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Robust sensorless vector control of an induction machine using Multiobjective Adaptive Fuzzy Luenberger Observer.

M Bahloul1, L Chrifi-Alaoui2, S Drid3

  • 1International Energy Research Centre (IERC),Tyndall National Institute, Cork, T12 R5CP, Ireland; Lab-STA Laboratory, National, School of Engineering of Sfax, 3038 Sfax, Tunisia.

ISA Transactions
|February 4, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sensorless control method for induction motors, enhancing speed estimation accuracy at low speeds. The approach addresses observer sensitivity to rotor resistance variations for improved performance.

Keywords:
Adaptive Luenberger observeInduction motor drivePole placement's constraintRobust sensorless vector controlTakagi- Sugeno fuzzy logic

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Robotics

Background:

  • Induction motors are widely used in industrial applications.
  • Sensorless control is desirable for cost and reliability.
  • Accurate speed estimation, especially at low speeds, remains a challenge.

Purpose of the Study:

  • To develop an inherent speed estimation scheme for induction motor sensorless control.
  • To address low-speed operation, observer pole assignment, and rotor resistance uncertainty issues concurrently.
  • To improve the robustness and dynamic performance of speed estimation.

Main Methods:

  • Design of a Multiobjective Adaptive Fuzzy Luenberger Observer (MAFLO).
  • Utilization of Takagi-Sugeno fuzzy models for observer design.
  • Application of Lyapunov theory for observer gain design.
  • Employing L2 techniques and D-stability analysis for robustness and performance.
  • Formulation of design conditions using Linear Matrix Inequalities (LMIs).

Main Results:

  • Concurrent resolution of sensorless control at low speeds, observer pole assignment, and rotor resistance uncertainty.
  • Minimized sensitivity of the observer to rotor resistance variations.
  • Guaranteed specified observer dynamic performance.
  • Experimental validation demonstrating effectiveness despite rotor resistance uncertainties.

Conclusions:

  • The proposed MAFLO provides an effective solution for sensorless induction motor control.
  • The method enhances speed estimation accuracy and robustness, particularly at low speeds.
  • The LMI-based design ensures reliable performance under parameter uncertainties.