  Dynamic average consensus is a decentralized control/estimation framework
where a group of agents cooperatively track the average of local time-varying
reference signals. In this paper, we develop a novel state decomposition-based
privacy preservation scheme to protect the privacy of agents when sharing
information with neighboring agents. Specifically, we first show that an
external eavesdropper can successfully wiretap the reference signals of all
agents in a conventional dynamic average consensus algorithm. To protect
privacy against the eavesdropper, a state decomposition scheme is developed
where the original state of each agent is decomposed into two sub-states: one
succeeds the role of the original state in inter-node interactions, while the
other sub-state only communicates with the first one and is invisible to other
neighboring agents. Rigorous analyses are performed to show that 1) the
proposed privacy scheme preserves the convergence of the average consensus; and
2) the privacy of the agents is protected such that an eavesdropper cannot
discover the private reference signals with any guaranteed accuracy. The
developed privacy-preserving dynamic average consensus framework is then
applied to the formation control of multiple non-holonomic mobile robots, in
which the efficacy of the scheme is demonstrated. Numerical simulation is
provided to illustrate the effectiveness of the proposed approach.
