Fast model predictive control for linear periodic systems with state and control constraints

被引：10

作者：

Hoai-Nam Nguyen ^{[1
]}

Bourdais, Romain ^{[2
]}

Gutman, Per-Olof ^{[3
]}

机构：

[1] IFP Energies Nouvelles, Rond Point Echangeur Solaize BP3, F-69360 Solaize, France

[2] Cent Supelec IETR, Ave Boulaie BP 81127, F-35511 Cesson Sevigne, France

[3] Technion Israel Inst Technol, Fac Civil & Environm Engn, IL-32000 Haifa, Israel

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2017年 / 27卷 / 17期

关键词：

polyhedral invariant sets; periodic systems; model predictive control; interpolating control; input and state constraints; DISCRETE-TIME-SYSTEMS; IMPROVED VERTEX CONTROL; UNCERTAIN; MPC; STABILITY;

D O I：

10.1002/rnc.3760

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The design of stabilizing model predictive control laws for discrete-time linear periodic systems with state and control constraints is considered. Two algorithms are presented. The first one is based on interpolation between several unconstrained periodic controllers. Among them, one controller is chosen for the performance while the rest are used to extend the domain of attraction. The second algorithm aims to improve the performance by combining model predictive control and interpolating control. The proposed approaches not only guarantee recursive feasibility and asymptotic stability but also are optimal for states near the origin. Copyright (C) 2017 John Wiley & Sons, Ltd.

引用

页码：3703 / 3726

页数：24

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