The impact of model approximation in multiparametric model predictive control Academic Article

abstract

• 2018 Institution of Chemical Engineers Incorporating a high fidelity model that accurately describes a dynamical system in a model predictive control study may often lead to an intractable formulation where the use of model approximation is required. This study examines system identification, time series modeling, and linearization in the context of multiparametric model predictive control with the use of key error metrics including: (i) a novel comparison of key features of the feasible space and objective function in the optimization formulation, (ii) integral time absolute error, (iii) error distribution analysis, and (iv) step response profiles. Two examples are used as a basis for this study: a tank system which highlights the techniques used and a Continuously Stirred Tank Reactor (CSTR).

authors

• Pistikopoulos, Stratos

published proceedings

• CHEMICAL ENGINEERING RESEARCH & DESIGN

author list (cited authors)

• Katz, J., Burnak, B., & Pistikopoulos, E. N.

citation count

• 14

complete list of authors

• Katz, Justin||Burnak, Baris||Pistikopoulos, Efstratios N

publication date

• November 2018

publisher

• Elsevier  Publisher

published in

• n0263-8762ISSN  Journal

keywords

• Model Predictive Control
• Model Validation
• Multiparametric Programming

Digital Object Identifier (DOI)

• 10.1016/j.cherd.2018.09.034

start page

• 211

end page

• 223

volume

• 139

URL

• http://dx.doi.org/10.1016/j.cherd.2018.09.034