To address the issue of degraded current tracking accuracy in NPC grid-connected inverters caused by parameter mismatches under conventional deadbeat predictive current control, this paper proposes a model reference adaptive deadbeat predictive control strategy based on Popov′s hyperstability theory. First, a two-step prediction scheme is employed to forecast and compensate the output current, achieving high dynamic response and low harmonic distortion under ideal conditions. Second, to mitigate the influence of inductance parameter variations, a model reference adaptive structure is introduced. By comparing the outputs error of a reference model and an adjustable model, an adaptation law is designed according to Popov′s hyperstability theory, enabling real-time parameter identification and dynamic compensation. This enhances parameter robustness and current prediction accuracy without compromising dynamic performance. Furthermore, a dynamic factor-based SVPWM strategy is incorporated into the control framework. By redistributing the action time of voltage vectors, this method effectively suppresses neutral-point potential fluctuations while synthesizing the desired output voltage vector, thereby improving control quality and operational stability. Finally, both simulation and experimental results demonstrate that, compared with conventional deadbeat predictive current control,the proposed strategy reduces output current THD by 9% under matched parameters and by 28% under parameter mismatch, while improving dynamic response speed by 34%. These results verify the of the proposed strategy in enhancing system robustness, improving waveform quality, and accelerating dynamic response.