<div class="csl-bib-body">
<div class="csl-entry">Du, Z. P., Kofler, S., Ritzberger, D., Jakubek, S., & Hametner, C. (2023). Optimal design of experiments model predictive controller. In <i>22nd IFAC World Congress. Yokohama, Japan, July 9-14, 2023. Proceedings</i> (pp. 11173–11178). Elsevier. https://doi.org/10.1016/j.ifacol.2023.10.839</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/189921
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dc.description.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.
en
dc.description.sponsorship
FFG - Österr. Forschungsförderungs- gesellschaft mbH
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dc.language.iso
en
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dc.relation.ispartofseries
IFAC-PapersOnLine
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dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.subject
Experiment design
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dc.subject
Identifiability
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dc.subject
Input and excitation design
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dc.subject
Intelligent control of power systems
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dc.subject
Optimal control theory
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dc.subject
Optimal operation and control of power systems
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dc.title
Optimal design of experiments model predictive controller
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
