Event-Triggered Privacy-Preserving Average Consensus for Multiagent Networks With Time Delay: An Output Mask Approach

This article investigates the privacy-preserving average consensus problem for the general continuous-time multiagent network systems (MANSs) with time delay via the event-triggered communication scheme. In order to avoid disclosing the initial states of the network agents and at the same time achieve the agents' consensus for MANSs with time delay, a novel consensus control algorithm based on a new privacy-preserving approach is proposed. The new privacy-preserving approach is that we construct an output mask to make agents' internal states indiscernible by others, which is different from the existing privacy-preserving methods adding random noises to the update law of agents' states. Compared with the existing privacy-preserving methods, our approach makes all agents in MANSs exactly converge to the average value of initial states instead of its mean square value. Based on the proposed algorithm, we carry out the detailed theoretical consensus analysis of the network agents, from which, it is shown that the upper bound of the communication time delay between neighbors' agents can be estimated approximately. Moreover, the Zeno-behavior of event-triggered time sequences for each agent is excluded. Finally, two simulation examples are performed to demonstrate the effectiveness of our theoretical results.