A fuzzy decision-making algorithm-based header height measurement system for combine harvester

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2025-02-03 DOI:10.1016/j.measurement.2025.116918

Qian Wang , Jun-jie Zhao , Zhi-jun Meng , Wu-chang Qin , Feng Wang , Chun-jiang Zhao , Qing-zhen Zhu , Chang-kai Wen , Yan-xin Yin

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引用次数: 0

Abstract

Header height is a critical parameter during the operation of the harvester. Accurately and consistently measuring header height is challenging due to adverse factors such as uneven farmland topography, irregular ground surfaces, weeds, and crop stubble. This study proposes an intelligent calculation algorithm based on the fusion of the Kalman filter and fuzzy decision-making (KFF) to achieve accurate, stable, reliable and real-time header height measurement and a header height online measurement system (HMS) for combine harvester is developed. The algorithm first calculates the initial header height according to the trajectory of the header. The Kalman filter algorithm then predicts the header height for the subsequent state. Finally, the accurate header height is calculated after fuzzy decision-making. The algorithm effectively addresses the interference caused by complex working conditions on header height measurement. A band-pass filter is designed and a signal transmitter based on an STM32 embedded microcontroller is developed to counter the interference characteristics of header bumps and vibration on the height detection signal. Furthermore, the header height online measurement system (HMS) software for combine harvesters was developed. This system handles sensor data collection, conducts header height fusion calculations, and facilitates human–machine interaction. This study carries out field tests under varying combine harvester operating speeds and initial static header heights (ISHH). Results showed that the measurement error is mainly distributed in the range of − 0.5 to 0.5 cm. The mean relative error (MRE) of the absolute header height measurement error was 0.9 %, 1.0 %, 1.0 %, and 1.0 % at velocities of 2, 3, 4, and 5 km/h, respectively. The average MRE was 0.975 %. Furthermore, the MRE of the measurement results was 1.6 %, 1.0 %, 0.8 % and 0.6 % for different ISHH of 10, 15, 20 and 25 cm, respectively. The mean MRE was 1 %. ANOVA results showed that different speeds and ISHH do not significantly impact the measurement results of the HMS. The HMS developed in this study exhibited a high degree of precision and stability, providing critical support for the continuous regulation of header height.

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基于模糊决策算法的联合收割机机头高度测量系统

铲车高度是收割机运行过程中的一个关键参数。由于不平坦的农田地形、不规则的地面、杂草和作物残茬等不利因素，准确和一致地测量收穗高度具有挑战性。提出了一种基于卡尔曼滤波与模糊决策（KFF）融合的智能计算算法，实现了准确、稳定、可靠、实时的收割机收头高度测量，并开发了联合收割机收头高度在线测量系统（HMS）。该算法首先根据头的运动轨迹计算头的初始高度。然后，卡尔曼滤波算法预测后续状态的报头高度。最后，通过模糊决策计算出准确的头球高度。该算法有效地解决了复杂工况对封头高度测量的干扰。设计了带通滤波器，开发了基于STM32嵌入式单片机的信号发射机，以对抗头部碰撞和振动对高度检测信号的干扰特性。在此基础上，开发了联合收割机头高在线测量系统软件。该系统处理传感器数据采集，进行头球高度融合计算，便于人机交互。本研究在不同的联合收割机运行速度和初始静态头高（ISHH）下进行了现场试验。结果表明，测量误差主要分布在- 0.5 ~ 0.5 cm范围内。在速度为2、3、4和5 km/h时，绝对头高度测量误差的平均相对误差（MRE）分别为0.9%、1.0%、1.0%和1.0%。平均MRE为0.975%。在10、15、20、25 cm不同高度下，MRE分别为1.6%、1.0%、0.8%、0.6%。平均MRE为1%。方差分析结果显示，不同的速度和ISHH对HMS的测量结果没有显著影响。本研究开发的HMS具有高度的精度和稳定性，为连续调节掘进高度提供了关键支持。

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.