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Article(id=1324333592930824385, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2026.01.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760457600000, receivedDateStr=2025-10-15, revisedDate=1766160000000, revisedDateStr=2025-12-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1793675333482, onlineDateStr=2026-11-03, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793675333482, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793675333482, creator=13701087609, updateTime=1793675333482, updator=13701087609, issue=Issue{id=1324333573712511176, tenantId=1146029695717560320, journalId=1319690209941618849, year='2026', volume='7', issue='1', pageStart='1', pageEnd='196', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793675328901, creator='13701087609', updateTime=1793872389599, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325160106287039215, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325160106287039216, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=86, endPage=95, ext={EN=ArticleExt(id=1324333593102790850, articleId=1324333592930824385, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on path tracking controller of crawler-type cabbage harvester based on IPSO-FUZZY-PP, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of low tracking accuracy and poor scene adaptability of the existing differential tracked chassis path tracking controller, this study uses a small light and simple tracked cabbage harvester as a test platform to innovatively design an improved particle swarm optimization (IPSO) with fore-look distance adaptive fuzzy pure tracking controller (IPSO-FUZZY-PP). First, the study analyzes the operating characteristics of the cabbage harvester's pulling roller, and the cabbage's demand for navigation accuracy is determined. Then, the differential kinematics model of the crawler harvester is constructed to clarify the relationship between the track speed on both sides and the driving and steering states. The lateral deviation and heading deviation are used as the input of the fuzzy controller, and the PWM duty cycle difference of the two-sided motor is used as the output. The IPSO algorithm is used to dynamically optimize the fore-look distance. The simulation results show that the convergence speed of the controller is 60% higher than that of the traditional particle swarm optimization algorithm, which can effectively avoid the local optimal solution. In the cement pavement test (driving speed of 0.5 m/s), the maximum tracking deviation of the controller is 0.035 m, and the average absolute deviation is 0.017 m. Compared with the traditional pure tracking controller, the accuracy is improved by 34.6%, the rise time is shortened from 1.71 s to 0.76 s, and the response speed is improved by 55.6%. In the field test (driving speed 0.3, 0.5, 0.8 m/s), the maximum tracking deviation is not more than 0.031 m, 0.037 m, 0.041 m, respectively, and the average absolute deviation is controlled within 0.010 m, 0.015 m, 0.018 m, respectively. Compared with the traditional pure tracking controller, the accuracy is improved. The controller proposed in this study can dynamically adapt to the narrow row spacing and multi-speed conditions of cabbage harvest, meet the accuracy requirements of cabbage harvest navigation, and provide technical support for the precise alignment of unmanned cabbage harvest.

, authors=Xu GAO1, Jianfei ZHANG1, Run ZHAO1, Xuhui YANG1, 2, Jianbo LIU1, 2, authorsList=Xu GAO, Jianfei ZHANG, Run ZHAO, Xuhui YANG, Jianbo LIU, authorCompany=null, correspAuthors=Jianfei ZHANG, authorNote=

GAO Xu, E-mail:

, correspAuthorsNote=
* ZHANG Jianfei, E-mail:
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针对现有差速履带底盘路径跟踪控制器跟踪精度低、场景适配性差问题，本研究以小型轻简型履带式甘蓝收获机为试验平台，创新设计一种改进粒子群优化（IPSO）前视距离的自适应模糊纯跟踪控制器（IPSO-FUZZY-PP）。研究首先通过分析甘蓝收获机拔取辊作业特性，确定甘蓝对行导航精度需求随后构建履带式收获机差速运动学模型，明确两侧履带速度与行驶、转向状态的关联；以横向偏差、航向偏差为模糊控制器输入，双侧电机PWM占空比差为输出，结合IPSO算法动态优化前视距离。仿真结果显示，该控制器收敛速度较传统粒子群优化算法提升60%，可有效避免局部最优解；水泥路面试验（行驶速度0.5 m/s）中，该控制器最大跟踪偏差为0.035 m，平均绝对偏差为0.017 m，较传统纯跟踪控制器精度提升34.6%，上升时间从1.71 s缩短至0.76 s，响应速度提升55.6%；田间试验（行驶速度0.3 m/s、0.5 m/s、0.8 m/s）中，其最大跟踪偏差分别不超过0.031 m、0.037 m、0.041 m，平均绝对偏差分别控制在0.010 m、0.015 m、0.018 m以内，精度较传统纯跟踪控制器有所提升。本研究提出的控制器，可动态适配甘蓝收获的窄行距、多速度工况，满足甘蓝采收导航精度需求，为甘蓝无人化收获的精准对行提供技术支撑。

