A Monte-Carlo based model approximation technique for linear model predictive control of nonlinear systems

被引：19
|
作者：
Lambert, Romain S. C. [1 ]
Rivotti, Pedro [1 ]
Pistikopoulos, E. N. [1 ]
机构：
[1] Univ London Imperial Coll Sci Technol & Med, Dept Chem Engn, Ctr Proc Syst Engn, London SW7 2AZ, England
来源：
COMPUTERS & CHEMICAL ENGINEERING | 2013年 / 54卷
基金：
欧洲研究理事会;
关键词：
Model predictive control; Model order reduction; Model approximation;
D O I：
10.1016/j.compchemeng.2013.03.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we present a model approximation technique based on N-step-ahead affine representations obtained via Monte-Carlo integrations. The approach enables simultaneous linearization and model order reduction of nonlinear systems in the original state space thus allowing the application of linear MPC algorithms to nonlinear systems. The methodology is detailed through its application to benchmark model examples. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：60 / 67
页数：8
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