文章基本信息

标题：Non-Cooperative Distributed MPC with Iterative Learning
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作者：Haimin Hu ; Konstantinos Gatsis ; Manfred Morari 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：5225-5232
DOI：10.1016/j.ifacol.2020.12.1198
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a novel framework of distributed learning model predictive control (DLMPC) for multi-agent systems performing iterative tasks. The framework adopts a non-cooperative strategy in that each agent aims at optimizing its own objective. Local state and input trajectories from previous iterations are collected and used to recursively construct a time-varying safe set and terminal cost function. In this way, each subsystem is able to iteratively improve its control performance and ensure feasibility and stability in every iterations. No communication among subsystems is required during online control. Simulation on a benchmark example shows the efficacy of the proposed method.
关键词：KeywordsDistributed SystemsModel Predictive ControlIterative Learning Control