PUBLICATIONS

Model predictive control (MPC) is regarded as a significant modern control for the current control of permanent magnet synchronous motor (PMSM). However, the computation burden of MPC imposes its advantage to be implemented in sophisticated converter topologies an multistep prediction 

horizons. Multilayer neural network with MPC (MLNN-MPC) is increasingly used in different convertersto overcome the drawback of high 

computational time. However, it has a higher computational time compared to a single-layer neural network (SLNN). In addition, many parameters 

need to be optimized such as initial weights, number of iterations, and neurons. In this paper, a SLNN with MPC is proposed to predict the current of 

PMSM. The proposed SLNNMPC is trained using the Levenberg Marquardt algorithm. Meanwhile, it shows better performance than MLNN-MPC with 

lower computational time by optimizing only one parameter. Furthermore, the simulation results are shown to verify the effectiveness of the proposed method