Augmented two-side-looped Lyapunov functional for sampled-data-based synchronization of chaotic neural networks with actuator saturation

This paper further investigated the synchronization problem of the chaotic neural networks by utilizing the sampled-data control with actuator saturation. Firstly, an augmented two-side-looped Lyapunov functional including both the states of the error system and their derivative is constructed. Then the Wirtinger-based integral inequality in combination with the improved reciprocally convex matrix inequality is applied to estimate the derivative of the presented Lyapunov functional and improved synchronization criteria are derived. As a result, a state feedback controller based on sampled-data is designed, making the drive system synchronize with the response system. Finally, through the results of the numerical example, the validity and superiority of the proposed methods have been confirmed.