Long-Horizon Direct Model Predictive Control with Reduced Computational Complexity

Mattia Rossi, Petros Karamanakos, Arto Sankala

Tutkimustuotos: KonferenssiartikkeliTieteellinenvertaisarvioitu

1 Sitaatiot (Scopus)
10 Lataukset (Pure)

Abstrakti

The paper proposes a strategy that reduces the computational burden of enumeration-based direct model predictive control (MPC) with long prediction intervals. This is achieved by combining a move blocking scheme with an educated restriction of the set of candidate solutions. To demonstrate the effectiveness of the proposed algorithm, a two-level converter connected to the grid via an LCL filter serves as a case study.

AlkuperäiskieliEnglanti
Otsikko2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)979-8-3503-9686-7
ISBN (painettu)979-8-3503-9687-4
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Predictive Control of Electrical Drives and Power Electronics - Wuhan, Kiina
Kesto: 16 kesäk. 202319 kesäk. 2023

Conference

ConferenceIEEE International Conference on Predictive Control of Electrical Drives and Power Electronics
Maa/AlueKiina
KaupunkiWuhan
Ajanjakso16/06/2319/06/23

Rahoitus

ACKNOWLEDGEMENT The authors would like to acknowledge the financial support from the Tandem Industry Academia (TIA) funding program of the Finnish Research Impact Foundation (FRIF).

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality

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