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Model-Free Deadbeat Predictive Current Control for Grid-Connected Inverters Using Autoregressive Model and Recursive Least Squares

Mostefa Kermadi, Aissa Rebai, Saad Mekhilef, Lotfi Baghli, Nadhir Mesbahi, Marizan Binti Mubin, Hazlie Mokhlis, Harold Steven Ruiz

Résumé

This paper presents a novel Model-Free Deadbeat Predictive Current Controller (MF-DBPC) tailored for grid connected two-level inverters incorporating resistance-inductance (R-L) filters. Unlike traditional approaches, the MF-DBPC leverages a data-driven model derived solely from current and voltage measurements, eliminating the need for explicit systems parameter inputs. Central to the MF-DBPC’s functionality is an Auto-Regressive with Exogenous Input (ARX) model, complemented by a Recursive Least Squares (RLS) estimator for real-time parameter identification. This strategy offers enhanced adaptability to dynamic system conditions and achieve robustness against parameter mismatches inherent in grid-connected inverter systems. To demonstrate this, two distinct model-free predictive control strategies have been benchmarked: one grounded in the deadbeat control principle and the other utilizing a rolling optimization technique. Simulation analyses demonstrate that the MF-DBPC, driven by the deadbeat principle, yields superior current waveform quality while requiring a sampling frequency five times lower than its rolling optimization technique. Experimental validation further confirms the efficacy of the MF-DBPC across steady-state and dynamic performance metrics. Notably, its robustness against filter inductance mismatches is highlighted, showcasing resilience under challenging real-world conditions.

Détails

Langue originaleEnglish
RevueIEEE Journal of Emerging and Selected Topics in Power Electronics
Volume13
Numéro4
Numéro d’article11008703
Pages5138-5148
ÉditeurInstitute of Electrical and Electronics Engineers
ISSN2168-6777 · 2168-6785
DOI10.1109/jestpe.2025.3572359
Statut de publicationPublié — 21 mai 2025

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