Distributed Average Tracking over Weight-Unbalanced Directed Graphs
Shan Sun, Fei Chen, Wei Ren · 2019
The distributed average tracking (DAT) problem in an unbalanced directed network is studied in this paper, in which each agent aims at tracking the average of a group of time- varying reference signals using only local information and local communication. While the existing literature primarily focuses on the case of undirected or weight-balanced directed networks, we deal with the much more challenging case of unbalanced directed networks. We propose a distributed continuous algorithm with a chain of two integrators coupled with a distributed estimator. We show that if the deviations among the reference accelerations tend to zero (respectively, are bounded), the algorithm can achieve DAT with zero (respectively, bounded) tracking error under an unbalanced directed network. The algorithm is robust to initialization errors in terms of agent states and achieve DAT for a wide class of reference signals. Numerical examples are presented to illustrate the derived theoretical results.