A Distributed Feedback-Based Framework for Nonlinear Aggregative Optimal Control

作     者：Sforni, Lorenzo Carnevale, Guido Notarstefano, Giuseppe 

作者机构：Alma Mater Studiorum Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy 

出 版 物：《IEEE TRANSACTIONS ON AUTOMATIC CONTROL》 (IEEE Trans Autom Control)

年 卷 期：2025年第70卷第6期

页      面：3784-3799页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：European Research Council [OPT4SMART638992] Ministero degli Affari Esteri e della Cooperazione Internazionale [PGR10067] 

主　　题：Optimal control Trajectory Costs Vehicle dynamics Distributed algorithms Cost function Vectors Surveys Multi-agent systems Games Distributed optimization multiagent systems optimal control optimization algorithms 

摘      要：In this article, we propose a distributed, first-order, feedback-based approach to solve nonlinear optimal control problems with aggregative cost functions over networks of cooperative multiagent systems. Taking inspiration from a centralized, first-order optimal control framework, named GoPRONTO, we propose a distributed method exploiting a feedback scheme iteratively updated according to a distributed tracking mechanism. Due to the aggregative structure of the problem and the desired distributed paradigm, the centralized scheme would require global quantities that are not locally available. Thus, our distributed method concurrently updates a proxy of the centralized scheme with a set of local, auxiliary variables named trackers which suitably exploit interagent communication to reconstruct the global quantities. By relying on LaSalle-based arguments, we theoretically prove that our algorithm generates a sequence of trajectories converging to the set of trajectories satisfying the first-order necessary conditions for optimality. Finally, we corroborate the theoretical results with numerical simulations on a distributed optimal control application for a fleet of 50 quadrotors.