Model predictive control (MPC) is a very attractive candidate to replace standard field-
oriented control algorithms for electrical motors. We demonstrate that it is possible to …

Abstract Model Predictive Control (MPC) is an effective method of driving motors and power
converters due to its quick response, integrity, and multivariable control adaptability. A …

Conventional finite-control set model predictive current control predicts the motor behavior
for every switching state and selects the voltage vector that can minimize the cost functions …

This article proposes a new fault-tolerant control strategy based on finite-control-set model
predictive control (FCS-MPC) for interior permanent-magnet synchronous motor (IPMSM) …

In this article, a real-time moment of inertia identification technique using Petri probabilistic
fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior …

The parallel three-level neutral-point clamp (3L-NPC) inverters are widely solicited today to
satisfy the higher power demand by using low-rated switching devices in industrial …

This paper focuses on the sensorless high-precision position correction strategy for a 100
kW@ 20 000 r/min brushless dc (BLdc) motor with low stator inductance (0.051 mH). Two …

An interior permanent magnet synchronous motor (IPMSM) drive system with machine
learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control …

This paper reports the optimal control problem on the interior permanent magnet
synchronous motor (IPMSM) systems. The control performance of the traditional model …

Sensorless control of BLDC motors is preferred nowadays due to a number of drawbacks in
sensor based control. Line‐to‐line back EMF is generally extracted by filtering the terminal …