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doi: 10.3934/dcdss.2020210

Design and experiment of seeding electromechanical control seeding system based on genetic algorithm fuzzy control strategy

College of mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 45002, China

* Corresponding author: Yongchang Yu

Received  April 2019 Revised  May 2019 Published  December 2019

Traditional soybean seeders are driven by land wheels, which are easy to slip in complex operating conditions, resulting in the increased miss-seeding index and row-spacing coefficient of variation, etc. For solving these problems, a soybean electrical-control seeding system was designed in this paper. When the system operates, a Hall-type speed sensor in the system collected the information of seeding speed, and then the theoretic seeder speed was calculated out by PLC with row-spacing calculation formula; with fuzzy control strategies, the real-time seeder speed collected by a coder was used for optimal control of the stepping motor for seeders, in order to yield the best seeder speed and improve the seeding precision. Field tests showed that, when seeding the soybean with the electrical-control seeding system, the repeated-seeding index averaged 1.3%, less than 1.42 percentage points compared to that of traditional seeding; the miss-seeding index averaged 1.08%, less than 2.09 percentage points compared to that of traditional seeding; and the row-spacing coefficient of variation averaged 2.79%, less than 2.34 percentage points compared to that of traditional seeding. The seeding effect was great.

Citation: Sheng Wang, Xue An, Chen Yang, Long Liu, Yongchang Yu. Design and experiment of seeding electromechanical control seeding system based on genetic algorithm fuzzy control strategy. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020210
References:
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show all references

References:
[1]

Z. Bo and H. Wu, Pharmacological characteristics analysis of two molecular structures, Applied Mathematics and Nonlinear Sciences, 2 (2017), 93-110.  doi: 10.21042/AMNS.2017.1.00008.  Google Scholar

[2]

L. Q. ChenC. L. Zhang and R. Wu, Design and test of electronic control seeding system for maize, Transactions of the Chinese Society for Agricultural Machinery, 48 (2017), 51-59.   Google Scholar

[3]

T. CuiL. Yang and S. Shi, Air-suction corn precision metering device with mechanical supporting plate to assist carrying seed, Transactions of the Chinese Society for Agricultural Machinery, 43 (2012), 48-53.   Google Scholar

[4]

W. Gao and W. Wang, New isolated toughness condition for fractional (g, f, n) - critical graph, Colloquium Mathematicum, 147 (2017), 55-65.  doi: 10.4064/cm6713-8-2016.  Google Scholar

[5]

K. H. LeeS. W. BackS. H. YuY. J. Kim and S. O. Chung, An image-based application rate measurement system for a granular fertilizer application, Transactions of the Chinese Society of Agricultural Engineering, 57 (2014), 679-687.   Google Scholar

[6]

H. W. LiX. R. Zhang and J. He, Design and experiment on no-till planter in horizontal residue-throwing finger-wheel type for maize, Transactions of the Chinese Society for Agricultural Machinery, 41 (2010), 39-43.   Google Scholar

[7]

L. C. LiuY. X. Gu and J. Yuan, Fis-based method to generate bivariate control parameters regulation sequence for fertilization, Transactions of the Chinese Society of Agricultural Engineering, 27 (2011), 134-139.   Google Scholar

[8]

F. C. Liu, Y. L. Liu, D. H. Jin, X. Y. Jia and T. T. Wang, Research on workshop-based positioning technology based on internet of things in big data background, Complexity, 2018 (2018), Article ID 7875460, 11 pages. doi: 10.1155/2018/7875460.  Google Scholar

[9]

P. X. MengD. Y. Geng and Y. H. Li, Design and test on telescopic clip finger type of metering device, Transactions of the Chinese Society of Agricultural Machinery, 46 (2016), 38-45.   Google Scholar

[10]

F. W. MengC. Y. Liu and L. F. Liu, Genetic algorithm based parameter selection of permanent magnet linear synchronous motor servo system design, Journal of Tsinghua University, 52 (2012), 1751-1757.   Google Scholar

[11]

X. P. WangM. Chen and W. Lu, Design and experiment of optimization control system for variable fertilization in winter wheat field based on fuzzy pid, Transactions of the Chinese Society of Agricultural Machinery, 47 (2016), 71-76.   Google Scholar

