Study on Modeling Method of a Multi-Parameter Control System for Threshing and Cleaning Devices in the Grain Combine Harvester

Yang Li, Lizhang Xu (), Liya Lv, Yan Shi and Xun Yu
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Yang Li: Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China
Lizhang Xu: Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China
Liya Lv: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Yan Shi: Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China
Xun Yu: Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 9, 1-18

Abstract: In order to realize the demand for high-quality and high-efficiency harvest in modern agriculture, the grain combine harvesters must have the ability to intelligently adjust the operation parameters. The difficult problem is to establish the multi-parameter control system model for threshing and cleaning devices. The threshing and cleaning devices are located in the same rack space, and the interaction mechanism among agricultural material movement, mechanical structure, and airflow field is very complex. In view of the difficulties in the theoretical modeling of threshing and cleaning devices, a large number of operating parameters and performance indicators, strong coupling, and high requirements for real-time control, the system identification method was used to model the threshing and cleaning system in this paper. Firstly, the amplitude modulated PRBS input signals were designed as the input parameters of the system identification test, and the output signals acquisition test was carried out in the field. Then, the multi-input and multi-output signals of the system were used as training data, and the fusion method of the PSO (particle swarm optimization) algorithm and WNN (wavelet neural network) was proposed to identify it, and the optimal state-space model was obtained. Finally, the model identification and verification experiments were carried out on the threshing and cleaning system of various crops during the actual harvest. The VAF (variance-accounted-for) values of system identification model verification results were greater than or equal to 81.7%, and the RMSE (root mean square error) values were less than or equal to 0.602. The modeling method has high accuracy and adaptability, which laid a good foundation for realizing multi-parameter coordinated control of threshing and cleaning devices.

Keywords: combine harvester; threshing and cleaning devices; multi-parameter; system modeling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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