Microbrushless DC Motor Control Design Based on Real-Coded Structural Genetic Algorithm
This paper presents the realization of a microbrushless dc motor (MBDCM) feedback system based on a real-coded structural genetic algorithm (RSGA), which combines the advantages of conventional real genetic algorithms and structured genetic algorithms for optimal control design. In the RSGA, a dynamic crossover and mutation probability adjusting method mimicking the characteristics of Butterworth filters is proposed to enhance the search performance. A SinCos encoder with a line drive of 128 sin/cos signals per revolution is implemented to achieve precise positioning. The SinCos encoder possesses the advantage of high resolution via signal interpolation. The method inherited is simple yet effective, based on logic devices. To verify effectiveness of the proposed methodology, simulations are conducted and an experimental platform with a digital signal processing unit, a motor driver, a MBDCM, and a SinCos encoder is built to verify applicability of the proposed method. The experimental results demonstrating the aforementioned method work properties correlate well with the expectation.