Low Complexity MPC-DSVPWM for Current Control of PMSM Using Neural Network Approach

Abstract

Multilayer neural network-based model predictive control (MLNN-MPC) has received a lot of attention in different power electronic applications. However, the computational burden often imposes limitations in low-order DSPs especially if a large number of voltage vectors (VVs) are used. The execution time of MLNN-MPC in low-order DSPs is affected heavily by the number of input, output, neurons in the hidden layer, and the type of activation function. Furthermore, MLNN contains many parameters that needed to be optimized, such as initial weights, number of iterations, and number of neurons. Therefore, in this study, a creative single-layer neural network-based model predictive control with discrete space vector PWM (SLNN-MPC-DSVPWM) is proposed to overcome these limitations. The main advantages of the proposed method include easy implementation on low-order DSPs, better performance compared with MLNN-MPC, allowing the use of a large number of VVs, and no initialization of lookup tables for all VVs. The proposed SLNN is trained using the Levenberg-Marquardt algorithm and results in an execution time of only 8μs compared with the complexity of the conventional MPC-DSVPWM and recent MLNN-MPC methods. The SLNN-MPC-DSVPWM is validated by both simulation and experimental results for permanent magnet synchronous motors.