Affiliations: [a] College of Mechanical and Electrical Engineering of the Open University of Henan, Zhengzhou, Henan 450008, China | [b] Henan Air-Leakage Test Engineering Research Center, Zhengzhou, Henan 450002, China | [c] Academic Affairs Office of Henan Agricultural University, Zhengzhou, Henan 450002, China | [d] College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin 130000, China | [e] College of Mechanical and Electrical Engineering of Henan Agricultural University, Zhengzhou, Henan 450002, China
Correspondence:
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Corresponding author: Sheng Wang, College of Mechanical and Electrical Engineering of the Open University of Henan, Zhengzhou, Henan 450008, China. E-mail: [email protected].
Abstract: In the existing soybean breeding and planting machinery, the power source of the metering device adopts the ground wheel transmission method mostly. However, this power transmission method is likely to cause slippage during the planting operation, which will cause problems such as the increase of the missed seeding index and the increase of the coefficient of plant spacing. It is not conducive for scientific researchers to carry out breeding operations. Aiming at this problem, an electronically controlled soybean seeding system is designed, and the power of the seed metering device is derived from the motor. In order to improve the control accuracy of the electronically controlled seeding system, the precise control of the soybean seeding rate is finally realized. The electric drive soybean seeding system adopts closed-loop control, the motor model of the electric drive seeding system is established, and the transfer function of the motor is obtained. PID control based on a genetic algorithm is adopted, and the corresponding parameters are substituted into the control system simulation model established in MATLAB/SIMULINK. Field verification tests have been carried out on the conventional fuzzy PID control system and the electric drive soybean planter of the fuzzy PID control system based on a genetic algorithm. The result showed that the average of the repeat-seeding parameter is 1.30% better than the average of conventional seeding system (1.40%), the average of the miss-seeding parameter is 1.08% better than the average of conventional seeding system (2.09%) and the average of row-spacing variation parameter is 2.79% better than the average of conventional seeding system (2.34%). In conclusion, the new seeding system is robust obviously. Field trial results show that seeding with Genetic Algorithm Fuzzy control is better.
