  Model predictive control (MPC) has become one of the well-established modern
control methods for three-phase inverters with an output LC filter, where a
high-quality voltage with low total harmonic distortion (THD) is needed.
Although it is an intuitive controller, easy to understand and implement, it
has the significant disadvantage of requiring a large number of online
calculations for solving the optimization problem. On the other hand, the
application of model-free approaches such as those based on artificial neural
networks approaches is currently growing rapidly in the area of power
electronics and drives. This paper presents a new control scheme for a
two-level converter based on combining MPC and feed-forward ANN, with the aim
of getting lower THD and improving the steady and dynamic performance of the
system for different types of loads. First, MPC is used, as an expert, in the
training phase to generate data required for training the proposed neural
network. Then, once the neural network is fine-tuned, it can be successfully
used online for voltage tracking purpose, without the need of using MPC. The
proposed ANN-based control strategy is validated through simulation, using
MATLAB/Simulink tools, taking into account different loads conditions.
Moreover, the performance of the ANN-based controller is evaluated, on several
samples of linear and non-linear loads under various operating conditions, and
compared to that of MPC, demonstrating the excellent steady-state and dynamic
performance of the proposed ANN-based control strategy.
