Distributed Average Tracking in Weight-Unbalanced Directed Networks

• 成果类型: Article
• 署名作者: Sun, Shan; Chen, Fei; Ren, Wei
• 署名单位: University of California System; University of California Riverside; Northeastern University - China; Northeastern University - China
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3046029
• 发表日期: 2021
• 页码: 4436-4443
• 关键词: Heuristic algorithms; Directed graphs; Laplace equations; Eigenvalues and eigenfunctions; Distributed algorithms; CONVERGENCE; Network topology; distributed average tracking; multiagent system; weight-unbalanced directed graphs

摘要

This article studies a distributed average tracking (DAT) problem, in which a collection of agents work collaboratively, subject to local communication, to track the average of a set of reference signals, each of which is available to a single agent. Our primary objective is to seek a design methodology for DAT under possibly weight-unbalanced directed networks-the most general and thus most challenging case from the network topology perspective, which has few results in the literature. For this purpose, we propose a distributed algorithm based on a chain of two integrators that are coupled with a distributed estimator. It is found that the convergence depends on not only the network topology but also the deviations among the reference signal accelerations. Another primary interest of this article stems from the dynamics perspective-a point perceived as a main source of control design difficulty for multiagent systems. Indeed, we devise a nonlinear algorithm that is capable of achieving DAT under weight-unbalanced directed networks for agents subject to high-order integrator dynamics. The results show that the convergence to the vicinity of the average of the reference signals is guaranteed as long as the signals' states and control inputs are all bounded. Both algorithms are robust to initialization errors, i.e., DAT is insured even if the agents are not correctly initialized, enabling the potential applications in a wider spectrum of application domains.