Reduced order model predictive control for constrained discrete-time linear systems

被引：8

作者：

Hara, N. ^{[1
]}

Kojima, A. ^{[2
]}

机构：

[1] Osaka Prefecture Univ, Dept Elect & Informat Syst, Osaka 5998531, Japan

[2] Tokyo Metropolitan Univ, Tokyo 1910065, Japan

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2012年 / 22卷 / 02期

关键词：

model predictive control; constrained systems; eigenvalue problem; optimization; REGULATOR; STATE;

D O I：

10.1002/rnc.1685

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A reduced order model predictive control (MPC) is discussed for constrained discrete-time linear systems. By employing a decomposition method for finite-horizon linear systems, an MPC law is obtained from a reduced order optimization problem. The decomposition enables us to construct pairs of initial state and control sequence which have large influence on system responses, and it also characterizes the standard LQ control. The MPC law is obtained based on a combination of the LQ control and dominant input sequences over the prediction horizon. The proposed MPC method is illustrated with numerical examples. Copyright (C) 2011 John Wiley & Sons, Ltd.

引用

页码：144 / 169

页数：26

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