Commande prédictive non-linéaire : application à la production d'énergie

Abstract : This thesis deals with hybrid optimal control and Model Predictive Control (MPC) of power plants by use of physical models. Models of the facilities are developed with Modelica, an equation based language tailored for modelling multi-physics systems. Modeling of physical systems with Modelica is introduced in a first part, as well as some of the symbolic processing done by Modelica compilers that transform the original model to a form suited for optimization. Then, a method to solve optimal control problems on hybrid systems (such as power plants) is presented. This methods provides an optimal trajectory for the power plant on a long horizon. The optimal trajectory computed by the method includes the trajectories of continuous inputs as well as switching decisions for components in the plant. The optimization algorithm combines the collocation method and a method named Sum Up Rounding (SUR) for dealing with switches. Finally, a Model Predictive Controller is developed in order to follow this optimal trajectory in real time, and to cope with disturbances on the actual system and modelling errors. The proposed MPC uses tangent linear models of the plant that are derived automatically from the nonlinear model.
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Submitted on : Monday, September 19, 2016 - 4:18:32 PM
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  • HAL Id : tel-01368526, version 1


Manon Fouquet. Commande prédictive non-linéaire : application à la production d'énergie. Mathématiques [math]. CentraleSupélec, 2016. Français. ⟨tel-01368526⟩



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