Statistics

Direct torque control

Inverter

Image (mathematics)

Electrical engineering

Switched reluctance motor

Physics

Engineering

Control engineering

Torque

Artificial intelligence

Voltage

Model predictive control

Rotor (electric)

Control (management)

Machine control

Flexibility (engineering)

Mathematics

Computer science

Control theory (sociology)

Mechanical engineering

Induction motor

Thermodynamics

Noise (video)

IEEE Transactions on Power Electronics

This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods