Copyright © 2012 M. Rajchakit et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper studies the problem of guaranteed cost control for a class of uncertain
delayed neural networks. The time delay is a continuous function belonging to a given
interval but not necessary to be differentiable. A cost function is considered as a
nonlinear performance measure for the closed-loop system. The stabilizing controllers
to be designed must satisfy some exponential stability constraints on the closed-loop
poles. By constructing a set of augmented Lyapunov-Krasovskii functionals combined
with Newton-Leibniz formula, a guaranteed cost controller is designed via memoryless
state feedback control, and new sufficient conditions for the existence of the guaranteed
cost state feedback for the system are given in terms of linear matrix inequalities
(LMIs). Numerical examples are given to illustrate the effectiveness of the obtained
result.