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Related Experiment Video

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Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector

Muhammad Affan1,2, Riaz Uddin1,2

  • 1Haptics, Human-Robotics and Condition Monitoring Lab (Affiliated Lab of National Center of Robotics and Automation - HEC Pakistan), Islamabad, Pakistan.

International Journal of Control, Automation, and Systems
|August 2, 2021
PubMed
Summary

A novel Learning and Adaptive Model-based Predictive Control (LAMPC) enhances three-phase induction motor vector control. This advanced technique improves dynamic response, tracking precision, and disturbance handling for efficient motor drives.

Keywords:
Induction motormodel predictive controlvector controlvoltage vectors

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Area of Science:

  • Electrical Engineering
  • Control Systems Engineering

Background:

  • High-speed microprocessors enable complex vector control algorithms for motor drives.
  • Existing techniques like PI, DTC, FOC, and MPC have limitations, driving the need for advanced methods.

Purpose of the Study:

  • To propose a new Learning and Adaptive Model-based Predictive Control (LAMPC) for three-phase induction motor vector control.
  • To enhance motor drive performance by addressing limitations of current vector control techniques.

Main Methods:

  • The proposed LAMPC method adaptively updates the induction motor's dynamic model using a receding horizon principle for the inner current control loop.
  • A Brain Emotional Learning-based Intelligent Controller (BELIC) is employed for the outer speed control loop.

Main Results:

  • The LAMPC methodology demonstrates desirable dynamic response, precise tracking, and effective disturbance handling.
  • Satisfactory steady-state performance is achieved with the proposed control strategy.

Conclusions:

  • The LAMPC approach offers significant improvements over existing vector control techniques for induction motors.
  • Simulation results validate the effectiveness and significance of the proposed LAMPC method through comparisons.