Event-triggered Differential Privacy Protection Tracking Control

To counter the risk of eavesdropper attacks inherent in traditional tracking control methods, research on tracking control in multi-agent systems must incorporate privacy protection considerations. This study introduces an event-triggered distributed differential privacy protection tracking control (EDPTC) method in multi-agent systems featuring both leaders and followers, aimed at addressing privacy protection issues in tracking control. The method is designed to ensure the privacy of the states of leaders and followers at all times while achieving mean square tracking. Given the differences in state updates between leaders and followers, privacy protection mechanisms tailored to each party are developed based on the sensitivity upper bound theorem: the decreasing noise mechanism for followers (DNMF) and the random noise mechanism for leaders (RNML) . Moreover, to minimize the impact of random noise on control performance, a leader state estimation algorithm is introduced, and a distributed event trigger is designed to reduce the communication frequency. Additionally, through matrix analysis and probability theory, the privacy of the EDPTC is proven, and sufficient conditions for achieving mean square tracking are derived. Finally, a series of numerical simulations validate the effectiveness of the proposed method.