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. Note to Practitioners—In industrial network systems, due to various stochastic disturbances, synchronization of SCNs is widely applied across multiple domains, such as power grid systems and coordinated robotic systems. Then, how to design effective controllers for synchronization of SCNs under limited communication resources has become a critical technical challenge, which promotes this paper. Based on periodic observation of system states and parametric adaptation mechanisms, the dynamic periodic adaptive ETC (DPAETC) strategy is proposed to achieve ESMS of SCNs and reduce communication resource consumption at the same time. In addition, from the simulation results, it can be seen that our DPAETC strategy greatly enhances the flexibility and effectiveness of the control strategy compared to those traditional ETC methods in practical application.