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.

Grid-connected power converters play a key role in several applications such as the integration of renewable energy sources and motor drives. For this reason, the development of high performance control strategies for this particular class of power converters has increasingly attracted the interest of both academic and industry researchers. This paper presents the predictive optimal switching sequence (OSS) direct power control (DPC) (OSS-DPC) algorithm for grid-connected converters. The OSS-DPC method belongs to the predictive-DPC family and provides the desired power references by calculating globally OSSs. To address computational and implementation issues, an efficient control algorithm, named reduced OSS-DPC, is introduced. The implementation of the proposed control strategy in a standard DSP is evaluated on a two-level power converter prototype working as a STATCOM. Experimental results show the algorithm's potential to provide high performance during both transient and steady states.

The modular multilevel converter (MMC) is currently one of the power converter topologies which has attracted more research and development worldwide. Its features, such as high quality of voltages and currents, high modularity and high voltage rating, have made the MMC a very good option for several applications including high-voltage dc (HVdc) transmission, static compensators (STATCOMs), and motor drives. However, its unique features such as the large number of submodules, floating capacitor voltages, and circulating currents require a dedicated control system able to manage the terminal variables, as well as the internal variables with high dynamical performance. In this paper, a review of the research and development achieved during the last years on MMCs is shown, focusing on the challenges and proposed solutions for this power converter still faces in terms of modeling, control, reliability, power topologies, and new applications.

In this paper, a finite control set model predictive controller for closed-loop control of an active front-end rectifier is presented. The proposed method operates in discrete time and does not require additional modulators. The key novelty of the control algorithm presented lies in the way dynamic references are handled. The control strategy is capable of providing suitable references for the source active power and the rectified voltage, without requiring additional control loops. Experimental results show that fast and accurate tracking of dynamic dc voltage and reactive power references can be achieved, while respecting the restrictions on maximum power levels of the rectifier.

In this work, a cost function design based on Lyapunov stability concepts for finite control set model predictive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal.

This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.