Abstract
This paper addresses the differentially private bipartite consensus control problem for general linear discrete-time multi-agent systems (MASs), where the initial states of the agents are the sensitive data to be protected from potential eavesdroppers. In response to the cooperative–competitive relationship among the agents, a novel hierarchical differential privacy mechanism (HDPM) is proposed, which consists of a classifier and a pair of input and output noise injectors. The HDPM and the bipartite consensus controller are co-designed to ensure the ultimate mean-square bipartite consensus while preserving differential privacy. Finally, numerical simulations are conducted to illustrate the effectiveness of the proposed algorithm.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