, authors=高旭1, 张健飞1, *, *, 赵闰1, 杨旭辉1, 2, 刘建博1, 2, authorsList=高旭, 张健飞, 赵闰, 杨旭辉, 刘建博, authorCompany=null, correspAuthors=张健飞, authorNote=

高旭，男，1999年生，山东诸城人，硕士研究生；研究方向为农业机械无人驾驶技术及装备。E-mail：

, correspAuthorsNote=
* 张健飞，男，1989年生，辽宁建平人，博士，副研究员；研究方向为结球类蔬菜收获、复杂地形无人驾驶技术及装备。E-mail：
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GAO Xu, E-mail:

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GAO Xu, E-mail:

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高旭，男，1999年生，山东诸城人，硕士研究生；研究方向为农业机械无人驾驶技术及装备。E-mail：

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高旭，男，1999年生，山东诸城人，硕士研究生；研究方向为农业机械无人驾驶技术及装备。E-mail：

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基于IPSO-FUZZY-PP的履带式甘蓝收获机路径跟踪控制器的研究
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高旭 1 , 张健飞 1, *, * , 赵闰 1 , 杨旭辉 1, 2 , 刘建博 1, 2
智能化农业装备学报（中英文） | 2026,7(1): 86-95
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智能化农业装备学报（中英文） | 2026 , 7 (1) : 86 -95
基于IPSO-FUZZY-PP的履带式甘蓝收获机路径跟踪控制器的研究
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高旭1 , 张健飞1, *, * , 赵闰1, 杨旭辉1, 2, 刘建博1, 2
作者信息
  • 1.农业农村部南京农业机械化研究所，江苏 南京，210014
  • 2.沈阳农业大学工程学院，辽宁 沈阳，110866
通讯作者:
* 张健飞，男，1989年生，辽宁建平人，博士，副研究员；研究方向为结球类蔬菜收获、复杂地形无人驾驶技术及装备。E-mail：
作者简介:

高旭，男，1999年生，山东诸城人，硕士研究生；研究方向为农业机械无人驾驶技术及装备。E-mail：

Research on path tracking controller of crawler-type cabbage harvester based on IPSO-FUZZY-PP
Xu GAO1 , Jianfei ZHANG1 , Run ZHAO1, Xuhui YANG1, 2, Jianbo LIU1, 2
Affiliations
  • 1.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
  • 2.College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
    • Corresponding Author:
    * ZHANG Jianfei, E-mail:

    GAO Xu, E-mail:

出版时间: 2026-02-15 doi: 10.12398/j.issn.2096-7217.2026.01.009
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针对现有差速履带底盘路径跟踪控制器跟踪精度低、场景适配性差问题，本研究以小型轻简型履带式甘蓝收获机为试验平台，创新设计一种改进粒子群优化（IPSO）前视距离的自适应模糊纯跟踪控制器（IPSO-FUZZY-PP）。研究首先通过分析甘蓝收获机拔取辊作业特性，确定甘蓝对行导航精度需求随后构建履带式收获机差速运动学模型，明确两侧履带速度与行驶、转向状态的关联；以横向偏差、航向偏差为模糊控制器输入，双侧电机PWM占空比差为输出，结合IPSO算法动态优化前视距离。仿真结果显示，该控制器收敛速度较传统粒子群优化算法提升60%，可有效避免局部最优解；水泥路面试验（行驶速度0.5 m/s）中，该控制器最大跟踪偏差为0.035 m，平均绝对偏差为0.017 m，较传统纯跟踪控制器精度提升34.6%，上升时间从1.71 s缩短至0.76 s，响应速度提升55.6%；田间试验（行驶速度0.3 m/s、0.5 m/s、0.8 m/s）中，其最大跟踪偏差分别不超过0.031 m、0.037 m、0.041 m，平均绝对偏差分别控制在0.010 m、0.015 m、0.018 m以内，精度较传统纯跟踪控制器有所提升。本研究提出的控制器，可动态适配甘蓝收获的窄行距、多速度工况，满足甘蓝采收导航精度需求，为甘蓝无人化收获的精准对行提供技术支撑。

