Abstract

Recently, recursive least square (RLS) based model free predictive control (MF-PC) has been widely applied to model estimation for superior performance to model-based method. In most previous researches the forgetting factor which is a tradeoff between the convergence speed and the steady-state tracking error is usually neglected, thus selected as a constant to ensure robustness. In this paper, a dynamic forgetting factor based bias compensated RLS (DFF-BCRLS) is proposed for model free predictive current control of a voltage source inverter. The dynamic forgetting factor is calculated by local optimal forgetting factor (LOFF) algorithm to improve data convergency and steady-state robustness. A bias compensation (BC) scheme is presented to further increase the accuracy of parameter estimation and immune input noise. The simulation results show that the proposed strategy has smaller tracking error in steady-state and better performance against model or parameter mismatches compared to the conventional RLS.