Motor Parameter Identification Using Genetic Algorithm

In this article, I will provide a detailed explanation of the motor parameter identification process using genetic algorithms, along with key implementation steps and methodologies. The entire procedure is implemented through MATLAB M-files, which allows for better understanding of the algorithm's implementation principles and underlying mechanisms. The genetic algorithm implementation typically includes key functions for initialization, fitness calculation, selection, crossover, and mutation operations, enabling optimized parameter search through evolutionary computation. Through this approach, we can achieve more accurate identification of motor parameters, thereby enhancing the performance and efficiency of motor systems. The M-file implementation demonstrates how to encode motor parameters as chromosomes, define appropriate fitness functions based on motor performance criteria, and optimize parameters through iterative evolution. The code structure includes modules for population initialization, fitness evaluation using motor model simulations, genetic operations, and convergence checking. If you are interested in motor parameter identification and genetic algorithms, this article will provide valuable information and insights into practical implementation techniques, algorithm customization for specific motor types, and performance optimization strategies. The implementation also covers handling of constraint parameters and convergence criteria settings for stable identification results.