Efficient Fixed-Switching Modulated Finite Control Set-Model Predictive Control Based on Artificial Neural Networks

The disadvantage of finite control set-model predictive control (FCS-MPC) is that the switching frequency is variable and relies on the sampling time and operating point. This paper describes how to implement a new algorithm to achieve a fixed-switching frequency functionality for the FCS-MPC. The used approach combines the FCS-MPC with the SVPWM, resulting in the calculation of dwell times and the selection of the best two active vectors for the next sample interval. These dwell times have a significant impact on FCS-MPC performance during transient and steady-state conditions, and their values are determined using various mathematical models. To solve the problem of the fixed-switching frequency with lower harmonics distortion compared to the conventional modulated MPC (M2PC), an ANN-based trained network is proposed to calculate the duty-cycle of the applied vectors and thus the dwell time in the next sampling interval. The ANN network receives the cost functions of the two active vectors and the zero vector from the M2PC control algorithm and determines the optimal duty-cycle for each vector based on a proper tuning. In this way, three goals are achieved, the first goal is that the algorithm explicitly obtains a fixed-switching frequency, and secondly, the cost is as simple as the conventional M2PC. Finally, the feature of including objectives and non-linearity is still applicable. The paper’s case study used the two level voltage source inverter (2L-VSI) for uninterruptible power supply (UPS) applications. The results based on MATLAB/Simulink revealed that the ANN-M2PC has retained all FCS-MPC features in addition to operating at a fixed-switching frequency, while the power quality is significantly enhanced.