Real-Time Discrete Neural Block Control Using Sliding Modes for Electric Induction Motors

被引：59

作者：

Alanis, Alma Y. ^{[1
]}

Sanchez, Edgar N. ^{[2
]}

Loukianov, Alexander G. ^{[2
]}

Perez-Cisneros, Marco A. ^{[1
]}

机构：

[1] Univ Guadalajara, Dept Ciencias Computac, Univ Ciencias Exactas & Ingn, Col Los Aguilas 45080, Zapopan Jalisco, Mexico

[2] Natl Polytech Inst, Adv Studies & Res Ctr, Guadalajara 45091, Jalisco, Mexico

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2010年 / 18卷 / 01期

关键词：

Discrete-time nonlinear systems; electric induction motor; extended Kalman filtering (EKF) learning; neural block control (NBC); sliding modes; NONLINEAR CONTROL; SYSTEMS;

D O I：

10.1109/TCST.2008.2009466

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with real-time adaptive tracking for discrete-time induction motors in the presence of bounded disturbances. A high-order neural-network structure is used to identify the plant model, and based on this model, a discrete-time control law is derived, which combines discrete-time block-control and sliding-mode techniques. This paper also includes the respective stability analysis for the whole system with a strategy to avoid adaptive weight zero-crossing. The scheme is implemented in real time using a three-phase induction motor.

引用

页码：11 / 21

页数：11

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