Topology Optimization of Rotor Structure in Brushless DC Motor With Concentrated Windings Using Genetic Algorithm Combined With Cluster of Material

2012 ◽  
Vol 48 (2) ◽  
pp. 899-902 ◽  
Author(s):  
Takeo Ishikawa ◽  
Kyoichi Nakayama
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Rotor Structure

Optimal core shape design for cogging torque reduction of brushless DC motor using genetic algorithm

2000 ◽  
Vol 36 (4) ◽  
pp. 1927-1931 ◽  
Author(s):  
Ki-Jin Han ◽  
Han-Sam Cho ◽  
Dong-Hyeok Cho ◽  
Hyun-Kyo Jung
Keyword(s):  
Genetic Algorithm ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Shape Design ◽  
Cogging Torque ◽  
Brushless Dc

Online self-adaptive proportional-integral-derivative control for brushless DC motor based on variable universe fuzzy inference system optimized by genetic algorithm

2021 ◽  
pp. 095440622110603
Author(s):  
Song Kewei ◽  
Ze Zhang ◽  
Hu Wang ◽  
Fang Hui
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Brushless Dc ◽  
Variable Universe ◽  
Control Rules ◽  
Self Adaptive

In this study, we propose a novel robust online self-adaptive Proportional-Integral-Derivative (PID) control design for Brushless DC Motor (BLDCM) speed system under different operating conditions. The online adaptive tuning for PID parameters is realized accurately by optimizing the control rules of variable universe fuzzy inference with a modified genetic algorithm (GA). Based on the variable fuzzy inference theory, the method of solving contraction–expansion factor in real-time through fuzzy inference is proposed. Furthermore, the process to optimize two inference rules by GA is improved to get optimal control rules for adjusting PID parameters. Finally, multiple sets of simulations and experiments are conducted to validate the proposed controller in different conditions by building Simulink models and setting up experiment platforms. The results of this study not only demonstrate the effectiveness of the proposed controller but also provide technical suggestions for the speed control of BLDCM.

scholarly journals Optimization of Proportional Integral Derivative Parameters of Brushless Direct Current Motor Using Genetic Algorithm

2020 ◽  
pp. 24-32
Author(s):  
Isaiah Adebayo ◽  
David Aborisade ◽  
Olugbemi Adetayo
Keyword(s):  
Genetic Algorithm ◽  
Direct Current ◽  
Pid Controller ◽  
Absolute Error ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Bldc Motor ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Brushless Dc

Optimal performance of the Brushless Direct Current (BLDC) motor is to be realized using an efficient Proportional Integral Derivative (PID) controller. However, conventional tuning technique fails to perform satisfactorily under parameter variations, nonlinear conditions and time delay. Also using conventional technique to tune the parameters gain of the PID controller is a difficult task. To overcome these difficulties, modern heuristic optimization technique are required to optimally tune the Proportional, Integral, Derivative of the controller for optimal speed control of three phase BLDC motor. Thus, genetic algorithm (GA) based PID controller was used to achieve a high dynamic control performance. The Brushless DC Motor mathematical equation which describes the voltage and corresponding rotational angular speed and torque of the brushless DC motor was employed using electrical DC Machines theorem. The Genetic algorithm was further analyzed by adopting the three common performance indices i.e. Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE) in order to capture and compare the most suitable BLDC Motor speed and torque control characteristics. All simulations were done using MATLAB (R2018a). The simulation result showed that the system with GA-PID controller had the better system response when compared with the existing technique of ZN-PID controller.

Sign in / Sign up

Export Citation Format

Share Document