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Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems
Author(s) -
Narasingam Abhinav,
Kwon Joseph SangIl
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16743
Subject(s) - nonlinear system , lyapunov function , operator (biology) , mathematics , model predictive control , control theory (sociology) , controller (irrigation) , observable , stability (learning theory) , mathematical optimization , dynamical systems theory , convex optimization , computer science , regular polygon , control (management) , artificial intelligence , physics , quantum mechanics , biochemistry , chemistry , geometry , repressor , machine learning , biology , transcription factor , agronomy , gene
In this work, we propose the integration of Koopman operator methodology with Lyapunov‐based model predictive control (LMPC) for stabilization of nonlinear systems. The Koopman operator enables global linear representations of nonlinear dynamical systems. The basic idea is to transform the nonlinear dynamics into a higher dimensional space using a set of observable functions whose evolution is governed by the linear but infinite dimensional Koopman operator. In practice, it is numerically approximated and therefore the tightness of these linear representations cannot be guaranteed which may lead to unstable closed‐loop designs. To address this issue, we integrate the Koopman linear predictors in an LMPC framework which guarantees controller feasibility and closed‐loop stability. Moreover, the proposed design results in a standard convex optimization problem which is computationally attractive compared to a nonconvex problem encountered when the original nonlinear model is used. We illustrate the application of this methodology on a chemical process example.