International Conference Hasan Ali Gamal Al-kaf, Samer Saleh Hakami, Laith M. Halabi, and Kyo-Beum Lee, "Model Predictive Current Control Using Single Layer Neural Network for PMSM Drives," in Proc. ECCE Conf., 2022.
2022
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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
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