期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

测量数据部分丢失的非线性系统迭代学习控制 被引量：1

Iterative Learning Control for Nonlinear Systems with Missing Data
在线阅读 下载PDF
导出
摘要 研究非线性离散系统的迭代学习控制．利用并行分配补偿方法(PDC)确定非线性系统的T-S模型,把非线性模型特换为局部线性模型。假定数据丢失的概率已知,采用满足Bernoulli分布的序列来描述测量数据的丢失,研究具有测量数据部分丢失的线性离散系统的迭代学习控制器的设计。结合T-S模型设计了数据丢失的迭代学习控制器,所设计的迭代学习控制器具有期望收敛特性和二次型性能指标。仿真结果表明了该设计方法的有效性。 Based on the Takagi-Sugeno fuzzy model, a fuzzy iterative learning control scheme is developed by the parallel distributed compensation （PDC） method. For packet-based transmission of data over a network, or temporary sensor failure, etc. , data samples may be missed in measured signals. The missed measurements happen at any sample time, and the probability of the occurrence of missing data is assumed to be known. The series meeting the Bernoulli distribution is used to describe missed measurements. The fuzzy iterative learning controller guarantees the expected convergence of the tracking error and has quadratic performance indices. Simulation example demonstrates the validity of the design approach.
作者 蔡逢煌 王武 杨富文
机构地区 福州大学电气工程与自动化学院
出处 《南京航空航天大学学报》 EI CAS CSCD 北大核心 2006年第B07期8-12,共5页 Journal of Nanjing University of Aeronautics & Astronautics
基金 国家自然科学基金(60474049)资助项目 福建省自然科学基金(A0410012)资助项目 福州大学科技发展基金(2005-XY-04)资助项目
关键词 迭代学习控制 数据丢失 T—S模型 期望收敛 非线性系统 iterative learning control missing data T-S model expected convergence nonlinear system
分类号 TP13 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献11

  • 1Ahn H S, Choi C H, Kim K B. Iterative learning control for a class of nonlinear system[J]. Automatica, 1993,29(6):1575-1578.
  • 2Lucibello P, Panzieri S, Ulivi G. Repositioning control of a two-link flexible arm by learning[J]. Automatica, 1997,33 (4): 579-590.
  • 3Ahn H S, Choi C H, Kim K B. Iterative learning control for a class of nonlinear system[J]. Automatica, 1993,29(6) : 1575-1578.
  • 4Takagi T, Sugeno M. Fuzzy identifichtion of systems and its applications to modeling and control[J].IEEE Trans on System, Man, and Cybernetics,1985,15(1) :116-132.
  • 5Kim E, Lee H. New approaches to relaxed quadratic stability condition of fuzzy control system[J]. IEEE Trans on Fuzzy Systems, 2000,8 (5):523-534.
  • 6张丽萍,杨富文.基于T-S模型的迭代学习控制算法及其在机器人点位控制中的仿真研究[J].系统仿真学报,2005,17(1):166-169. 被引量：9
  • 7Taniguchi T, Tanaka K, Yamafuji K. Nonlinear model following control via Takagi-Sugeno fuzzy model[C]//Proc of the American Control Conference. San Diego, California: Institute of Electrical and Engineers Inc, 1999:1837-1841.
  • 8Begovich O, Sanchez E N, Maldonado M. TakagiSugeno fuzzy scheme for real-time trajectory tracking of an underactuated robot[J]. IEEE Trans on Control Systems Technology, 2002,10(1) : 14-20.
  • 9Ma Xiaojun, Sun Zengqi. Output tracking and regulation of nonlinear system based on Takagi-Sugeno fuzzy model[J]. IEEE Trans on Systems, Man and Cybernetics-Part B: Cybernetics, 2000,30 (1): 47-59.
  • 10Lee H, Park J, Joo Y. Comments on output tracking and regulation of nonlinear system based on Takagi-Sugeno fuzzy model [J]. IEEE Trans on Systems, Man and Cybernetics-Part B:Cybernetics,2003,33(3) :521-523.

二级参考文献11

  • 1Arimoto S, Kawamura S, Miyazaki E Bettering operation of robots by learning[J]. Journal of Robotic Systems, 1984, 1(2): 123-140.
  • 2Bondi P, Casalino G, Gambardella L. On the iterative learning control theory for robotic manipulators[J]. IEEE Journal of Robotic and Automation, 1988, 4(1): 14-22.
  • 3Horowitz R. Learning control of robot manipulators[J]. ASME Journal of Dynamic Systems, Measurement, and Control, 1993,115(2): 402-411.
  • 4Lucibcllo P, Panzieri S, Ulivi G Repositioning control of a two-link flexible arm by learning[J]. Automatiea, 1997, 33(4): 579-590.
  • 5Lucibello E State steering by learning for a class of nonlinear control systems[J]. Automatica, 1994, 30(9): 1463-1468.
  • 6Khalil H K. Nonlinear Systems[M]. New York: Prentice Hall, 1996.
  • 7Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Trans on System, Man,and Cybernetics, 1985,15(1): 116-132.
  • 8Wang H O, Tanaka K, Griffin M F. An approach to fuzzy control of nonlinear systems: stability and design issues[J]. IEEE Trans on Fuzzy Systems, 1996, 4(1): 14-23.
  • 9Tanaka K, Sugeno M. Stability analysis and design of fuzzy control systems[J]. Fuzzy Sets and Systems, 1992, 45(2): 135-156.
  • 10Wonham W M. Linear Multivariable Control: A Geometric Approach[M]. Berlin: Springer-Verlag, 1979.

共引文献8

同被引文献8

  • 1Smith S C, Seller P. Estimation with lossy measurements: jump estimators for jump systems [ J ]. IEEE Trans Automatic Control, 2003, 48(12) : 2 163 -2 171.
  • 2Wang Z, Yang F, Ho D W C, et al. Robust finite - horizon filtering for stochastic systems with missing measurements [ J ]. IEEE Signal Process Lett, 2005, 12(6) : 437 -440.
  • 3Wang Z, Ho D W C, Liu X. Variance constrained filtering for uncertain stochastic systems with missing measurements [ J ]. IEEE Transactions on Automatic Control, 2003, 48 (7) : 1 254 - 1 258.
  • 4Yang F, Wang Z, Gani M. Robust H2/H∞ filtering for uncertain systems with missing measurement[ C]//Proc of the 45th IEEE Conference on Decision & Control. San Diego: [ s. n. ], 2006: 4 769 - 4 774.
  • 5Cai F H, Yang Y H, Wang W, et al. Observer - based H∞ control for linear stochastic discrete - time systems with missing measurements[ C]//Proc the 5th International Conference on Machine Learning and Cybernetics. [ s. l. ] : IEEE Press, 2006, 2 : 759 - 763.
  • 6Wang W, Yang F. Observer - based non - fragile H∞ control for linear systems [ J ]. Dynamics of Continuous Discrete and Impulsive Systems: Applications and Algorithms, 2005: (Special Issue) : 102 - 106.
  • 7Yang F, Wang Z, Hung Y S, et al. H∞ control for networked systems with random communication delays[ J ]. IEEE Trans Automatic Control, 2006, 51(3) : 511 -518.
  • 8阮玉斌,杨富文,王武.测量丢失概率不确定的网络化系统的鲁棒故障检测[J].控制与决策,2008,23(8):894-899. 被引量：7

引证文献1

二级引证文献1

南京航空航天大学学报
2006年 第B07期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部