Consensus-Based Distributed Optimization Enhanced by Integral Feedback
Xuan Wang, Shaoshuai Mou, Brian D. O. Anderson
Abstract
Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multiagent networks to solve convex optimization problems with local linear constraints. Assuming agent interactions are modeled by an undirected graph, the algorithm is capable of achieving the optimum solution with an exponential convergence rate. Furthermore, inherited from the beneficial integral feedback, the proposed algorithm has attractive requirements on communication bandwidth and good robustness against disturbance. Both analytical proof and numerical simulations are provided to validate the effectiveness of the proposed distributed algorithms in solving constrained optimization problems.