| 自动导航拖拉机的农机具作业精度研究 |
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| 引用本文： | 马志凯,种坤,赵晓顺,赵建国,赵树朋,于合龙.自动导航拖拉机的农机具作业精度研究[J].河北农业大学学报,2022,45(4):109-114. |
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| 作者姓名： | 马志凯 种坤 赵晓顺 赵建国 赵树朋 于合龙 |
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| 作者单位： | 1. 河北农业大学 机电工程学院，河北 保定 071001；
2. 吉林农业大学 智慧农业研究院 , 吉林 长春 130118 |
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| 基金项目： | 河北省重点研发计划项目(22327204D,201227168,19227208D) |
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| 摘 要： | 针对自动导航拖拉机带农机具作业时农机具跟踪误差会被放大的问题，提出了1种能够提高农机具作业精度的拖拉机导航算法。采用路径跟踪误差最小的优化控制方法，研究拖拉机自动导航作业时引起机具误差大的因素及改进方法，设计具有农机具误差项的拖拉机模型预测控制算法，对该算法进行可行性试验及对比试验。试验结果表明采用带机具误差项的算法使农机具横向偏差平均降低了42.36%，横向偏差标准差平均降低了40.91%，航向角最大偏差平均降低了6.11%,航向角偏差标准差平均降低了31.04%。结果表明增加农机具误差项的模型预测控制算法能有效提升农机具的作业精度。
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| 关 键 词： | 模型预测控制 辅助驾驶 路径跟踪 卫星导航 |
| 收稿时间： | 2021-12-16 |
Research on the work precision of farm machinery driven by automatic navigation tractors |
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| MA Zhikai,CHONG Kun,ZHAO Xiaoshun,ZHAO Jianguo,ZHAO Shupeng,YU Helong.Research on the work precision of farm machinery driven by automatic navigation tractors[J].Journal of Agricultural University of Hebei,2022,45(4):109-114. |
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| Authors: | MA Zhikai CHONG Kun ZHAO Xiaoshun ZHAO Jianguo ZHAO Shupeng YU Helong |
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| Institution: | 1.College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China;
2.College of Information and Technology, Jilin Agricultural University, Changchun 130118, China |
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| Abstract: | Aiming at the magnification of tracking errors of agricultural machinery when they are driven by automatic navigation tractors, a tractor navigation algorithm is proposed to improve the work accuracy of agricultural machinery.In this paper, the factors causing large agricultural machinery error during tractor automatic navigation operation were studied using the optimal control method with the minimum path tracking error to find methods for improvement.The agricultural machinery error term was added to a model predictive control algorithm of tractor, followed by the feasibility and comparison tests of the algorithm. The experimental results showed that the lateral deviation reduced by 42.36% when the agricultural machinery error term was included in the algorithm. The standard deviation of lateral deviation reduced by 40.91%. The maximum deviation of heading angle reduced by 6.11% and the standard deviation of heading angle reduced by 31.04%. The results displayed that the model predictive control algorithm with agricultural machinery error terms can effectively improve the work accuracy of agricultural machinery. |
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| Keywords: | model control auxiliary driving path tracking satellite navigation |
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