Du, Z. P., Kofler, S., Ritzberger, D., Jakubek, S., & Hametner, C. (2023). Optimal design of experiments model predictive controller. In 22nd IFAC World Congress. Yokohama, Japan, July 9-14, 2023. Proceedings (pp. 11173–11178). Elsevier. https://doi.org/10.1016/j.ifacol.2023.10.839
E325-04 - Forschungsbereich Regelungstechnik und Prozessautomatisierung E325 - Institut für Mechanik und Mechatronik
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Published in:
22nd IFAC World Congress. Yokohama, Japan, July 9-14, 2023. Proceedings
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Date (published):
2023
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Event name:
22nd IFAC World Congress
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Event date:
9-Jul-2023 - 14-Jul-2023
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Event place:
Yokohama, Japan
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Number of Pages:
6
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Publisher:
Elsevier, Amsterdam
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Keywords:
Experiment design; Identifiability; Input and excitation design; Intelligent control of power systems; Optimal control theory; Optimal operation and control of power systems
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Abstract:
System investigations such as simulation, diagnosis, and control require well-identified models. This work proposes an optimal design of experiments model predictive controller (MPC) to obtain experiments for identification. The main contribution is an MPC formulation with a target-oriented implementation of the parameter sensitivity (Fisher information), which remains a convex quadratic problem. Computers can optimally and efficiently solve quadratic problems, including constraints, and the method is demonstrated with a linear cathode model of a polymer electrolyte membrane fuel cell. The MPC is demonstrated in simulations, including disturbances, and significantly improves the parameter identifiability compared to a non-optimized experiment.
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Project title:
Increasing market penetration of FC cars by efficient system solutions: 878123 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)
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Research Areas:
Sustainable and Low Emission Mobility: 33% Modeling and Simulation: 33% Automation and Robotics: 34%
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