关键词: 无刷直流电机, 星鸦优化算法, 佳点集, 随机惯性权重, 透镜成像反向学习策略, 转速控制

Abstract: Aiming at the problems of slow response speed and low control accuracy in the dual closed loop control system of brushless DC motor, and the slow convergence speed of the standard nutcracker optimization algorithm ( NOA) , an improved nutcracker optimization algorithm ( INOA) and optimized PID controller parameter tuning strategy was studied. The population was initialized using the best point set to enrich the diversity of the nutcracker population, random inertia weights were added to balance the exploration and development of the INOA, lens imaging reverse learning strategy was used to perform greedy learning on the optimal solution and expand the optimal solution interval. Four sets of benchmark test functions were selected to evaluate the performance of the INOA, further demonstrating the effectiveness and feasibility of the improved algorithm. Simulation experiments were conducted under three conditions: no-load, sudden increase in speed, and sudden increase in load. The simulation results showed that compared to traditional PID control and fuzzy PID control, the PID speed control system using the improved nutcracker optimization algorithm had a faster speed response and higher control accuracy.

Key words: brushless DC motor( BLDCM), nutcracker optimization algorithm( NOA), best point collection, random inertia weight, lens imaging reverse learning strategy, speed control