Virtual Voltage Vector–Based Polynomial‐Fitted Sampled‐Data Model Predictive Control for Single‐Phase Asymmetric Cascaded H‐Bridge Inverter

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The asymmetric cascaded H‐bridge (ACHB) inverter is capable of generating a greater number of output voltage levels under the same number of modules compared to conventional topologies. However, when applied to such multilevel systems, conventional finite control set model predictive control (FCS‐MPC) tends to exhibit undesirable harmonic components and chaotic switching frequency variation. Aiming at the inherent defects of excessive computational burden and poor steady‐state accuracy faced by FCS‐MPC in real‐time applications, this paper proposes a virtual voltage vector MPC strategy based on a sampled‐data model. By replacing the exponential matrix in the discretized sampled‐data model with a polynomial approximation, it enables more efficient real‐time execution. Finally, the proposed control strategy is implemented to a single‐phase nine‐level ACHB inverter. Both simulation and experimental results demonstrate the superior performance of the proposed algorithm under steady and dynamic states.