix inequality and generalized systems, the original nonlinear system is transformed into an augmented generalized system, and a robust sliding-mode observer which can estimate both the states and measurement noise vector of the original system is developed. Second, a robust slidingmode differentiator is considered to exactly estimate the derivative of the output vector of the augmented generalized system, and a kind of unknown information reconstruction method which can be used to estimated unknown inputs of original system is proposed. The proposed method avoids using the derivative information of system output. Finally, a train system model is used to illustrate the effectiveness of the proposed methods which not only estimates the states of the original system, but also reconstructs the unknown input and measurement noise.