This work presents an alternating sequential model predictive control (ASMPC) scheme applied to multimodular direct matrix converters (MMDMCs). A common limitation of model predictive control (MPC) in power converters is the need to assign weighting factors when several objectives are considered simultaneously. In practice, choosing appropriate weights often requires empirical adjustment and depends strongly on the system and operating conditions. Even sequential MPC (SMPC), which addresses part of this problem by prioritizing objectives, still implicitly relies on weighting through its predefined hierarchy and may lead to unbalanced performance when conditions change.
To address these deficiencies, the proposed ASMPC alternately evaluates two control objectives—load current tracking and input reactive power minimization—at each sampling step, without requiring explicit weighting factors or fixed priority ordering. This strategy was implemented in MATLAB/Simulink using an architecture composed of two direct matrix converters operating in parallel. The influence of parameter N2, which defines the number of candidate states considered after the first evaluation, was analyzed under step changes in reference currents of 30 A and 60 A. Performance metrics such as total harmonic distortion (THD) and mean squared error (MSE) were evaluated, supported by a descriptive statistical analysis including mean, standard deviation, mean absolute deviation (MAD), and coefficient of variation (CV).
Simulation results show low dispersion and stable performance against variations in N2. Overall, ASMPC provides a robust, efficient, and easy-to-implement solution for modular power converter systems, showing clear advantages over classical and sequential predictive control schemes.
