Sampled-data control for synchronization of Markovian jumping neural networks with packet dropout

This paper addressed the master-slave synchronization problem of Markovian jumping neural networks with control packet dropout and sampled-data control. The packet dropout process is modelled as certain Bernoulli distributed white noise sequences. Under the zero-input strategy, a new stochastic switched sampled-data controller is proposed. Based on Lyapunov theory, an improved Lyapunov-Krasovskii function is constructed to derive the stability criteria. By using the technique of convex combination and free-matrix-based inequality, sufficient conditions can be obtained to guarantee the synchronization even if the packet dropout happens randomly. By employing the proposed scheme, the corresponding sampled-data controller is acquired through solving the linear matrix inequalities. The numerical example is provided to verify the feasibility and advantages of the approach.

Automated summary

This paper proposes a new approach to address the master-slave synchronization problem of Markovian jumping neural networks with control packet dropout and sampled-data control. The proposed method guarantees synchronization by establishing stability criteria and employing convex combination and free-matrix-based inequality techniques.