甘蓝  /  履带式收获机  /  纯跟踪  /  粒子群算法  /  精准作业  /  模糊控制

Aiming at the problems of low tracking accuracy and poor scene adaptability of the existing differential tracked chassis path tracking controller, this study uses a small light and simple tracked cabbage harvester as a test platform to innovatively design an improved particle swarm optimization (IPSO) with fore-look distance adaptive fuzzy pure tracking controller (IPSO-FUZZY-PP). First, the study analyzes the operating characteristics of the cabbage harvester's pulling roller, and the cabbage's demand for navigation accuracy is determined. Then, the differential kinematics model of the crawler harvester is constructed to clarify the relationship between the track speed on both sides and the driving and steering states. The lateral deviation and heading deviation are used as the input of the fuzzy controller, and the PWM duty cycle difference of the two-sided motor is used as the output. The IPSO algorithm is used to dynamically optimize the fore-look distance. The simulation results show that the convergence speed of the controller is 60% higher than that of the traditional particle swarm optimization algorithm, which can effectively avoid the local optimal solution. In the cement pavement test (driving speed of 0.5 m/s), the maximum tracking deviation of the controller is 0.035 m, and the average absolute deviation is 0.017 m. Compared with the traditional pure tracking controller, the accuracy is improved by 34.6%, the rise time is shortened from 1.71 s to 0.76 s, and the response speed is improved by 55.6%. In the field test (driving speed 0.3, 0.5, 0.8 m/s), the maximum tracking deviation is not more than 0.031 m, 0.037 m, 0.041 m, respectively, and the average absolute deviation is controlled within 0.010 m, 0.015 m, 0.018 m, respectively. Compared with the traditional pure tracking controller, the accuracy is improved. The controller proposed in this study can dynamically adapt to the narrow row spacing and multi-speed conditions of cabbage harvest, meet the accuracy requirements of cabbage harvest navigation, and provide technical support for the precise alignment of unmanned cabbage harvest.

cabbage  /  crawler harvester  /  pure tracking  /  particle swarm optimization algorithm  /  precise operation  /  fuzzy control
高旭, 张健飞, 赵闰, 杨旭辉, 刘建博. 基于IPSO-FUZZY-PP的履带式甘蓝收获机路径跟踪控制器的研究. 智能化农业装备学报（中英文）, 2026 , 7 (1) : 86 -95 . DOI: 10.12398/j.issn.2096-7217.2026.01.009
Xu GAO, Jianfei ZHANG, Run ZHAO, Xuhui YANG, Jianbo LIU. Research on path tracking controller of crawler-type cabbage harvester based on IPSO-FUZZY-PP[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (1) : 86 -95 . DOI: 10.12398/j.issn.2096-7217.2026.01.009
基金
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  • 国家重点研发计划项目(2023YFD2001203)
  • 江苏省农机研发制造推广应用一体化试点专项项目(JSYTH10)
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2026年第7卷第1期
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doi: 10.12398/j.issn.2096-7217.2026.01.009
  • 接收时间：2025-10-15
  • 首发时间：2026-11-03
  • 出版时间：2026-02-15
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  • 收稿日期：2025-10-15
  • 修回日期：2025-12-20
基金
National Key Research and Development Program of China(2023YFD2001203)
国家重点研发计划项目(2023YFD2001203)
Jiangsu Agricultural Machinery Research and Development Manufacturing Application Integration Pilot Special Project(JSYTH10)
江苏省农机研发制造推广应用一体化试点专项项目(JSYTH10)
作者信息
    1.农业农村部南京农业机械化研究所，江苏 南京，210014
    2.沈阳农业大学工程学院，辽宁 沈阳，110866

通讯作者:

* 张健飞，男，1989年生，辽宁建平人，博士，副研究员；研究方向为结球类蔬菜收获、复杂地形无人驾驶技术及装备。E-mail：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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