@inproceedings{46e9e0505b444470b62a153295c55f10,
title = "Parameter Space Design of Speed Controller for BLDC Motor Using SIWPSO-RBFNN Algorithm",
abstract = "In this study, an intelligent method is used at the optimal parameters of a time delay based proportional integral (PI) controller for brushless direct current (BLDC) motor speed control system problem. The intelligent method uses the particle swarm optimization (PSO) method to search the optimal proportional gain and the integral gain of the PI control parameter space when the delay time varies. Then, a radial basis function neural network (RBFNN) method is used to obtain an optimal parametric fitting curve from the results of the stochastic inertia weight in PSO (SIWPSO) method. The 3D stability boundaries is shown in graphical form in the control parameter space. The optimal PI control parameters for the speed control are illustrated in a BLDC motor with a digital signal processor (DSP) based control platform. Details of the simulated results and DSP-based experimental results are also presented in this study.",
keywords = "Brushless DC motor, Particle swarm optimization, Radial basis function neural network, Time delay",
author = "Hsu, {Ya Wen} and Chen, {Guan Yan} and Perng, {Jau Woei}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2019",
month = jan,
day = "16",
doi = "10.1109/SMC.2018.00369",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2146--2151",
booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
address = "美國",
}