东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (3): 305-308.DOI: 10.12068/j.issn.1005-3026.2014.03.001

• 信息与控制 •    下一篇

丢包信息部分已知的变采样周期网络控制系统的H∞控制

李媛1，张鹏飞2，张庆灵1   

  1. (1东北大学 理学院， 辽宁 沈阳110819; 2沈阳工业大学 理学院， 辽宁 沈阳110870)
  • 收稿日期:2013-05-26 修回日期:2013-05-26 出版日期:2014-03-15 发布日期:2013-11-22
  • 通讯作者: 李媛
  • 作者简介:李媛(1974-)，女，山东龙口人，东北大学博士研究生，沈阳工业大学副教授；张庆灵(1956-)，男，辽宁营口人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61074029，61104093，61273008).

H∞ Control of Networked Control Systems with TimeVarying Sampling Periods and Partially Known Packet Dropout Information

LI Yuan1， ZHANG Pengfei2， ZHANG Qingling1   

  1. 1. School of Sciences， Northeastern University， Shenyang 110819， China; 2. School of Science， Shenyang University of Technology， Shenyang 110870， China.
  • Received:2013-05-26 Revised:2013-05-26 Online:2014-03-15 Published:2013-11-22
  • Contact: ZHANG Qingling
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摘要： 研究了基于时变采样周期网络控制系统的H∞控制问题.考虑数据丢包个数有界且符合一个有限状态的Markov链，设计状态反馈控制器，将具有网络时延和丢包的NCSs建模为Markov跳变系统.针对数据丢包的信息难以精确获得的情况，即转移概率部分已知时，基于李雅普诺夫方法，给出系统随机稳定且具有H∞范数界γhkj的充分条件.基于LMIs技术，获得控制器设计方案.数值实例表明该方法是有效的并具有较好的H∞性能.

关键词: 网络控制系统, H∞控制, 时变采样周期, 转移概率部分已知, 李雅普诺夫方法

Abstract: The H∞ control problem for networked control systems(NCSs) based on timevarying sampling periods was studied. Since the successive packetdropouts were driven by an independent finite Markov chain， the NCSs with the networkedinduced delays and packetdropouts were modeled as Markov jump systems by designing the state feedback controller. Aiming at the fact that the packet dropout information was hard to be obtained accurately， namely it did not need to know the transition probabilities completely， the sufficient condition for stochastic stability with H∞ norm bound γhkj was presented on the basis of Lyapunov method. The stabilizing controller was constructed in terms of linear matrix inequalities (LMIs). The results of the numerical examples demonstrated the H∞ performance better than the existing methods.

Key words: networked control systems(NCSs), H∞ control, timevarying sampling periods, partially known transition probabilities, Lyapunov method

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