Wang Song, Zhao Bin, Yi Shujuan, Zhou Zheng, Zhao Xue
College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
College of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.
Sensors (Basel). 2022 Sep 3;22(17):6678. doi: 10.3390/s22176678.
To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index () was 95.83%, the average multiple index () was 1.11%, the average missing index () was 3.23%, and the average precision index () was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.
为提高电动播种机的播种电机控制性能，设计了一种基于遗传粒子群优化（GAPSO）的电动播种模糊PID控制策略。由于模糊控制器的参数难以确定，在模糊控制器的输入端应用了两个量化因子，并在模糊控制器的输出端引入了三个比例因子。采用遗传筛选方法将遗传算法（GA）和粒子群优化（PSO）结合成GAPSO。在更新之前，引入GAPSO来优化两个量化因子、三个比例因子和三个特征函数的初始值。仿真结果表明，基于GAPSO的模糊PID控制器系统的最大超调量为0.071%，调节时间为0.408 s，稳态误差为3.0693×10，表明该策略具有优异的控制性能。田间试验结果表明，电动播种机的性能优于地轮链链轮播种机。播种机作业速度为6km/h时，平均合格指数（）为95.83%，平均复式指数（）为1.11%，平均漏播指数（）为3.23%，平均精度指数（）为14.64%。研究结果为电动播种机模糊PID控制器的参数整定方式提供了参考。
