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

abstract

• 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. 2013 Elsevier Ltd.

authors

• Pistikopoulos, Stratos

published proceedings

• COMPUTERS & CHEMICAL ENGINEERING

author list (cited authors)

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

citation count

• 27

complete list of authors

• Lambert, Romain SC||Rivotti, Pedro||Pistikopoulos, EN

publication date

• July 2013

publisher

• Elsevier  Publisher

published in

• Computers and Chemical Engineering  Journal

keywords

• Model Approximation
• Model Order Reduction
• Model Predictive Control

Digital Object Identifier (DOI)

• 10.1016/j.compchemeng.2013.03.004

start page

• 60

end page

• 67

volume

• 54

URL

• http://dx.doi.org/10.1016/j.compchemeng.2013.03.004