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.

Field oriented control (FOC), direct torque control (DTC) and finite set model predictive control (FS-MPC) are different strategies for high performance electrical drive systems. FOC uses linear controllers and pulse width modulation (PWM) to control the fundamental components of the load voltages. On the other hand, DTC and FS-MPC are nonlinear strategies that generate directly the voltage vectors in the absence of a modulator. This paper presents all three methods starting from theoretic operating principles, control structures and implementation. Experimental assessment is performed to discuss their advantages and limitations in detail. As main conclusions of this work, it is affirmed that different strategies have their own merits and all meet the requirements of modern high performance drives.

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.

Finite Control Set Model Predictive Control (FCS-MPC) calculates torque and flux tracking errors via a cost function that is used for selecting the optimal vector. Compared with Field Oriented Control(FOC), FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulse-width modulation (PWM) and inner proportion-integration (PI) controllers. However, the weighting factor for modifying torque and flux terms must be tuned in accordance with varying operating conditions; this is an area in which further research is needed. In this paper, a Parallel Predictive Torque Control (PPTC) with adaptive constraints is proposed as a solution for this problem. The PPTC method optimizes torque and flux terms simultaneously, and switching-state candidates are then selected in an adaptive mechanism. The key feature is that torque and flux tracking errors are constrained within the optimized boundaries. The proposed PPTC is compared with the state-of-the-art PTC method. Both simulation and experimental results confirm that the proposed method, which has no weighting factor, achieves an even better dynamic performance and robustness than the conventional PTC.

A lunenberger disturbance observer based control scheme for surface mounted permanent magnet synchronous motor (SPMSM) is proposed in this paper. Firstly, a new SPMSM model is given by considering the external disturbances and parameter mismatches. Secondly, a lunenberger observer is introduced to estimate the lumped disturbances in the speed and current loops, respectively. Thirdly, based on the observed disturbances, a speed controller is designed which is proved to be stable. Finally, an improved deadbeat-based predictive current control (DPCC) based on the estimated disturbances and the new SPMSM model is designed to control the current loop. The proposed method is implemented on a Xilinx Zynq SoC XC7Z020-CLG484-1 and FPGA implementations of the improved DPCC and conventional DPCC are compared and analyzed in detail in terms of area utilization and time consumption. Although the execution time of both methods is very short, the improved DPCC takes less time than the conventional DPCC due to the parallel processing capabilities of FPGAs. The experimental results verify that the proposed method has good dynamic performance, load disturbance suppression performance and parameter robustness.