引用本文:易泽仁,谢巍,刘龙文,胥布工.一类非线性系统的神经网络自适应区间观测器设计[J].控制理论与应用,2023,40(10):1730~1736.[点击复制]
YI Ze-ren,XIE Wei,LIU Long-wen,XU Bu-gong.A neural network adaptive interval observer design for a class of nonlinear systems[J].Control Theory and Technology,2023,40(10):1730~1736.[点击复制]
一类非线性系统的神经网络自适应区间观测器设计
A neural network adaptive interval observer design for a class of nonlinear systems
摘要点击 950  全文点击 269  投稿时间：2021-06-02  修订日期：2023-04-17
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DOI编号  10.7641/CTA.2022.10471
  2023,40(10):1730-1736
中文关键词  区间观测器  径向基函数神经网络  非线性系统  梅茨勒矩阵
英文关键词  interval observer  radial basis function neural network  nonlinear systems  Metzler matrix
基金项目  国家自然科学基金项目(61973125), 佛山市重点领域科技攻关项目(2020001006812)
作者单位E-mail
易泽仁 华南理工大学 yizeren_123@163.com 
谢巍* 华南理工大学 weixie@scut.edu.cn 
刘龙文 华南理工大学  
胥布工 华南理工大学  
中文摘要
      本文研究了一类单输入单输出非线性系统的神经网络自适应区间观测器设计问题. 针对由状态和输入所描述的未知非线性函数的界不可测, 现有的区间观测器方法并未有效地处理系统含有参数不确定性的未知非线性函数. 首先, 本文构造两个径向基函数神经网络来逼近未知非线性部分, 进而分别估计系统状态的上下界; 然后, 选择合适的Lyapunov函数, 采用网络权值校正和网络误差选择机制确保所设计的误差动态系统有界和非负性, 并证明了神经网络自适应区间观测器的稳定性; 最后, 通过仿真实例验证了所提出的神经网络自适应区间观测器的有效性.
英文摘要
      The problem in designing a neural network adaptive interval observer for a class of single-input single-output nonlinear systems is considered in this paper. The bounds of unknown nonlinear functions described by the state and the input are unmeasurable, so that the existing interval observers are not effective in dealing with unknown nonlinear functions with parameter uncertainty in their systems. In this work, two radial basis function (RBF) neural networks are constructed to approximate the unknown nonlinear part, and then the upper and lower bounds of the system state are estimated, respectively. After chosen a suitable Lyapunov function, network weight correction and network error selection mechanisms are given, which are used to make sure the designed error dynamic system is bounded and non-negative. Furthermore, the stability of the neural network adaptive interval observer is proved. Finally, a numerical simulation example is applied to verify the effectiveness of the proposed neural network adaptive interval observer.