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
收藏
页码：11 / 21
页数：11
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