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Communication Dans Un Congrès Année : 2021

Model Predictive Control as an Industrially Applicable Approach for Power Control of Solid Oxide Fuel Cells

Résumé

In this paper, a model predictive control (MPC) combined with a discrete-time stationary Kalman filter as an observer for non-measureable states and input disturbances is presented as a simple and industrially applicable approach for controlling the electric power of a solid oxide fuel cell (SOFC). The developed controller was tested in a simulation in terms of its robustness under consideration of model uncertainties and measurement noise. The results were compared with a PI output-feedback controller combined with a feedforward control and an internal model control (IMC). For the MPC the framework conditions are equal to the PI controller and the IMC. Since these conditions can be reproduced by simulations, we can omit a rerun of the experiments. As a result, the MPC provides comparable results and presents as the better of the two alternative controllers.
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Dates et versions

hal-03419472 , version 1 (22-11-2021)

Identifiants

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Wiebke Frenkel, Julia Kersten, Harald Aschemann, Andreas Rauh. Model Predictive Control as an Industrially Applicable Approach for Power Control of Solid Oxide Fuel Cells. 2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR), Aug 2021, Międzyzdroje, Poland. pp.251-256, ⟨10.1109/MMAR49549.2021.9528484⟩. ⟨hal-03419472⟩
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