The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.

In this paper, an improved finite control set-model predictive control (FCS-MPC) with an optimized weighting factor is presented. The main goal of this paper is reducing the torque ripples when the FCS-MPC is implemented by means of the two-level inverter. For this purpose, the weighting factor is calculated via an optimization method. The optimization is based on dividing the control interval into two parts: active time for applying the active voltage vectors and zero time for applying the zero voltage. With this technique, the torque ripple is calculated as a function of weighting factor and it is optimized. The method is validated by simulations and experiments, using two-level inverter, at two speed regions (nominal speed and low speed). The results are compared with conventional FCS-MPC.

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.

The predictive control method deals with the prediction of the motor behavior based on a mathematical model of the motor. This model is dependent on the motor parameters. However, these parameters may not match with their actual values due to the measurement error or they may change during the operation of the motor. All these uncertainties and model inexactitude lead to inaccurate prediction of the motor behavior and deteriorate the performance of the predictive algorithm. This paper presents an extension of the predictive current control (PCC) method to improve the prediction accuracy for a permanent-magnet synchronous motor (PMSM). The proposed strategy not only reduces the current ripple but also improves the robustness of the system against parameter uncertainties. Simulation and experimental results that confirm the good performance of the proposed method are presented.

Finite control set-model predictive control (FCS-MPC) is emerging as an attractive alternative for power converters control. In comparison with the classical linear controllers, FCS-MPC needs a shorter control loop cycle time to achieve the same performance. But, the conventional FCS-MPC involves a large amount of calculation. The calculation efforts increase with the number of switching states of the power converter as well as the control objectives, which is a challenge for its application. This paper proposes an effective method to simplify the FCS-MPC and to reduce its calculation efforts for its application in matrix converter-fed permanent magnet synchronous motors. The experimental results which verify the good performance of the proposed method are presented.

In this paper, two kinds of observer-based sensorless predictive torque control methods are proposed. The predictive method is based on examining feasible voltage vectors (VVs) in a prescribed cost function. The VV that minimizes the cost function is selected. A novel robust prediction model is presented. The prediction model includes sliding mode feedbacks. The feedback gains are assigned by the H-inf method. Two kinds of observers are applied for flux and speed estimation, i.e., sliding mode full order observer and reduced order observer. In order to verify the proposed method, simulation and experimental results are presented in wide speed range. A comparison of the two methods is performed based on the results.