Robust and stable predictive control with bounded uncertainties

被引：6

作者：

Ramos, C. ^{[1
]}

Martinez, M. ^{[1
]}

Sanchis, J. ^{[1
]}

Herrero, J. M. ^{[1
]}

机构：

[1] Univ Politecn Valencia, Dept Syst Engn & Control, Valencia 46022, Spain

来源：

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS | 2008年 / 342卷 / 02期

关键词：

model predictive control; min-max optimization; regularization; robustness; stability;

D O I：

10.1016/j.jmaa.2007.12.073

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Min-Max optimization is often used for improving robustness in Model Predictive Control (MPC). An analogy to this optimization could be the BDU (Bounded Data Uncertainties) method, which is a regularization technique for least-squares problems that takes into account the uncertainty bounds. Stability of MPC can be achieved by using terminal constraints, such as in the CRHPC (Constrained Receding-Horizon Predictive Control) algorithm. By combining both BDU and CRHPC methods, a robust and stable MPC is obtained, which is the aim of this work. BDU also offers a guided method of tuning the empirically tuned penalization parameter for the control effort in MPC. (C) 2008 Elsevier Inc. All rights reserved.

引用

页码：1003 / 1014

页数：12

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