A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor

Abstract-The proportional-integral-derivative controllers were the most popular controllers of this century because of their remarkable effectiveness, and simplicity of implementation. However, proportional-integral-derivative controllers are usually poorly tuned in practice. This article presents a...

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Bibliographic Details
Published in:Electric power components and systems 2015-02, Vol.43 (3), p.309-319
Main Authors: El-Wakeel, Amged Saeed, Ellissy, Abou El-Eyoun Kamel Mohamed, Abdel-hamed, Alaa Mohamed
Format: Article
Language:English
Subjects:
Bacteria
bacterial foraging
Controllers
Direct current
Electric currents
Electric motors
Electric power
Forages
genetic algorithm
hybrid optimization techniques
Matlab
optimal control
Optimization
Optimization algorithms
particle swarm optimization
Permanent magnet brushless DC motor
Permanent magnets
PID controller
Proportional integral derivative
Simulation
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Summary:Abstract-The proportional-integral-derivative controllers were the most popular controllers of this century because of their remarkable effectiveness, and simplicity of implementation. However, proportional-integral-derivative controllers are usually poorly tuned in practice. This article presents a hybrid particle swarm optimization and bacterial foraging techniques for determining the optimal parameters of a proportional-integral-derivative controller for speed control of a permanent magnet brushless DC motor. The first part of the article deals with the system modeling and its verification where a model of modest accuracy cannot be expected to give a fair comparison of different controllers. The remaining parts of the article present the application of different optimization techniques to tune the proportional-integral-derivative controller as applied to the motor model. The particle swarm optimization, bacterial foraging, and bacterial foraging-particle swarm optimization algorithms are implemented in MATLAB while the GA Toolbox is used. The performance of the tuned controllers is simulated and experimentally verified to evaluate the main characteristics of each one. It is found that the proposed hybrid bacterial foraging-particle swarm optimization technique is more efficient in improving the step response characteristics and achieving the desired performance indices.
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2014.981320