Classic, fuzzy and predictive dtc strategies for the PMSM using the bacterial foraging algorithmas an online parameter estimator


  • Gabriel Noriega Simon Bolivar University
  • José Restrepo Simón Bolívar University
  • Alexander Bueno Simón Bolívar University
  • José M. Aller Simón Bolívar University
  • María I. Giménez Simón Bolívar University
  • Victor Guzmán Simón Bolívar University



PMSM, DTC, bacterial foraging, fuzzy inference system, parameter estimation, predictive control


This work presents a comparison between four control techniques applied to drive a PMSM: Classic DTC, Modified DTC with a Fuzzy Inference System, Predictive DTC and Predictive DTC with Fuzzy Inference System. Parameters estimation for the predictive strategies is performed using a population-based search algorithm (Bacterial Foraging), which is able to calculate on line the PMSM parameters. The electric torque and stator flux linkages experimental results show that the predictive strategies that use the machine parameters estimated by the Bacterial Foraging Algorithm present a significant improvement when compared with non predictive techniques.

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Author Biographies

Gabriel Noriega, Simon Bolivar University

Power Electronics Industrial Systems Group.

José Restrepo, Simón Bolívar University

Power Electronics Industrial Systems Group.

Alexander Bueno, Simón Bolívar University

Power Electronics Industrial Systems Group.

José M. Aller, Simón Bolívar University

Power Electronics Industrial Systems Group.

María I. Giménez, Simón Bolívar University

Power Electronics Industrial Systems Group.

Victor Guzmán, Simón Bolívar University

Power Electronics Industrial Systems Group.


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How to Cite

Noriega, G., Restrepo, J., Bueno, A., Aller, J. M., Giménez, M. I., & Guzmán, V. (2012). Classic, fuzzy and predictive dtc strategies for the PMSM using the bacterial foraging algorithmas an online parameter estimator. Revista Facultad De Ingeniería Universidad De Antioquia, (64), 182–194.

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