Title :
State estimation for a class of nonlinear differential games using differential neural networks
Author :
Garcia, E. ; Murano, Daishi A.
Author_Institution :
Dept. of Mech. & Mechatron., Inst. Tecnol. y de Estudios Super. de Monterrey Campus Estado de Mexico (ITESM-CEM), Atizapan de Zaragoza, Mexico
fDate :
June 29 2011-July 1 2011
Abstract :
This paper deals with the problem of the state estimation for a certain class of nonlinear differential games, where the mathematical model of this class is completely unknown. Being thus, a Luenberger-like differential neural network observer is applied and a new learning law for its synaptic weights is suggested. Furthermore, by means of a Lyapunov stability analysis, the stability conditions for the state estimation error are established and the upper bound of this error is obtained. Finally, a numerical example illustrates the applicability of this approach.
Keywords :
Lyapunov methods; differential games; learning (artificial intelligence); neurocontrollers; nonlinear control systems; observers; Luenberger-like differential neural network observer; Lyapunov stability analysis; learning law; mathematical model; nonlinear differential games; state estimation; Equations; Games; Mathematical model; Neural networks; Observers; Symmetric matrices; Differential games; dynamic neural networks; state observers;
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4577-0080-4
DOI :
10.1109/ACC.2011.5990779
