NN-Based Approach to Model Predictive Control for Three-Phase Inverters Using One- or Two-Step Horizons

Finite-control-set model predictive control (FCS-MPC) gives low-distortion voltage regulation for LC-filtered two-level three-phase inverters, but its computation grows exponentially with the prediction horizon. We trained neural networks offline to imitate it. Four imitators were formed by crossing two network types, a pattern-recognition classifier (NNPR) and a regression network (NNRG), with data from one-step and two-step FCS-MPC teacher controllers, giving NNPR1, NNPR2, NNRG1, and NNRG2. All were evaluated in MATLAB/Simulink with linear resistive loads at sixteen training and four unseen interpolated conditions. Mean output-voltage total harmonic distortion (THD) was 2.55% for NNPR1, 0.89% for NNPR2, 2.89% for NNRG1, and 1.36% for NNRG2, against 2.32% and 1.73% for one-step and two-step FCS-MPC, respectively. The training horizon mattered more than the network type, and the classifier gave lower mean THD at both horizons. NNPR2 gave lower THD than its teacher controller at all sixteen conditions while switching 26% less often. The advantage came mainly from the load current, which the teacher controller estimated but the networks received as measured; given the measured value, the teacher controller came within 0.13 percentage points of NNPR2. Network computation is horizon-independent; MATLAB execution times are indicative only. Results are simulation-only; hardware-in-the-loop validation with delay compensation must precede deployment.