Distributed event-triggered estimation for dynamic average consensus: A perturbation-injected privacy-preservation scheme

Abstract

Previous studies on the distributed estimation problem for dynamic average consensus of multi-agent networks are usually based on the assumption that each agent continuously and honestly shares information with its neighbors. To relax this assumption, this paper focuses on the distributed event-triggered private estimation problem. By injecting random perturbations into the original reference signal, an adaptive robust distributed privacy-preserving event-triggered estimation algorithm is proposed. With the proposed algorithm, the convergence of the estimation error is guaranteed under intermittent communication, while protecting the private reference signal information from disclosure. To determine the timing for communication, an adaptive distributed dynamic triggering mechanism with a dynamically updated internal triggering variable is designed. In addition, a dynamically updated adaptive gain instead of a static gain is employed in the estimation algorithm and triggering mechanism to eliminate the dependence on some global information. Finally, numerical simulation results are presented to illustrate the validity of the theoretical results.