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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Advanced direct torque control based on neural tree controllers for induction motor drives.

Oualid Aissa1, Abderrahim Reffas2, Abdelbasset Krama3

  • 1LPMRN Laboratory, Faculty of Sciences and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, Algeria.

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Summary

This study presents a novel decision tree direct torque control (T-DTC) for induction motors, significantly reducing torque and flux ripples. This advanced method enhances control accuracy and robustness for electric motor applications.

Keywords:
Artificial neural networkDecision treeDirect torque controlInduction motorOPAL-RT 5600Virtex 6 FPGA

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Conventional direct torque control (DTC) suffers from significant flux and torque ripples.
  • Hysteresis controllers and traditional switching tables limit the performance of existing DTC methods.

Purpose of the Study:

  • To introduce a novel decision tree-based direct torque control (T-DTC) strategy for induction motors.
  • To substantially reduce flux and torque ripples compared to conventional DTC.
  • To enhance the accuracy and robustness of induction motor control.

Main Methods:

  • Developed a T-DTC approach utilizing artificial neural networks and M5 Prime model trees.
  • Replaced conventional hysteresis controllers with M5 Prime model trees.
  • Implemented a novel decision tree table using the C4.5 classifier algorithm.

Main Results:

  • Demonstrated significant reduction in flux and torque ripples.
  • Achieved enhanced accuracy and robustness in induction motor control.
  • Validated the T-DTC strategy through MATLAB/Simulink simulations and real-time HIL platform testing.

Conclusions:

  • The proposed T-DTC strategy offers notable improvements over existing techniques.
  • T-DTC provides a more effective method for controlling induction motors with reduced ripples.
  • The integration of decision trees and neural networks shows promise for advanced motor control.