Combined model approximation techniques and multiparametric programming for explicit nonlinear model predictive control
- 作者：Pedro Rivotti ; p.rivotti@imperial.ac.uk ; Romain S.C. Lambert ; Efstratios N. Pistikopoulos
- 关键词：Multiparametric programming ; Nonlinear MPC ; Explicit MPC ; Nonlinear model reduction ; Model approximation
- 刊名：Computers and Chemical Engineering
- 出版年：2012
- 期刊代码：35_00981354
- 类别：et
- 出版时间：11 July, 2012
- 卷：42
- 期：Complete
- 页码：277-287
- 文件大小：1033 K
摘要
This work presents a methodology to derive explicit multiparametric controllers for nonlinear systems, combining model approximation techniques and multiparametric model predictive control (mp-MPC) algorithms. Particular emphasis is given to an approach that applies a nonlinear model reduction technique, based on balancing of empirical gramians, which generates a reduced order model suitable for nonlinear mp-MPC algorithms. This approach is compared with a recently proposed method that uses a meta-modelling based model approximation technique which can be directly combined with standard multiparametric programming algorithms. The methodology is illustrated for two nonlinear models, of a distillation column and a train of CSTRs, respectively.