Distributed continuous-time algorithm for robust resource allocation problems using output feedback

This paper proposes a novel distributed continuous-time algorithm for the resource allocation problem with uncertainty parameters, which is a robust optimization problem. The considered objective function is the sum of local convex functions assigned to agents in a multi-agent network, with private set constraints and global inequality constraint involving uncertain parameters. Each agent only knows its local objective function, local constraint set, and neighbor information. We propose a novel continuous-time distributed subgradient-based algorithm with projected output feedback to solve the optimization problem. Finally, we show that the algorithm is able to find the optimal solution under some mild conditions.