Enlarging the terminal region of nonlinear model predictive control using the support vector machine method

被引：40

作者：

Ong, C. J. ^{[1
]}

Sui, D.

Gilbert, E. G.

机构：

[1] Natl Univ Singapore, Singapore MIT Alliance, Dept Mech Engn, Singapore 117548, Singapore

[2] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA

来源：

AUTOMATICA | 2006年 / 42卷 / 06期

关键词：

nonlinear model predictive control; support vector machine; constraints; stability; terminal conditions;

D O I：

10.1016/j.automatica.2006.02.023

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 this paper, receding horizon model predictive control (RHMPC) of nonlinear systems subject to input and state constraints is considered. We propose to estimate the terminal region and the terminal cost off-line using support vector machine learning. The proposed approach exploits the freedom in the choices of the terminal region and terminal cost needed for asymptotic stability. The resulting terminal regions are large and, hence provide for large domains of attraction of the RHMPC. The promise of the method is demonstrated with two examples. (c) 2006 Elsevier Ltd. All rights reserved.

引用

页码：1011 / 1016

页数：6

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