作者
Haimin Hu, Konstantinos Gatsis, Manfred Morari, George J Pappas
发表日期
2020/1/1
期刊
21st IFAC World Congress
卷号
53
期号
2
页码范围
5225-5232
出版商
Elsevier
简介
This 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.
学术搜索中的文章
H Hu, K Gatsis, M Morari, GJ Pappas - IFAC-PapersOnLine, 2020