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.

Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.

With the increasing penetration of distributed energy resources into islanded microgrids, a grid-forming inverter (GFI) has become the key element interfacing renewable energy sources. Usually, the GFI is employed with an output filter to minimize the harmonic content achieving high-quality output voltage regulation. To this end, model-predictive control (MPC) has been widely proposed to control the output voltage of GFI systems due to the high-quality performance, fast control response, and straightforward handling of constraints. However, an accurate model of the system is required for the conventional MPC to avoid a suboptimal performance under uncertainties. To overcome this known drawback, a novel model-free predictive control is proposed in this article. Consequently, the output voltage of an $LCL$ -filtered GFI is regulated without the knowledge of the physical model.

A cascade control strategy is employed in the boost inverter to generate a sinusoidal signal at grid frequency with very low distortion. The internal loop of the cascade employs a switching surface based on the difference of the inductor currents to induce sliding motions in the power stage. The outer loop in turn establishes the reference of the internal loop and ensures the tracking of an external sinusoidal signal at 50 Hz. The reported approach is analytical and is based on the equivalent control method. The root locus of the capacitor voltages at the equilibrium point of the inner loop is obtained assuming a constant value of the loop reference. In the particular case of a zero reference, sinusoidal variations at grid frequency are superposed to the corresponding equilibrium point and the resulting ideal dynamics are linearised yielding the control to output transfer function of the system. A proportional‐integral (PI) compensator is designed for both large bandwidth and small phase error. Experimental results in a 500 W prototype are in perfect agreement with the analytical predictions.

The three‐phase VIENNA rectifier supplying a regulated DC bus in a micro‐grid architecture is controlled in this study by means of a sliding‐mode regulation loop, which imposes a loss‐free resistor (LFR) behaviour in each phase for power factor correction. Assuming equal parameters per phase and that the intermediate node between the output capacitors of the DC link is the neutral point of the AC generator, the rectifier can then be modelled as a tetra‐port LFR with three decoupled resistive input ports and one output port characterised by a power source representing the input power absorbed by the rectifier. After demonstrating that the resulting sliding dynamics are globally stable, the control is analogically executed, this only requiring an analogue multiplier, several operational amplifiers and some auxiliary digitally implemented logic circuitry. Experimental results for different power levels and frequencies supplied by a low‐power wind generator are in perfect agreement with the theoretical predictions.