@article{MAKHILLJEAS2017122315288,
    title = {Metaheuristic Design and Optimization of Fuzzy-Based
SRM Speed Controller using Ant Colony Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {23},
    pages = {7382-7388},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.7382.7388},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.7382.7388},
    author = {Mohamed and},
    keywords = {Switched Reluctance Motor (SRM),Fuzzy Logic Controller (FLC),Ant Colony Optimization (ACO),speed control,flexibility,precision},
    abstract = {For electrical drives good dynamic performance is mandatory so as to respond to the changes in
command speed and torques. Thus various speed control techniques are being used for real time application.
The speed of Switched Reluctance Motor (SRM) can be adjusted to a great extend so as to provide relatively
easy control and high performance. There are several conventional and numeric types of controllers intended
for controlling the SRM speed and executing various tasks, PID controller, Fuzzy Logic Controller (FLC) or the
combination between them, fuzzy-swarm, fuzzy-neural networks, fuzzy-genetic algorithm, fuzzy-ants colony,
fuzzy-particle swarm optimization. We would like to clarify in this study the use of Ant Colony Optimization
Algorithm (ACO) to optimize the scaling factors of fuzzy logic controller for speed regulation of SRM. The
obtained results were simulated on MATLAB/Simulink environment. Excellent flexibility and adaptability as well
as high precision and good robustness are obtained by the proposed strategy. The simulations results
demonstrate that the proposed ACO-FLC speed controller realize a good dynamic behavior of SRM compared
with conventional FLC controller.}
    }