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In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 $\mu s$). Fast response speed to variations in voltage reference and load resistance can be achieved t. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) strategies for power conversion devices benefit from extending the prediction horizon length. Solving this problem relies on the definition of the underlying integer least-squares problem. Sphere decoding algorithm (SDA) has been extensively used in previous works as an approach to solve this problem. In this article, a parallel and fully scalable K-best SDA hardware design is proposed as an alternative. The K-best SDA establishes a different breadth-first search strategy, which addresses some of the main drawbacks of the SDA. Through experimental tests based on an uninterruptible power supply, the K-best SDA performance for long prediction horizon FCS-MPC is assessed and verified. Results demonstrate key beneficial aspects through which the K-best SDA is capable of rendering an improved control performance when compared to the conventional SDA. [ABSTRACT FROM AUTHOR]

A finite control set model predictive control (FCS-MPC) strategy consists of a prediction model, a cost function and an optimization algorithm. Consequently, the performance of the FCS-MPC depends on the proper design of these three elements. This article assesses the influence of the prediction model of an uninterruptible power supply (UPS). Since the load connected to the voltage source inverter (VSI) affects the dynamic of the system state variables, the load dynamic should be included in the system model. This makes the design of the prediction model a challenge because the load connected to the VSI is generally unknown. To deal with this uncertainty, this work proposes an augmented prediction model based on a state observer that includes as many harmonic components as necessary to accurately represent the output current. The performance of the FCS-MPC for a UPS is evaluated in a laboratory prototype using the proposed and the conventional prediction models. Experimental results show that the proposed solution provides a more accurate representation of the output current, improving the system performance. [ABSTRACT FROM AUTHOR]

In this article, a model predictive control (MPC) strategy based on the optimal switching sequence (OSS) concept for a single-phase grid-connected H-bridge neutral-point-clamped (H-NPC) power converter is presented. The proposed OSS-MPC algorithm considers both the grid current tracking error and the dc-link capacitor voltage balance. Special emphasis is placed on the power converter control region in order to design suitable switching sequence candidates for this multiobjective control problem. Additionally, based on an analysis of the weighting factor effect over closed-loop performance, it is possible to demonstrate that this controller parameter is relatively easy to adjust. In fact, the weighting factor only affects the peak current during transients, with no effect over the steady-state performance. As a result, the proposed OSS-MPC provides a fast closed-loop dynamic to the H-NPC converter, which operates with a fixed switching frequency at all times. This predictive control strategy is experimentally validated in a 3.5-kVA laboratory setup. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) based on optimal switching sequences (OSSs) for a single-phase grid-connected full-bridge neutral-point-clampled (NPC) power converter is presented. The predictive control algorithm is formulated in terms of OSSs, which was originally proposed to govern three-phase power converters. In this paper, the OSS concept is extended to control single-phase power converters. The proposed MPC algorithm belongs to the continuous control set MPC and is able to provide fixed switching frequency while handling system constraints. The proposed algorithm has been experimentally tested in an NPC power converter prototype. Experimental results show the desired fixed switching behavior in the steady-state condition and the intrinsic fast dynamic provided by MPC during transients. Furthermore, the test outcomes demonstrate the robustness of the proposed controller under large system parameter deviations. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on the deployment of predictive control in power converters and electrical drives. [ABSTRACT FROM PUBLISHER]

Model-based predictive control (MPC) for power converters and drives is a control technique that has gained attention in the research community. The main reason for this is that ?although MPC presents high computational burden, it can easily handle multivariable case and system constraints and nonlinearities in a very intuitive way. Taking advantage of that, MPC has been successfully used for different applications such as an active front end (AFE), power converters connected to resistor?inductor RL loads, uninterruptible power supplies, and high-performance drives for induction machines, among others. This article provides a review of the application of MPC in the power electronics area. [ABSTRACT FROM PUBLISHER]

The use of an inverter with an output LC filter allows for generation of output sinusoidal voltages with low harmonic distortion, suitable for uninterruptible power supply systems. However, the controller design becomes more difficult. This paper presents a new and simple control scheme using predictive control for a two-level converter. The controller uses the model of the system to predict, on each sampling interval, the behavior of the output voltage for each possible switching state. Then, a cost function is used as a criterion for selecting the switching state that will be applied during the next sampling interval. In addition, an observer is used for load-current estimation, enhancing the behavior of the proposed controller without increasing the number of current sensors. Experimental results under linear and nonlinear load conditions, with a 5.5-kW prototype, are presented, verifying the feasibility and good performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]