Nonlinear Model Predictive Control for Stochastic Differential Equation Systems

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review



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Using the Van der Pol oscillator model as an example, we provide a tutorial introduction to nonlinear model predictive control (NMPC) for systems governed by stochastic differential equations (SDEs) that are observed at discrete times. Such systems are called continuous-discrete systems and provides a natural representation of systems evolving in continuous-time. Furthermore, this representation directly facilities construction of the state estimator in the NMPC. We provide numerical details related to systematic model identification, state estimation, and optimization of dynamical systems that are relevant to the NMPC.

Original languageEnglish
Book seriesI F A C Workshop Series
Issue number20
Pages (from-to)430-435
Publication statusPublished - 2018
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Continuous-Discrete Extended Kalman Filter, Maximum Likelihood Estimation, Nonlinear Model Predictive Control, Stochastic Differential Equations, Van der Pol Oscillator

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