Title :
Trajectory tracking for delayed recurrent neural networks
Author :
Sanchez, Edgar N. ; Perez, JoseP ; Perez, Joel
Author_Institution :
CINVESTAV, Unidad Guadalajara, Jalisco
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;
Conference_Titel :
American Control Conference, 2006
Conference_Location :
Minneapolis, MN
Print_ISBN :
1-4244-0209-3
Electronic_ISBN :
1-4244-0209-3
DOI :
10.1109/ACC.2006.1656559
