Parameter Self-Tuning of Vector Control for Permanent Magnet Synchronous Motor Based on IGAHF

In permanent magnet synchronous motor (PMSM) vector control, the speed current dual closed-loop controller simultaneously adjusts the motor speed and stator current to achieve precise control of the motor's operating state, thus ensuring system stability and performance. An improved genetic algorithm with hierarchical fitness (IGAHF) is proposed, introducing adaptive crossover and mutation probabilities that dynamically adjust based on the population's fitness levels, utilizing Halton sequence to randomize population generation, effectively covering the entire search space, better balancing global and local search, accelerating algorithm convergence, and enhancing robustness. By considering the cascade relationship in the control loop, the hierarchical fitness evaluation is achieved to control performance objectives. Matlab simulation was used to compare the control performance of three algorithms, showing that this method has better control effects and performance advantages.