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Corrección de Parámetros en Línea Basada en Redes Neuronales Artificiales para el Control de PMSM Utilizando el

Joseph O Akinwumi1, Yuan Gao1, Xin Yuan2

  • 1School of Engineering, University of Leicester, Leicester LE1 7RH, UK.

Sensors (Basel, Switzerland)
|January 28, 2026
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Resumen
Este resumen es generado por máquina.

Este estudio introduce una red neuronal artificial (ANN) para compensar las variaciones de parámetros en unidades de motor síncrono de imanes permanentes (PMSM). La ANN mejora el rendimiento del controlador y la robustez frente a las incertidumbres en el acoplamiento de flujo y la inductancia.

Palabras clave:
red neuronal artificialcontrol predictivo basado en modelomotor síncrono de imanes permanentesalgoritmo de decodificación esférica

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Área de la Ciencia:

  • Ingeniería Eléctrica
  • Sistemas de Control
  • Inteligencia Artificial

Sus antecedentes:

  • Las unidades de motor síncrono de imanes permanentes (PMSM) son susceptibles a la degradación del rendimiento debido a variaciones de parámetros como el acoplamiento de flujo y la inductancia bajo incertidumbres operativas.
  • La estimación precisa de parámetros es crucial para mantener el rendimiento y la eficiencia óptimos en las unidades PMSM.

Objetivo del estudio:

  • Desarrollar y evaluar un método basado en redes neuronales artificiales (ANN) para la estimación y compensación en línea de desajustes de parámetros en unidades PMSM.
  • Mejorar la robustez y el rendimiento de las unidades PMSM frente a las variaciones en el acoplamiento de flujo y la inductancia.

Principales métodos:

  • Generación de datos utilizando el Control Predictivo Basado en Algoritmo de Decodificación Esférica (SDA-MPC) con desajustes de parámetros que varían entre ±50%.
  • Entrenamiento fuera de línea de una ANN para establecer un mapeo entre las características medidas y las estimaciones de parámetros.
  • Despliegue en línea de la ANN entrenada para actualizar dinámicamente los parámetros del controlador dentro del marco SDA-MPC.

Principales resultados:

  • La compensación basada en ANN mejoró el seguimiento de la corriente y redujo la Distorsión Armónica Total (THD) bajo diversas condiciones de desajuste de parámetros.
  • En escenarios específicos, particularmente con una inductancia sobreestimada, la THD podría aumentar en comparación con la operación nominal.
  • La ANN demostró capacidades adaptativas, devolviendo el controlador al rendimiento de referencia a medida que los parámetros se normalizaban.

Conclusiones:

  • La adaptación basada en datos utilizando ANN ofrece una mayor robustez para las unidades PMSM con una sobrecarga computacional mínima.
  • El método propuesto muestra potencial para mejorar el rendimiento de las unidades PMSM bajo incertidumbres operativas realistas.
  • Se justifica una mayor investigación, incluyendo pruebas de hardware en el bucle y análisis de efectos de temperatura.