English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dynamic average consensus is a decentralized control/estimation frameworkwhere a group of agents cooperatively track the average of local time-varyingreference signals. In this paper, we develop a novel state decomposition-basedprivacy preservation scheme to protect the privacy of agents when sharinginformation with neighboring agents. Specifically, we first show that anexternal eavesdropper can successfully wiretap the reference signals of allagents in a conventional dynamic average consensus algorithm. To protectprivacy against the eavesdropper, a state decomposition scheme is developedwhere the original state of each agent is decomposed into two sub-states: onesucceeds the role of the original state in inter-node interactions, while theother sub-state only communicates with the first one and is invisible to otherneighboring agents. Rigorous analyses are performed to show that 1) theproposed privacy scheme preserves the convergence of the average consensus; and2) the privacy of the agents is protected such that an eavesdropper cannotdiscover the private reference signals with any guaranteed accuracy. Thedeveloped privacy-preserving dynamic average consensus framework is thenapplied to the formation control of multiple non-holonomic mobile robots, inwhich the efficacy of the scheme is demonstrated. Numerical simulation isprovided to illustrate the effectiveness of the proposed approach.

Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control