Implementation of Micro DC Brushless Motor Controller based on Real Structured Genetic Algorithm

• Main Authors: Chih-Wei Tsai, 蔡志瑋
• Other Authors: 林俊良
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/52413026205485825800

碩士 === 國立中興大學 === 電機工程學系所 === 96 === This thesis presents the realization of a micro brushless DC motor (MBDCM) feedback system based on a real ordered genetic algorithm (RSGA) approach, which combines the advantages of conventional real genetic algorithms (RSGA) and structured genetic algorithms (SGA), for determining an optimal controller. A dynamic crossover and mutation probability adjusting method based on Butterworth filters was proposed to improve the overall searching performance of the RSGA. The optimal MBDCM controller is realized on the TMS320F240 digital signal processing (DSP) development board to achieve hardware compactness. Control design requirements include robust stability, control consumption and tracking performance. A SinCos encoder with a line drive of 128 sin/cos signals per revolution was implemented to achieve and position control. SinCos encoders have the inherent advantage of providing high resolutions via signal interpolation. The interpolation method inherited is simple yet effective, based on logic devices. To verify the effectiveness of the proposed methodology, simulations are performed on MATLAB and an experimental platform involving a motor driver, a micro brushless DC motor, and a SinCos encoder was built to verify the applicability of the proposed method in practical situations. Simulation studies and experimental results show that the proposed algorithm converges faster, excels in performance, and yields better performance for a motor feedback controller in comparison to traditional approaches.