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随机非线性系统基于事件触发机制的自适应神经网络控制

王桐 邱剑彬 高会军

王桐, 邱剑彬, 高会军. 随机非线性系统基于事件触发机制的自适应神经网络控制. 自动化学报, 2019, 45(1): 226-233. doi: 10.16383/j.aas.2018.c180404
引用本文: 王桐, 邱剑彬, 高会军. 随机非线性系统基于事件触发机制的自适应神经网络控制. 自动化学报, 2019, 45(1): 226-233. doi: 10.16383/j.aas.2018.c180404
WANG Tong, QIU Jian-Bin, GAO Hui-Jun. Event-triggered Adaptive Neural Network Control for a Class of Stochastic Nonlinear Systems. ACTA AUTOMATICA SINICA, 2019, 45(1): 226-233. doi: 10.16383/j.aas.2018.c180404
Citation: WANG Tong, QIU Jian-Bin, GAO Hui-Jun. Event-triggered Adaptive Neural Network Control for a Class of Stochastic Nonlinear Systems. ACTA AUTOMATICA SINICA, 2019, 45(1): 226-233. doi: 10.16383/j.aas.2018.c180404

随机非线性系统基于事件触发机制的自适应神经网络控制

doi: 10.16383/j.aas.2018.c180404
基金项目: 

黑龙江省青年科学基金 QC2018077

博士后创新人才支持计划 BX201700067

国家自然科学基金 61873311

中国博士后科学基金面上项目 2018M630359

国家自然科学基金 61803122

高等学校学科创新引智计划项目 B16014

详细信息
    作者简介:

    王桐  哈尔滨工业大学讲师.主要研究方向为非线性系统的自适应控制.E-mail:twang@hit.edu.cn

    邱剑彬  哈尔滨工业大学教授.主要研究方向为非线性系统的模糊控制.E-mail:jbqiu@hit.edu.cn

    通讯作者:

    高会军   哈尔滨工业大学教授.主要研究方向为网络化控制.本文通信作者.E-mail:hjgao@hit.edu.cn

Event-triggered Adaptive Neural Network Control for a Class of Stochastic Nonlinear Systems

Funds: 

Heilongjiang Province Science Foundation for Youths QC2018077

National Postdoctoral Program for Innovative Talents BX201700067

National Natural Science Foundation of China 61873311

China Postdoctoral Science Foundation Grant 2018M630359

National Natural Science Foundation of China 61803122

the 111 Project B16014

More Information
    Author Bio:

       Lecturer at Harbin Institute of Technology. His main research interest is adaptive control for nonlinear systems

       Professor at Harbin Institute of Technology. His main research interest is fuzzy control for nonlinear systems

    Corresponding author: GAO Hui-Jun    Professor at Harbin Institute of Technology. His research interest covers networked control systems. Corresponding author of this paper
  • 摘要: 针对一类具有严格反馈结构且控制方向未知的随机非线性系统,提出了基于事件触发机制的自适应神经网络(Adaptive neural network,ANN)输出反馈控制方法.利用径向基神经网络逼近系统中未知的非线性函数.通过引入Nussbaum增益函数并设计滤波器,解决了系统控制方向未知的问题.通过设计具有相对阈值的事件触发机制,保证了闭环随机非线性系统的有界性.最后给出数值仿真例子验证所提控制方法的有效性.
    1)  本文责任编委 鲁仁全
  • 图  1  系统的跟踪和观测性能

    Fig.  1  Output tracking and observation performance

    图  2  控制信号

    Fig.  2  Control signals

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出版历程
  • 收稿日期:  2018-06-08
  • 录用日期:  2018-08-27
  • 刊出日期:  2019-01-20

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