Synchronization of Stochastic Complex Networks by Dynamic Periodic Adaptive Event-Triggered Control

Throughout this paper, we introduce a novel dynamic periodic adaptive event-triggered control (ETC) strategy to explore the exponential synchronization in mean square (ESMS) of stochastic complex networks (SCNs). Firstly, unlike continuous ETCs, periodic ETCs rely on the periodic monitoring of system states, inherently avoiding the Zeno phenomenon. Secondly, by incorporating a dynamic function and an exponential term, the event-triggering frequency can be effectively reduced. Simultaneously, the triggering thresholds within the event-triggering mechanism automatically adjust in response to changes in the error term, further enhancing control efficiency. Subsequently, through the integration of graph-theoretic results and Lyapunov analysis, we obtain sufficient conditions to ensure ESMS of SCNs under the proposed dynamic periodic adaptive ETCs. Lastly, the analytical results are applied to a stochastic single-link robot arm system, and corresponding numerical simulation results are provided to illustrate the effectiveness of our proposed strategy.