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离散时间非线性系统的数据驱动无模型自适应迭代学习控制(英文)
  • ISSN号:1000-8152
  • 期刊名称:《控制理论与应用》
  • 时间:0
  • 分类:TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构:School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Advanced Control Systems Lab, School of Electronics and Information Engineering, Beijing Jiaotong University
  • 相关基金:supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009);High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30)
中文摘要:

Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.

英文摘要:

Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.

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期刊信息
  • 《控制理论与应用》
  • 北大核心期刊(2011版)
  • 主管单位:国家教育部
  • 主办单位:华南理工大学 中国科学院数学与系统科学研究院
  • 主编:胡跃明
  • 地址:广州五山路华南理工大学3号楼516室
  • 邮编:510640
  • 邮箱:aukzllyy@scut.edu.cn
  • 电话:020-87111464
  • 国际标准刊号:ISSN:1000-8152
  • 国内统一刊号:ISSN:44-1240/TP
  • 邮发代号:46-11
  • 获奖情况:
  • 国内外数据库收录:
  • 美国化学文摘(网络版),美国数学评论(网络版),德国数学文摘,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊(2004版),中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版),中国北大核心期刊(2000版)
  • 被引量:21084