Parameter-free ultralocal model-based predictive current control method for grid-tied inverters using extremum seeking control

Abstract

This paper delves into the control engineering challenges faced by grid-tied inverter systems stemming from uncertainties such as unidentified physical parameters, unmodeled dynamics, and disturbances. To mitigate these challenges, this paper introduces a parameter-free ultralocal model method based on extremum seeking control (ESC). Firstly, the unmodeled part of the system is estimated using the extended state observer (ESO), and the current is predicted using the ultralocal model. Secondly, an online optimizer for ultralocal model control gain parameter based on ESC is designed to adjust the control gain, aiming to minimize current prediction error. This approach effectively disentangles the control performance from reliance on physical parameters and mitigates the impact of system uncertainties. Ultimately, the efficacy of the proposed approach is validated by hardware-in-the-loop (HIL) experiments and compared with conventional model-based and model-free current predictive control strategies. The comparative analysis underscores the method’s potential to significantly enhance disturbance rejection capabilities and overall system robustness.