Further results on sampled-data synchronization control for chaotic neural networks with actuator saturation

Abstract The problem of local synchronization control for chaotic neural networks with sampled-data and saturating actuators is investigated in this paper. The intervals from the sampling instant tk to its next instant t k + 1 are assumed to be within a sampling interval. By taking advantage of characteristic information on the whole sampling interval, a new two-sided sampling-interval-dependent discontinuous Lyapunov functional is first constructed, which depends on the available information of both the intervals from tk to t and from t to t k + 1 . Then, by utilizing the Lyapunov functional, a novel sampling-interval-dependent stability condition is derived, rendering the synchronization error systems stable. Moreover, an optimization approach is provided to design desired sampled-data controllers such that the set of admissible initial conditions can be maximized. Finally, the effectiveness and benefits of the presented method is verified by numerical simulation.

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