Privacy-Preserving Distributed Cooperative Command Governor Schemes

A novel distributed Command Governor (CG) scheme, relying on cooperative distributed optimization, is presented for multi-agent networked systems subject to local and coupling constraints. Unlike existing non-cooperative distributed CG schemes, the proposed approach leverages a distributed optimization framework based on relaxation techniques and duality theory to enable agents to cooperatively contribute individually to the minimization of a global performance index. Furthermore, agents exchange only a vector that encodes the resource utilization of other agents, preserving privacy by avoiding the exchange of information about objective functions, constraints, or admissible commands. The effectiveness of the proposed approach is validated through an illustrative example involving vehicles operating in a 2D space that are subject to connectivity keeping coupling constraints.