Abstract
Achieving average consensus without disclosing sensitive initial states is important for secure multi-agent coordination. This work lifts each vector-valued agent state into a higher-dimensional space and designs dynamic, low-rank, positive-semidefinite matrix-valued inter-agent couplings. The resulting fully distributed algorithm conceals the original states while preserving exact average consensus. Its convergence analysis is reduced to an average-consensus problem on switching matrix-weighted networks, and privacy is guaranteed when each agent has at least one legitimate neighbor. Because the method relies on basic matrix operations rather than cryptographic procedures, it is suitable for efficient distributed control and optimization.
Dewei LI
Professor | Lab Leader
Currently serves as the Director of the Technical Committee on Predictive Control and Intelligent Decision of Chinese Association of Automation, Member of the Control Theory and Applications Education Working Group, and Editorial Member of Control Engineering Practice. He has led over 20 national-level projects, including Key Projects, General Projects and International Cooperation Projects from NSFC, Key Research and Development Programs from MOST. He has also undertaken more than 20 commissioned projects from key enterprises.
