Nonlinear Model Predictive Control for Stochastic Differential Equation Systems

Niclas Laursen Brok, Henrik Madsen, John Bagterp Jørgensen
2018 IFAC-PapersOnLine  
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
more » ... ated to systematic model identification, state estimation, and optimization of dynamical systems that are relevant to the NMPC. Abstract: 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. Abstract: 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. Abstract: 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. Abstract: 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. Abstract: 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.
doi:10.1016/j.ifacol.2018.11.071 fatcat:yaqnzuaahbesxlkudrywwkxddm