Discrete-time adaptive backstepping nonlinear control via high-order neural networks

被引：122

作者：

Alanis, Alma Y. ^{[1
]}

Sanchez, Edgar N. ^{[1
]}

Loukianov, Alexander G. ^{[1
]}

机构：

[1] IPN, Ctr Invest Estudios Avanzados, Unidad Guadalajar, Guadalajara 45091, Jalisco, Mexico

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 04期

关键词：

backstepping; discrete-time systems; electric induction motor; extended Kalman filtering (EKF); high-order neural networks (HONNs);

D O I：

10.1109/TNN.2007.899170

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 deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

引用

页码：1185 / 1195

页数：11

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