Latest Advances of Model Predictive Control in Electrical Drives. Part II: Basic Concepts and Advanced Strategies

The application This paper 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 paper 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: Basic Concepts and Advanced Strategies

Latest Advances of Model Predictive Control in Electrical Drives. Part II: Basic Concepts and Advanced Strategies

Abstract

The application This paper 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 paper 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.

Connclusions

TThe results presented in this paper show that MPC can be adapted to control a wide variety of electrical machines, maintaining the simplicity of the basic control strategy. Particular restrictions and conditions associated with the different types of machines can be easily included by introducing minor changes in the cost functions. Speed, torque, and flux are well controlled in all applications. A general assessment of the dynamic behavior of the controlled machine shows that model predictive control reaches better results than two well-established high-performance strategies, namely Field Oriented Control and Direct Torque Control. A more specific assessment in a high-power machine driven by a 3-level neutral point clamped inverter shows that the strategy known as Model Predictive Pulse Pattern Control has superior performance, reducing the inverter losses and the distortion in the motor current when compared with the classical solutions. Another specific assessment for electric cars shows that multi-step model predictive current control has an outstanding behavior generating less current distortion in the motor, reducing the inverter losses, with a comparable acoustic noise, compared to classical linear control. As the main conclusion, it can be affirmed that Model Predictive Control emerges as a brilliant and competitive alternative to high-performance strategies for the control of electrical machines.

Jose Rodriguez; et al., “Latest Advances of Model Predictive Control in Electrical Drives. Part II: Applications and Benchmarking with Classical Control Methods,” in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2021.3121589.