Discrete-time backstepping induction motor control using a sensorless recurrent neural observer

Author

Alanis, Alma Y. ; Sanchez, Edgar N. ; Loukianov, Alexander G.

Author_Institution

Unidad Guadalajara, Guadalajara

fYear

2007

fDate

12-14 Dec. 2007

Firstpage

6112

Lastpage

6117

Abstract

This paper deals with the problem of controlling the discrete-time induction motor model based on a sensorless observer with only currents measurements. First a recurrent high order neural observer for the unknown plant is designed, then a high order neural network is used to emulate a control law designed by the backstepping technique. The learning algorithm for both neural networks is based on an extended Kalman filter. The applicability of the proposed observer-controller scheme is tested via simulation.

Keywords

Kalman filters; Lyapunov methods; MIMO systems; control system synthesis; discrete time systems; induction motors; learning (artificial intelligence); machine control; neurocontrollers; nonlinear control systems; nonlinear filters; observers; recurrent neural nets; tracking filters; HONN application; Kalman filter; Lyapunov approach; MIMO nonlinear systems; control law design; discrete-time backstepping induction motor control; high order neural network; learning algorithm; sensorless recurrent neural observer design; tracking problem; Adaptive systems; Backstepping; Control systems; Induction motors; Neural networks; Nonlinear control systems; Nonlinear systems; Sensorless control; Trajectory; USA Councils; Induction motors; backstepping; extended Kalman filter; high-order neural network; sensorless discrete-time nonlinear observer;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2007 46th IEEE Conference on

Conference_Location

New Orleans, LA

ISSN

0191-2216

Print_ISBN

978-1-4244-1497-0

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2007.4434164

Filename

4434164