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

By Sergio Vazquez, José Raúl Rodríguez Rodríguez, Marco E. Rivera, Leopoldo García Franquelo, Margarita Norambuena. IEEE transactions on industrial electronics (1982. Print), 2017

1,494 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e1b442e2bfe2e05c7ba870474c19bdc6fd03ec45

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In Control and Systems Engineering

1. Xiaojie Su
2. Ligang Wu
3. Tobias Geyer
4. Ralph M. Kennel
5. Jianxing Liu
6. Yabin Gao
7. Maxime Wack
8. Tomislav Dragicevic

In Electrical and Electronic Engineering

1. Petros P. Karamanakos
2. Tobias Geyer
3. Ralph M. Kennel
4. Tomislav Dragicevic
5. Mateja Novak

Citations in the corpus

• Disturbance-Observer-Based Control for Air Management of PEM Fuel Cell Systems via Sliding Mode Technique (2019, background): “Many model-based control strategies have been proposed for the control design of fuel cell air-feed systems, ranging from linear state feedback control [8], model predictive control [9], [10], intelligent control [11], and sliding mode control (SMC) [3], [6], [12]–[14].”
• Multilevel Converters: Control and Modulation Techniques for Their Operation and Industrial Applications (2017, neutral): “Other approaches, like sliding mode control or model predictive control, have been also explored [37], [38], [56]–[58].”
• Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, background): “mission systems (FACTS), and uninterruptible power supplies (UPS), to name a few [14]–[20].”
• Weighting Factor Design in Model Predictive Control of Power Electronic Converters: An Artificial Neural Network Approach (2019, background): “To sum up, it can be concluded that optimization of up to four weighting factors, which covers basically all applications of FS-MPC in power electronics, can be considered as the upper limit with relatively standard hardware.”
• Guidelines for the Design of Finite Control Set Model Predictive Controllers (2020, background): “As a result, many algorithms in the framework of MPC have hitherto been developed for several power electronic systems, ranging from low- to high-power applications [9]– [12].”