Finite-Time Guaranteed Cost Control of Caputo Fractional-Order Neural Networks

In this paper, we investigate the problem of finite-time guaranteed cost control of uncertain fractional-order neural networks. Firstly, a new cost function is defined. Then, by using linear matrix inequalities (LMIs) approach, some new sufficient conditions for the design of a state feedback controller which makes the closed-loop systems finite-time stable and guarantees an adequate cost level of performance are derived. These conditions are in the form of linear matrix inequalities, which therefore can be efficiently solved by using existing convex algorithms. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.