Koopman Lyapunov-based model predictive control of nonlinear chemical process systems Academic Article uri icon


  • Published 2019. This article is a U.S. Government work and is in the public domain in the USA. 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.

published proceedings


author list (cited authors)

  • Narasingam, A., & Kwon, J.

citation count

  • 38

complete list of authors

  • Narasingam, Abhinav||Kwon, Joseph Sang-Il

publication date

  • August 2019