A two-time-scale dynamic average consensus estimator
He Bai · 2016
We consider a dynamic average consensus problem where a group of agents is required to track the average of their time-varying inputs. We assume that the inputs are sinusoidal with a single unknown frequency. We develop a distributed two-time-scale estimator that estimates the unknown frequency and achieves average consensus of the inputs. We establish input-to-state (ISS) properties of the estimator using two-time-scale averaging theory. We also explore benefits of fusing the agents' individual frequency estimates. Using a simulation example, we demonstrate that the average consensus performance is significantly improved by incorporating the fused frequency estimates into the estimator.