A Novel Adaptive Model Predictive Current Control for Three-Level Neural-Point-Clamped Inverter With RL Load

Parameter uncertainty has been a key challenge for the model predictive current control (MPCC) of a three-level neutral-point clamped (3L-NPC) inverter. To solve this issue, this article proposes an adaptive MPCC (AMPCC), which employs a novel online observer to estimate the system parameter with guaranteed convergence. The observed parameter is then utilized in the deadbeat MPCC to accurately calculate the duty ratios of voltage vectors (VVs). Moreover, to eliminate the tedious weighting factor selection process, a new simplified neutral-point (NP) voltage balancing method is proposed to rearrange the distribution time of the complementary VVs in each control period, resulting in precise dc-link voltage balance. Noticeably, the proposed NPV balancing method also attains very fast dynamic responses when the input voltage changes. Consequently, the proposed AMPCC obtains superior control performance with the constant switching frequency, while imposing a low computational burden. Thorough experimental assessments have been carried out to verify the effectiveness of the proposed method.