On Feasibility, Stability and Performance in Distributed Model Predictive Control

被引：72

作者：

Giselsson, Pontus ^{[1
]}

Rantzer, Anders ^{[1
]}

机构：

[1] Lund Univ, Dept Automat Control, S-22100 Lund, Sweden

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2014年 / 59卷 / 04期

基金：

瑞典研究理事会;

关键词：

Distributed model predictive control; feasibility; performance guarantee; stability; DECOMPOSITION;

D O I：

10.1109/TAC.2013.2285779

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In distributed model predictive control (DMPC), where a centralized optimization problemis solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.

引用

页码：1031 / 1036

页数：6

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