Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

Jamal Abd Ali, M. A. Hannan, Azah Mohamed, Maher G.M. Abdolrasol

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

© 2015 Elsevier Ltd. All rights reserved. This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.
Original languageEnglish
Pages (from-to)49-62
Number of pages42
JournalMeasurement: Journal of the International Measurement Confederation
DOIs
Publication statusPublished - 01 Jan 2016
Externally publishedYes

Fingerprint

fuzzy mathematics
Induction motors
Fuzzy logic
Controllers
fitness
Membership functions
Particle swarm optimization (PSO)
torque
damping
simulation
speed
Transient analysis
MATLAB
Torque
Rotors
Damping
particle
test

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "{\circledC} 2015 Elsevier Ltd. All rights reserved. This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.",
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Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm. / Ali, Jamal Abd; Hannan, M. A.; Mohamed, Azah; Abdolrasol, Maher G.M.

In: Measurement: Journal of the International Measurement Confederation, 01.01.2016, p. 49-62.

Research output: Contribution to journalArticle

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