A Generalized Integrated MPC-Fuzzy-Neural Network Approach for Multilevel Inverter Fed PMSMs

Abstract

This article proposes a generalized hybrid method (GHM) for permanent magnet synchronous motors that leverages the advantages of field-oriented control (FOC) in steady-state operation and model predictive control (MPC) during transient-state operation. The proposed GHM aims to achieve fast dynamic response, zero average steady-state error, low overshoot and computation time, and reduced voltage vectors (VVs) without the need for weighting factors tuning for multilevel inverters. To achieve these objectives, the GHM creates a single VV using FOC and merges it in the MPC. Furthermore, the fuzzy logic chooses the optimal VV and inserts its input-output to train the artificial neural network to directly select the optimal value and achieve fast selection behavior. The GHM was experimentally evaluated against conventional MPC, FOC, and recent conventional hybrid methods (CHMs) in different operating conditions. The results showed that the proposed GHM demonstrated a low computation time, fast dynamic response, and good steady-state performance. Additionally, the recent CHMs were found to suffer from large overshoot, whereas the proposed method exhibited stable performance and low overshoot.