Novel criteria for finite-time stabilization and guaranteed cost control of delayed neural networks

In this paper, the problem of robust finite-time stabilization with guaranteed cost control for a class of delayed neural networks is considered. The time delay is a continuous function belonging to a given interval, but not necessary to be differentiable. We develop a general framework for finite-time stabilization with guaranteed cost control based on the Lyapunov functional method and new generalized Jensen integral inequality. Novel criteria for the existence of guaranteed cost controllers are established in terms of linear matrix inequalities (LMIs). The proposed conditions allow us to design the state feedback controllers which robustly stabilize the closed-loop system in the finite time. A numerical example is given to illustrate the efficiency of the proposed method.

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