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

This article presents a novel model-free (MF) deadbeat (DB) 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 autoregressive with exogenous input (ARX) model, complemented by a recursive least squares (RLSs) estimator for real-time parameter identification. This strategy offers enhanced adaptability to dynamic system conditions and achieves robustness against parameter mismatches inherent in grid-connected inverter systems. To demonstrate this, two distinct MF predictive control strategies have been benchmarked: one grounded in the DB control principle and the other utilizing a rolling optimization technique. Simulation analyses demonstrate that the MF-DBPC, driven by the DB 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.