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基于即时学习算法非线性系统多模型自适应控制 被引量:3

Lazy learning algorithm for multi-model adaptive control of nonlinear system
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摘要 针对可获得大量输入输出数据的非线性系统 ,提出一种改进的即时模型辨识方法 ,并与自校正的极点配置控制算法相结合 ,设计多模型自适应控制器 .所提出的建模方法和相应的多模型自适应控制器能较好地逼近非线性系统的动态特性 。 This paper presents a variant of lazy learning algorithm for modelling of discrete time unknown nonlinear dynamical systems when only input output data are available. A pole placement self tuning controller based on the local linearization provided by the lazy learning algorithm is described to improve the dynamical response performance. A simulation result shows the effectiveness of the proposed method.
作者 孙维 王伟
出处 《大连理工大学学报》 CAS CSCD 北大核心 2002年第5期611-615,共5页 Journal of Dalian University of Technology
基金 国家杰出青年科学基金资助项目 (6982 5 10 6) 教育部高等学校骨干教师资助计划资助项目
关键词 多模型自适应控制 非线性系统 极点配置 即时学习算法 建模方法 即时局部模型 nonlinear system pole placement adaptive control/ lazy learning algorithm multi model
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参考文献5

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同被引文献14

  • 1颜学峰.基于改进神经网络的干点软测量[J].高技术通讯,2007,17(1):44-48. 被引量:1
  • 2Yu C, Roy R J, Kaufman H, et al. Multiple--model adaptive predictive control of mean arterial pressure and cardiac output [J]. IEEE Transactions on Biomedical Engineering, 1992, 39(8) :765--777.
  • 3Fujinaka T, Omatu S. A switching scheme for adaptive control using multiple models[A]. IEEE SMC "99 Conference Proceedings[C]. 1999, 5: 80--85.
  • 4Narendra K S, Xiang C. Adaptive control of discrete--time system using multiple models [A]. Proceedings of the 37th Conferrnce on Decision and Control [C]. Tampa. Florida, 1998.
  • 5Narendra K S, Balakrishnan J. Adaptive control using multiple Models[J].IEEE Transaction Automation Control, 1997, 42(2) :171--187.
  • 6Morse A S, Mayne D Q, Goodwin G C. Applications of hysteresis switching in parameter adaptive control [J]. IEEE Trans on automation control, 1992, 37(9):1343--1354.
  • 7王其红,潘天红,邹云.基于即时学习算法的软测量建模方法[J].南京理工大学学报,2007,31(6):679-683. 被引量:7
  • 8Wang Guang,Yin Shen,Okyay Kaynak.An LWPR-based data- driven fault detection approach for nonlinear process monitor[J]. IEEE,2014,10(4) :2016-2018.
  • 9Bontempi G,Birattari M,Bersini H. Lazy learning for local mod- elling and control design[J].Intemational Journal of Control, 1999,72 ( 7-8 ) : 643-658.
  • 10余伟,罗飞,杨红,许玉格.基于多神经网络的污水氨氮预测模型[J].华南理工大学学报(自然科学版),2010,38(12):79-83. 被引量:12

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