Cooperative Trajectory Tracking of Heterogeneous Networks by Distributed Learning

• 成果类型: Article
• 署名作者: Meng, Deyuan; Zhang, Jingyao
• 署名单位: Beihang University; Beihang University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3542420
• 发表日期: 2025
• 页码: 5397-5412
• 关键词: Distance learning; Computer aided instruction; Trajectory tracking; Heterogeneous networks; trajectory; Network topology; uncertainty; TOPOLOGY; stability analysis; CONVERGENCE; Cooperative trajectory tracking; Distributed Learning; heterogeneous network

摘要

This article presents a distributed learning approach to achieve the high-precision cooperative trajectory tracking tasks for heterogeneous networks at all time in the presence of changing topologies. An updating law of distributed learning is proposed by leveraging the cooperative tracking error trajectories of agents at all time samples, thanks to which a monotonic convergence result is established for heterogeneous networks of linear agents without iteration-varying uncertainties. When considering heterogeneous networks of nonlinear agents with iteration-varying uncertainties, robust convergence results are further explored such that not only can robust cooperative trajectory tracking be realized, but also a robust stability property can be acquired for distributed learning processes. To carry out robust convergence analysis of distributed learning, an extended contraction mapping and a heterogeneous-to-homogeneous transformation approaches are developed, which can particularly address full heterogeneities and unknown nonlinearities of agents.