Asymptotic Tracking of Uncertain Systems With Continuous Control Using Adaptive Bounding

被引:23
|
作者
Stepanyan, Vahram [1 ]
Kurdila, Andrew [2 ]
机构
[1] NASA, Ames Res Ctr, Mission Crit Technol Inc, Moffett Field, CA 94035 USA
[2] Virginia Polytech Inst & State Univ, Dept Mech Engn, Blacksburg, VA 24061 USA
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2009年 / 20卷 / 08期
关键词
Asymptotic tracking; disturbance rejection; neural network approximation; nonlinear uncertain systems; NONLINEAR-SYSTEMS; ADAPTATION;
D O I
10.1109/TNN.2009.2023214
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a robust adaptive control design method for a class of multiple-input-multiple-output uncertain nonlinear systems in the presence of parametric and nonparametric uncertainties and bounded disturbances. Using the approximation properties of the unknown continuous nonlinearities and the adaptive bounding technique, the developed controller achieves asymptotic convergence of the tracking error to zero, while ensuring boundedness of parameter estimation errors. The algorithm does not assume the knowledge of any bound on the unknown quantities in designing the controller. It is based on an integral technique involving the filtered tracking error and produces a continuous control. Theoretical developments are illustrated via simulation results.
引用
收藏
页码:1320 / 1329
页数:10
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