Privacy-preserving dynamic average consensus via state decomposition: Case study on multi-robot formation control

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