This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.

State of the Art of Finite Control Set Model Predictive Control in Power Electronics

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.

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Model Predictive Control for Power Converters and Drives: Advances and Trends

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.

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Model Predictive Control: A Review of Its Applications in Power Electronics

When control schemes based on finite control set model predictive control are experimentally implemented, a large amount of calculations is required, introducing a considerable time delay in the actuation. This delay can deteriorate the performance of the system if not considered in the design of the controller. In this paper, the problem is described, and the solution to this issue is clearly explained using a three-phase inverter as an example. Experimental results to validate this solution are shown.

Delay Compensation in Model Predictive Current Control of a Three-Phase Inverter

This paper presents a model predictive current control algorithm that is suitable for multilevel converters and its application to a three-phase cascaded H-bridge inverter. This control method uses a discrete-time model of the system to predict the future value of the current for all voltage vectors, and selects the vector which minimizes a cost function. Due to the large number of voltage vectors available in a multilevel inverter, a large number of calculations are needed, making difficult the implementation of this control in a standard control platform. A modified control strategy that considerably reduces the amount of calculations without affecting the system's performance is proposed. Experimental results for five- and nine-level inverters validate the proposed control algorithm.

Model Predictive Control of Multilevel Cascaded H-Bridge Inverters

In this paper, we present a predictive control algorithm that uses a state-space model. Based on classical control theory, an exact discrete-time model of an induction machine with time-varying components is developed improving the accuracy of state prediction. A torque and stator flux magnitude control algorithm evaluates a cost function for each switching state available in a two-level inverter. The voltage vector with the lowest torque and stator flux magnitude errors is selected to be applied in the next sampling interval. A high degree of flexibility is obtained with the proposed control technique due to the online optimization algorithm, where system nonlinearities and restrictions can be included. Experimental results for a 4-kW induction machine are presented to validate the proposed state-space model and control algorithm.

H. Miranda

Papers

Predictive Torque Control of Induction Machines Based on State-Space Models