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未知输出反馈非线性时滞系统自适应神经网络跟踪控制
引用本文:陈为胜,李俊民.未知输出反馈非线性时滞系统自适应神经网络跟踪控制[J].自动化学报,2005,31(5):799-803.
作者姓名:陈为胜  李俊民
作者单位:1.Department of Applied Mathematics, Xidian University, Xi0an 710071
基金项目:Supported by National Natural Science Foundation of P. R. China (60374015) and Natural Science Foundation of Shanxi Province (2003A15)
摘    要:An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.

关 键 词:Nonlinear  time-delay  systems    neural  network    backstepping
收稿时间:2004-09-04
修稿时间:2005-04-18

Adaptive Neural Tracking Control for Unknown Output Feedback Nonlinear Time-delay Systems
CHEN Wei-sheng,LI Jun-min.Adaptive Neural Tracking Control for Unknown Output Feedback Nonlinear Time-delay Systems[J].Acta Automatica Sinica,2005,31(5):799-803.
Authors:CHEN Wei-sheng  LI Jun-min
Affiliation:1.Department of Applied Mathematics, Xidian University, Xi0an 710071
Abstract:An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique.Neural networks are used to approximate unknown time-delay functions.Delay-dependent filters are intro- duced for state estimation.The domination method is used to deal with the smooth time-delay basis functions.The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error.Based on Lyapunov-Krasoviskii functional,the semi-global uniform ultimate boundedness(SGUUB)of all the signals in the closed-loop system is proved.The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.
Keywords:Nonlinear time-delay systems  neural network  backstepping
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