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.

The future electricity network has to be able to manage energy coming from different grids as well as from renewable energy sources (RES) and other distributed generation (DG) systems. Advanced power electronic converters can provide the means to control power flow and ensure proper and secure operation of future networks. This paper presents analysis, design, and experimental validation of a back-to-back three-phase ac-dc-ac multilevel converter employed for universal and flexible power management (UNIFLEX-PM) of future electrical grids and its advanced control technique. The proposed system has been successfully tested for bidirectional power flow operation with different grid operating conditions such as voltage unbalance, frequency variation, harmonic distortion, and faults due to short circuits.

Model predictive control (MPC) has a number of desirable attributes which are difficult to achieve with classical converter control techniques. Unfortunately, the nature of power electronics imposes restriction to the method, as a result of the limited number of available converter states. This, combined with the spread spectrum nature of harmonics inherent with the strategy, complicates further design. This paper presents a method for removing this characteristic without compromising the desirable functionality of predictive control. The method, named modulated MPC, is applied to a two-level three-phase converter and compared with a number of similar approaches. Experimental results are used to support theoretical analysis and simulation studies.

This paper presents a modification to the classical Model Predictive Control (MPC) algorithm and its application to active power filters. The proposed control is able to retain all the advantages of a finite control set MPC while improving the generated waveforms harmonic spectrum. In fact, a modulation algorithm, based on the cost function ratio for different output vectors, is inherently included in the MPC. The cost function-based modulator is introduced and its effectiveness on reducing the current ripple is demonstrated. The presented solution provides an effective and straightforward single loop controller, maintaining an excellent dynamic performance despite the modulated output and it is self-synchronizing with the grid. This promising method is applied to the control of a shunt active filter for harmonic content reduction through a reactive power compensation methodology. Significant results obtained by experimental testing are reported and commented, showing that MPC is a viable control solution for active filtering systems.

Multilevel converters are known to have many advantages for electricity network applications. In particular, cascaded H-bridge converters are attractive because of their inherent modularity and scalability. Predictive control for power converters is advantageous as a result of its applicability to discrete system and fast response. In this paper, a novel control technique, named modulated model predictive control, is introduced with the aim to increase the performance of model predictive control. The proposed controller addresses a modulation scheme as part of the minimization process. The proposed control technique is described in detail, validated through simulation and experimental testing, and compared with dead-beat and traditional model predictive control. The results show the increased performance of the modulated model predictive control with respect to the classic finite control set model predictive control in terms of current waveform total harmonic distortion (THD). Moreover, the proposed controller allows a multi-objective control, with respect to dead-beat control that does not present this capability.