Event-Triggered Privacy Preservation Consensus Control and Containment Control for Nonlinear MASs: An Output Mask Approach
Yang Liu, Xiangpeng Xie, Jiayue Sun, Dongsheng Yang
Abstract
This article investigates the privacy-preserving consensus control and containment control for strict-feedback multiagent systems (MASs). For the agents possessing sensitive state information that needs safeguarding, an output mask function is employed, which ensures that the true state value remains indiscernible to the other agents during the process of information interaction. However, the introduction of mask function increases the complexity of the cooperative control design for MASs, given the untrustworthiness of the received state information from other agents. To address this challenge, an adaptive backstepping-based control algorithm is proposed, relying on the masked states of neighboring agents. Simultaneously, a dynamic event-triggered control with the reset mechanism is introduced to save communication resources, in which the dynamic of the additional variable is determined by the preset conditions. Based on the proposed event-triggered privacy-preserving control method, it is ensured that the initial state value of each agent remains undisclosed, and the tracking errors can converge to a residual set around zero. Similar results are extendable to the privacy preservation containment control for MASs. Finally, the efficacy of the proposed control method is validated through two illustrative examples.