Parameter Space Design of Speed Controller for BLDC Motor Using SIWPSO-RBFNN Algorithm

• Published in: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 2146 - 2151
• Main Authors: Hsu, Ya-Wen, Chen, Guan-Yan, Perng, Jau-Woei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Brushless DC motor; Particle swarm optimization; Radial basis function neural network; Time delay
• ISSN: 2577-1655
• DOI: 10.1109/SMC.2018.00369

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.