[12]

X. WangC. Y. Liang and S. J. Yi, Pid control strategy of the variable rate fertilization control system, Transactions of the Chinese Society for Agricultural Machinery, 41 (2010), 157-162.   Google Scholar

[13]

Q. J. WangH. W. Li and J. He, Design and experiment on concave disc type maize ridge-till andno-till planter, Transactions of the Chinese Society of Agricultural Engineering, 27 (2011), 117-122.   Google Scholar

[14]

S. XiongR. H. Du and Z. Zhu, Brushless dc motor self-adaptive fuzzy pid control and simulation, Journal of Changsha University of Science and Technology, 11 (2014), 60-66.   Google Scholar

[15]

G. YangX. Y. Fu and X. Fang, Design and simulation of ga-based pid controller, Journal of Huazhong University of Science and Technology: Natural Science, 40 (2012), 1-5.   Google Scholar

[16]

L. YangS. Shi and D. X. Zhang, Design and experiment of pneumatic maize precision seed-metering device with combined holes, Transactions of the Chinese Society of Agricultural Engineering, 30 (2014), 10-18.   Google Scholar

[17]

D. X. Zhang, H. Zhang and T. Cui, Mechanical-pneumatic combined corn precision seed-metering device, Transactions of the Chinese Society for Agricultural Machinery. Google Scholar

[18]

X. C. ZhangH. Zhang and S. J. Li, Development and performance of electro-hydraulic proportion control system of variable rate fertilizer, Transactions of the Chinese Society of Agricultural Engineering, 26 (2010), 218-222.   Google Scholar

Figure 1.  Root locus plot for the transfer function of stepping motor
Figure 2.  General simulation result of PID parameter
Figure 3.  Optimization of the cost function
Figure 4.  PID parameter simulation result adjusted with genetic algorithm
Figure 5.  Speed measurement of seeder shaft
Figure 6.  Speed curve in general PID control
Figure 7.  Relative error in general PID control
Figure 8.  Speed curve in genetic algorithm-based PID control
Figure 9.  Relative error in genetic algorithm-based PID control
Table 1.  List of hardware configurations of the control system
Device names Types
Stepping motor 57BYG101
Stepping motor driver Two-phased hybrid DM542
Touch display SK-070FE
PLC FGM-64M
Speed sensor JWK:LJ12A3-2-Z/BX
Coder ZNZS-6E1R-M485
Inverter MainlinK-1000W
Device names Types
Stepping motor 57BYG101
Stepping motor driver Two-phased hybrid DM542
Touch display SK-070FE
PLC FGM-64M
Speed sensor JWK:LJ12A3-2-Z/BX
Coder ZNZS-6E1R-M485
Inverter MainlinK-1000W
Table 2.  Performance indicators of traditional seeding systems at different operating speeds
Performance indicators Low-speed operations Medium-speed operations High-speed operations Average
Row-spacing variation coefficient/% 3.62 5.28 6.48 5.13
Miss-seeding index/% 2.06 3.19 4.25 3.17
Repeated-seeding index/% 2.02 2.28 3.36 2.55
Performance indicators Low-speed operations Medium-speed operations High-speed operations Average
Row-spacing variation coefficient/% 3.62 5.28 6.48 5.13
Miss-seeding index/% 2.06 3.19 4.25 3.17
Repeated-seeding index/% 2.02 2.28 3.36 2.55
Table 3.  Performance indicators of electrical-control seeding systems at different operating speeds
Performance indicators Low-speed operations Medium-speed operations High-speed operations Average
Row-spacing variation coefficient/% 2.15 2.52 3.69 2.79
Miss-seeding index/% 0.98 1.03 1.22 1.08
Repeated-seeding index/% 0.92 1.12 1.35 1.13
Performance indicators Low-speed operations Medium-speed operations High-speed operations Average
Row-spacing variation coefficient/% 2.15 2.52 3.69 2.79
Miss-seeding index/% 0.98 1.03 1.22 1.08
Repeated-seeding index/% 0.92 1.12 1.35 1.13
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