Further results on stabilization of neural-network-based systems using sampled-data control

This paper investigates the problem of stabilization for sampled-data neural-network-based systems under variable sampling. A novel Lyapunov–Krasovskii functional (LKF) is introduced to the sampled-data systems. The benefit of the new approach is that the LKF develops more information about the actual sampling pattern. In addition, some symmetric matrices involved in the LKF are not required to be positive definite. Based on a recently introduced Wirtinger-based integral inequality that has been shown to be less conservative than Jensen’s inequality, much less conservative stabilization conditions are obtained to ensure the maximal sampling period and the minimal guaranteed cost control performance. Then, the corresponding sampled-data controllers can be synthesized by solving a set of linear matrix inequalities. Finally, an illustrative example is given to show the feasibility and effectiveness of the proposed method.