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.

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.

The evolution of power electronics and its control has been mainly driven by industry applications and influenced by the development achieved in several technologies, such as power semiconductors, converter topologies, automatic control, and analog and digital electronics. Digital signal processors (DSPs), in particular, have experienced an exponential development in processing power, which until now has not been fully exploited for control purposes in power converters and drive applications. Presently, the control system technology finds itself in a paradigm-changing tipping point, in which more demanding control goals, system flexibility, and functionalities required by emerging applications are driving the control system technology development, in addition to stabilization and robustness, which was the main focus in the past. This article walks briefly through the history of the mainstream power converter control scene, with an emphasis on the more recent introduction of predictive control, and gives a glimpse on the challenges and possibilities ahead. Special attention is given to finite control set (FCS)-model predictive control (MPC), because of its simplicity, flexibility, inherent adaptation to power electronic circuits and their discrete nature, both in the finite amount of switching states and the digital implementation with microprocessors.

It is well known that predictive control methods can be affected by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties is a recurrent concern at the moment of evaluating the viability of this method for power electronics applications. This paper proposes an analytic approach to examine the influence of model parametric uncertainties on the prediction error of FCS-MPC for current control in a three-phase two-level inverter. The analysis shows that the prediction error is not only determined by parametric mismatch but also by the instantaneous values of load current and inverter output voltage. This implies that within each sampling period of the predictive algorithm several conditions of prediction error are generated, as multiple voltage vectors are evaluated. Simulation and experimental results are provided and discussed showing the effects of inaccuracies in the modeling of load resistance and inductance parameters on the performance of FCS-MPC. Even though steady-state performance is noticeably affected with parameter changes, especially when the load inductance is overestimated by the model, its fast transient step response is less affected by parameter changes.

Model predictive control (MPC) methods for applications in power converters have received considerable attention in recent years. The idea behind MPC is to use a mathematical model of the system to predict its future behavior within a predefined time. An optimization problem that includes the control objectives, the predicted variables, and possible constraints of the system is solved, yielding the control actions to be applied.

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.