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doi:10.22028/D291-30912
Titel: | A Review on Model Predictive Control and its Applications in Power Electronics |
VerfasserIn: | Borreggine, Simone Monopoli, Vito Giuseppe Rizzello, Gianluca Naso, David Cupertino, Francesco Consoletti, Rinaldo |
Sprache: | Englisch |
Titel: | 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) |
Seiten: | 6 |
Verlag/Plattform: | IEEE |
Erscheinungsjahr: | 2019 |
Titel der Konferenz: | AEIT AUTOMOTIVE 2019 |
Konferenzort: | Torino, Italy |
Dokumenttyp: | Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag) |
Abstract: | Model Predictive Control (MPC) has established itself as a reliable control strategy due to its flexibility and high performance. Although the first applications of MPC date back to the 80's, its use in the field of power electronic converters has grown significantly only in recent years. Within this field, the advantages introduced by MPC include inherently multivariable design, improved dynamic performance, and possibility of accounting for constraints on input and output variables. The aim of this work is to summarize and analyze several applications of MPC presented over last years in the field of power electronics. The main features of MPC are first discussed, with a focus on both Finite Control Set MPC and Explicit MPC methods. Then, a summary of different issues and corresponding solutions is reported, together with a list of applications and results obtained on different converters and loads controlled via MPC. Finally, motivations for MPC use in powertrain applications are discussed, and a brief summary of powertrain configurations and related issues are presented. |
DOI der Erstveröffentlichung: | 10.23919/EETA.2019.8804594 |
URL der Erstveröffentlichung: | https://ieeexplore.ieee.org/abstract/document/8804594 |
Link zu diesem Datensatz: | hdl:20.500.11880/29125 http://dx.doi.org/10.22028/D291-30912 |
ISBN: | 978-8-8872-3743-6 978-88-87237-44-3 |
Datum des Eintrags: | 12-Mai-2020 |
Fakultät: | NT - Naturwissenschaftlich- Technische Fakultät |
Fachrichtung: | NT - Systems Engineering |
Professur: | NT - Prof. Dr. Stefan Seelecke |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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