Research on Brushless DC Motor Control with Improved Nutcracker Optimization Algorithm
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.
brushless DC motor(BLDCM)nutcracker optimization algorithm(NOA)best point collectionrandom inertia weightlens imaging reverse learning strategyspeed control
万方数据
维普
