An Improved Model Predictive Control to Enhance Voltage Performance for $LC$ Filtered Three-Level Inverters With Voltage Feedback Only

This article proposes a double vector model predictive control (MPC) method for the
$LC$
filtered three-level T-type inverter to gain superior voltage performance, which is current sensorless and computationally efficient. In the proposed scheme, inverter voltage increment is induced in the multiobjectives cost function, and the desired voltage is derived based on the simplified discrete model, which optimizes the output voltage control performance. The prepositive prediction calculation is shifted out from the evaluation circle, and the computation is lessened simultaneously. Then, aiming to estimate the capacitor current of the simplified discrete model with voltage feedback only, the strong-tracking Kalman filter-based observer is designed, which gets rid of the dependence on current sensors and lowers hardware cost. Furthermore, to improve the control accuracy of tracking the desired voltage, a two-layer decision algorithm based on the search tree is developed to find the optimal double vector easily, which obtains a low-complexity selection process using the univariate cost function. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the theoretical analyses and the proposed method.