Model Predictive Control—A Simple and Powerful Method to Control Power Converters
By Samir Kouro, Patricio Cortes, René E. Vargas, Ulrich Ammann, José Raúl Rodríguez Rodríguez. IEEE transactions on industrial electronics (1982. Print), 2009
939 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/70fabb2286aa33718d16408300f0e62a2f88dbf2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Tobias Geyer
- José Raúl Rodríguez Rodríguez
- Patricio Cortes
- César A. Silva
- Christian A. Rojas
- José R. Espinoza
- Haitham Abu-Rub
- Marco E. Rivera
In Electrical and Electronic Engineering
- Tobias Geyer
- José Raúl Rodríguez Rodríguez
- Haitham Abu-Rub
- Samir Kouro
- Patricio Cortes
- José R. Espinoza
- Christian A. Rojas
- Marco E. Rivera
Citations in the corpus
- Imposed Sinusoidal Source and Load Currents for an Indirect Matrix Converter (2012, background): “To deal with the high processing power needed for these microprocessors, some research has shown the positive potential of model predictive control (MPC) techniques in many power electronics applications [14], [15].”
- Review on model predictive control: an engineering perspective (2021, background): “[52] examined MPC regarding control of power converters.”
- Delay Compensation in Model Predictive Current Control of a Three-Phase Inverter (2012, background): “Several applications of this control principle are summarized in [1].”
- Model Predictive Control of Multilevel Cascaded H-Bridge Inverters (2010, methodology): “Depending on the specific application, it is possible to include reduction of the switching frequency, spectrum shaping, and others [18].”
- Model predictive control of high power converters and industrial drives (2016, methodology): “4 Equivalence with SVM A popular alternative to CB-PWM is SVM [18].”
- Algebraic Tuning Guidelines for Model Predictive Torque and Flux Control (2018, background): “This tuning process is usually performed on a trial-and-error basis [2] and is thus considered an unresolved issue [3].”
- Predictive Torque and Flux Control Without Weighting Factors (2013, methodology): “The discrete system model is evaluated for every possible actuation sequence and then compared with the reference in order to select the best voltage vector [16], [17].”
- Experimental Comparison of Model Predictive Control and Cascaded Control of the Modular Multilevel Converter (2015, methodology): “In this paper, a controller for the MMC is developed which is based on the finite control step MPC introduced in [17].”