Real-Time Output Trajectory Tracking using a Discrete Neural Backstepping Controller

Author

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

Author_Institution

Dept. de Cien- cias Computacionales, Univ. de Guadalajara, Guadalajara

fYear

2008

fDate

3-5 Sept. 2008

Firstpage

1289

Lastpage

1294

Abstract

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances. 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). The learning algorithm for the HONN is based on an Extended Kalman Filter (EKF). This paper also includes the respective stability analysis, using the Lyapunov approach. The proposed scheme is implemented in real-time to control a three phase induction motor, as to track a time-variying speed reference and a constant flux magnitude reference.

Keywords

Lyapunov methods; MIMO systems; discrete time systems; feedback; neurocontrollers; nonlinear control systems; position control; stability; Lyapunov method; adaptive tracking; block strict feedback form; discrete neural backstepping controller; discrete-time MIMO system; extended Kalman filter; high order neural network; nonlinear system; output trajectory tracking; stability analysis; Backstepping; Control systems; Induction motors; MIMO; Neural networks; Neurofeedback; Nonlinear systems; Real time systems; Recurrent neural networks; Trajectory; Backstepping; Discrete-time nonlinear control; Extended Kalman filter; High-order neural network; Induction Motor;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 2008. ISIC 2008. IEEE International Symposium on

Conference_Location

San Antonio, TX

ISSN

2158-9860

Print_ISBN

978-1-4244-2224-1

Electronic_ISBN

2158-9860

Type

conf

DOI

10.1109/ISIC.2008.4635939

Filename

4635939