Hybrid Extended State Observer-Based Model-Free Predictive Control for Interleaved Buck Converter

Model predictive control has gained increasing popularity in power electronics in recent years for its simple principle and excellent control performance. However, the traditional model prediction is highly dependent on the mathematical model of the exact system, and the mismatch of system parameters may degrade. Moreover, accurate system modeling remains inherently challenging due to the coexistence of inherent uncertainties and time-varying parametric characteristics. In this regard, a model-free predictive control strategy based on hybrid extended state observer is proposed in this paper. It constructs an ultra-local model of the system instead of an accurate physical model to reduce dependence on model parameters. The unmodeled portion of the system is compensated into the unknown terms of the ultra-local model to eliminate steady-state errors and improve the system robustness against disturbances. And the effect of error accumulation in case of model parameter mismatch is reduced. Besides, its stability is analyzed in detail. Finally, a three-phase interleaved buck converter is constructed for simulation and experimental verification. The results show the efficacy of the proposed method with strong robustness.