赵润茂,胡炼,罗锡文,周浩,袁琦堡,张盟.基于ARMA的插秧机田间行驶姿态预测[J].农业机械学报,2016,47(s1):8-12.
Zhao Runmao,Hu Lian,Luo Xiwen,Zhou Hao,Yuan Qibao,Zhang Meng.Prediction of Transplanter Attitude in Field Based on ARMA[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(s1):8-12.
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基于ARMA的插秧机田间行驶姿态预测   [下载全文]
Prediction of Transplanter Attitude in Field Based on ARMA   [Download Pdf][in English]
投稿时间:2016-07-20  
DOI:10.6041/j.issn.1000-1298.2016.S0.002
中文关键词:  插秧机  姿态  预测  侧倾角  ARMA
基金项目:公益性行业(农业)科研专项(201203059)、国家自然科学基金项目(31601225)、广东省科技计划项目(2015B020206002)、“十二五”国家科技支撑计划项目(2014BAD07)和广东省自然科学基金项目(2015A030310292)
作者单位
赵润茂 华南农业大学 
胡炼 华南农业大学南方粮油作物协同创新中心 
罗锡文 华南农业大学南方粮油作物协同创新中心 
周浩 华南农业大学 
袁琦堡 华南农业大学 
张盟 华南农业大学 
中文摘要:为减小水田不平度对农业机械精准作业品质的影响,进一步提高农机装备工作效率,从系统控制角度出发,提出将预测算法应用于农机具的姿态补偿控制。在分析自回归滑动平均模型(Auto regressive and moving average,ARMA)建模原理和适用性的基础上,对车载GPS/INS组合导航系统采集的插秧机田间行走侧倾角序列进行ARMA建模及提前预测,单次预测时长为1s和2s,预测总时长为30s;对GPS/INS组合导航系统所采集的原始值以2Hz、5Hz和10Hz 3种频率输出,以比较不同频率的数据建模对姿态预测精度的影响。结果表明:ARMA模型可有效预测插秧机未来1~2s内的姿态变化趋势;对于同一频率的样本,提前2s预测精度均低于提前1s预测精度,但差异不明显;以3种频率数据分别作样本时5Hz样本预测效果最好,其提前1s预测均方根误差和误差标准差分别为0.6567°、0.6565°,提前2s预测均方根误差和误差标准差分别为0.6712°、0.6769°。
Zhao Runmao  Hu Lian  Luo Xiwen  Zhou Hao  Yuan Qibao  Zhang Meng
South China Agricultural University,South China Agricultural University;Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China,South China Agricultural University;Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China,South China Agricultural University,South China Agricultural University and South China Agricultural University
Key Words:transplanter  attitude  prediction  roll angle  ARMA
Abstract:Aiming at reducing the influence caused by uneven fields to the working quality of precision agricultural equipment, lots of agricultural machinery were designed with special mechanisms to profile the farm surface, while the working efficiency is still limited to the response rate of servo and control system. For above reasons, a kind of ARMA (Auto regressive and moving average) algorithm was put forward and then be applied to predict vehicle attitudes so as to realize compensation control for tractor implement. The theory and applicability of ARMA were analyzed in detail before modelling using roll angle data collected from a working rice transplanter by the Inertia and GNSS measurement system. Finally, a 90s length data set was acquired of which the first 60s data was used to model, while the last 30s data was designed to make comparison with predicted values. In order to reduce the amount of calculation, the 100Hz raw data at frequency of 2Hz, 5Hz and 10Hz was output and then predicted the future roll angle of the three sets of data in 1~2 s respectively. First of all, the different method was deployed to make angle series stationary; secondly, a combining method of box order search solution and the MLE (Maximum likelihood estimation) was adopted to build models; at last, with the aid of AIC (Akaike information criterion), the final best model was identified after confirming the AIC. For better following of the dynamic trend of rice transplanter attitude and eliminating the negative influence brought by the increasing amount of sample data, the on line modelling method that older sample data was substituted constantly by new observational values in the modeling process was needed. By using three groups of sample data, prediction of rice transplanter attitude in future 1~2s was conducted utilizing Matlab, meanwhile, the whole predicting time was 30s. The results showed that: the validity of ARMA model in predicting attitude trend of farm vehicles was proven; for all the three sets of angle samples, 2s predicting error shows larger than 1s predicting error; the 5Hz roll angle series showed the best predicting effect, with 1s RMSE (root mean square error) and std (Standard deviation) of 0.6567°and 0.6565°, and 2s RMSE and std of 0.6712° and 0.6769° respectively.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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