DocumentCode
2278980
Title
Trajectory tracking for delayed recurrent neural networks
Author
Sanchez, Edgar N. ; Perez, JoseP ; Perez, Joel
Author_Institution
CINVESTAV, Unidad Guadalajara, Jalisco
fYear
2006
fDate
14-16 June 2006
Abstract
This paper deals with the problem of trajectory tracking for delayed recurrent neural networks. The tracking error is global asymptotic stabilized by a control law derived on the basis of a Lyapunov-Krasovsky functional. Then, it is established that this control law minimizes a meaningful cost functional. Applicability of the approach is illustrated by means of an example
Keywords
Lyapunov methods; asymptotic stability; delay systems; optimal control; position control; recurrent neural nets; time-varying systems; Lyapunov-Krasovsky functional; asymptotic stability; control law; cost functional; delayed recurrent neural networks; inverse optimal control; time-delay systems; trajectory tracking; Artificial neural networks; Control systems; Delay effects; Delay systems; Neural networks; Optimal control; Recurrent neural networks; Stability analysis; Target tracking; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2006
Conference_Location
Minneapolis, MN
Print_ISBN
1-4244-0209-3
Electronic_ISBN
1-4244-0209-3
Type
conf
DOI
10.1109/ACC.2006.1656559
Filename
1656559
Link To Document