Combined model approximation techniques and multiparametric programming for explicit nonlinear model predictive control Academic Article

abstract

• 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. 2012.

authors

• Pistikopoulos, Stratos

published proceedings

• COMPUTERS & CHEMICAL ENGINEERING

author list (cited authors)

• Rivotti, P., Lambert, R., & Pistikopoulos, E. N.

citation count

• 34

complete list of authors

• Rivotti, Pedro||Lambert, Romain SC||Pistikopoulos, Efstratios N

publication date

• July 2012

publisher

• Elsevier  Publisher

published in

• Computers and Chemical Engineering  Journal

keywords

• Explicit Mpc
• Model Approximation
• Multiparametric Programming
• Nonlinear Model Reduction
• Nonlinear Mpc

Digital Object Identifier (DOI)

• 10.1016/j.compchemeng.2012.01.009

start page

• 277

end page

• 287

volume

• 42

URL

• http://dx.doi.org/10.1016/j.compchemeng.2012.01.009