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.

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.

This paper presents a new and very simple strategy for torque and flux control of ac machines. The method is based on model predictive control and uses one cost function for the torque and a separate cost function for the flux. This strategy introduces a drastic simplification, achieving a very fast dynamic behavior in the controlled machines. Experimental results obtained with an induction machine confirm the drive's very good performance.

The ultralocal model of a plant expresses the behavior of the output variable in relation to the input without requiring any specific information about the plant. Replacing the conventional model of the system with an ultralocal model can effectively improve the robustness of the model predictive control (MPC) because all types of uncertainties that correlate with the system modeling are removed in the ultralocal model. This article proposes an integral sliding mode observer (ISMO)-based ultralocal model for the finite-set model predictive current control (FS-MPCC) of an induction motor (IM). The ISMO is constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. Moreover, an analytical comparison is made between the proposed ISMO and the conventional extended state observer (ESO) in the application of the ultralocal model. This comparison shows that the stability of ESO depends on the unknown term of the ultralocal model. In contrast, the stability of the proposed ISMO does not rely on any terms of the ultralocal model. Experimental tests confirm that the proposed FS-MPCC method is practically applicable to the IM drive system. Also, it improves the robustness of the predictive method against model uncertainty.

This paper presents an event-triggered model predictive control (ET-MPC) method for power converters. In the proposed method, the MPC scheme is triggered only when the state of the converter exceeds a preset threshold; otherwise, the MPC scheme is suspended and the control signal is held as constant. Therefore, compared to the conventional MPC with finite control set (FCS-MPC), the ET-MPC method has the advantages of less computational burden and less switching actions which contribute to lower switching losses, while ensuring satisfactory regulation performance. A buck converter prototype is adopted to validate the performance of ET-MPC. The results from the comparison with FCS-MPC demonstrate the effectiveness of the proposed ET-MPC method.

Finite Set Model Predictive Control (FS-MPC) has many advantages, such as a fast dynamic response and an intuitive implementation. For these reasons, it has been thoroughly researched during the last decade. However, the wave form produced by FS-MPC has a switching component whose spread spectrum remains a major disadvantage of the strategy. This paper discusses a modulated model predictive control that guarantees a spectrum switching frequency in the linear modulation range and extends its optimized response to the overmodulation region. Due to the equivalent high gain of the predictive control, and to the limit on the voltage actuation of the power converter, it is expected that the actuation voltage will enter the overmodulation region during large reference changes or in response to load impacts. An optimized overmodulation strategy that converges towards FS-MPC’s response for large tracking errors is proposed for this situation. This technique seamlessly combines PWM’s good steady-state switching performance with FS-MPC’s high dynamic response during large transients. The constant switching frequency is achieved by incorporating modulation of the predicted current vectors in the model predictive control of the currents in a similar fashion as conventional Space-Vector Pulse Width Modulation (SV-PWM) is used to synthesize an arbitrary voltage reference. Experimental results showing the proposed strategy’s good steady-state switching performance, its FS-MPC-like transient response and the seamless transition between modes of operation are presented for a permanent magnet synchronous machine drive.