| 履带式花生联合收获机路径跟踪控制方法与试验 |
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| 引用本文： | 何杰,满忠贤,胡炼,罗锡文,汪沛,李明锦,李伟聪.履带式花生联合收获机路径跟踪控制方法与试验[J].农业工程学报,2023,39(1):9-17. |
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| 作者姓名： | 何杰 满忠贤 胡炼 罗锡文 汪沛 李明锦 李伟聪 |
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| 作者单位： | 1. 华南农业大学南方农业机械与装备关键技术教育部重点实验室，广州 510642; 2. 岭南现代农业科学与技术广东省实验室，广州 510642; 3. 广东省农业人工智能重点实验室，广州 510642; |
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| 基金项目： | 广东省人工智能实验室项目（2021B1212040009）；岭南现代农业科学与技术广东省实验室科研项目（NT2021009）；国家花生产业技术体系（CARS-13） |
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| 摘 要： | 为提高无人驾驶履带式花生收获机沙地作业路径跟踪精度，以4HBL-2型自走式花生联合收获机为研究对象，开展了履带式收获机无人驾驶路径跟踪控制研究。建立了履带式收获机运动学模型与虚拟转向角函数关系；以航向偏差值作为观测量、阿克曼模型推算角速度作为测量值，设计卡尔曼融合算法，获得基于阿克曼模型的虚拟转向角度；根据虚拟转向角度对PID路径跟踪算法进行改进，提出了基于预瞄跟踪的双PID路径跟踪控制方法；通过脉冲宽度控制器实现了履带式花生收获机路径跟踪精准控制。仿真试验结果表明：基于预瞄跟踪双PID的路径跟踪控制方法能够进行路径跟踪控制，具有控制平滑和稳态误差小等特点。田间试验表明：花生收获机在沙地以0.6m/s的速度作业时，系统直线跟踪平均绝对误差为2.23 cm，最大偏差为4.14 cm，相对于PD路径跟踪控制器分别提高了56.12%和66.07%。上线试验中，初始偏差分别是0.5、1.0和1.5 m时，上线时间分别为11.00、12.92和13.78 s，上线距离为6.60、7.75和8.26 m；最大超调量分别为5.68%、5.84%和8.06%，相较于轮式收获机，上线距离分别减小了1.9...
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| 关 键 词： | 农业机械 收获 路径规划 花生 预瞄控制 虚拟阿克曼 |
| 收稿时间： | 2022/8/8 0:00:00 |
| 修稿时间： | 2022/12/25 0:00:00 |
Path tracking control method and experiments for the crawler-mounted peanut combine harvester |
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| HE Jie,MAN Zhongxian,HU Lian,LUO Xiwen,WANG Pei,LI Mingjin,LI Weicong.Path tracking control method and experiments for the crawler-mounted peanut combine harvester[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(1):9-17. |
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| Authors: | HE Jie MAN Zhongxian HU Lian LUO Xiwen WANG Pei LI Mingjin LI Weicong |
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| Institution: | 1. Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University , Guangzhou 510642, China; 2. Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; 3. Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China |
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| Abstract: | Peanut harvesting has posed a great promise for the sustainable development in modern agriculture. This study aims to improve the path-tracking accuracy of an unmanned crawler-mounted peanut harvester on sandy land. 4HBL-2 self-propelled peanut combine harvester was taken as the research object. A systematic investigation was also carried out on the unmanned path-tracking control of the crawler-mounted harvester. The optimal relationship was established between the kinematic model of the crawler-mounted harvester and the virtual steering angle function. The course deviation was used as the observation value, whereas, the angular velocity calculated by the Ackerman model was used as the measurement value. Kalman Fusion Algorithm was also designed to obtain the virtual steering angle using the Ackerman model. The PID path tracking was improved significantly, according to the virtual steering angle. A double PID path tracking control was proposed using preview tracking. A pulse width controller was then selected to realize the accurate path-tracking control of the crawler-mounted peanut harvester. The simulation test results showed that the path tracking control method based on preview tracking double PID can perform path tracking control, and had the characteristics of smooth control and small steady-state error. There was no change in the signal period and waveform distortion. A series of field experiments show that the average absolute error and the maximum deviation of the linear tracking were 2.23, and 4.14 cm, respectively, when the peanut harvester was operated at a speed of 0.6 m/s in the sand. The performance of the improved system was enhanced by 56.12%, and 66.07%, respectively, compared with the PID path tracking controller. The path tracking experiments showed that the response duration values of the control system were 11.00, 12.92, and 13.78 s, respectively, while the corresponding distances were 6.60, 7.75, and 8.26 m, respectively, when the initial deviation was 0.5, 1.0, and 1.5 m, respectively. Specifically, the maximum overshoot was 5.68, 5.84, and 6.09 cm, respectively. The tracking path distance was reduced by 1.92%, 4.43%, and 8.71%, respectively, whereas, the overshoot was reduced by 8.45%, 17.56%, and 5.17%, respectively, compared with the wheeled harvesters. The maximum deviation and average absolute error of the peanut harvester were 5.87 and 2.72 cm, respectively. The ratios of deviation within ±5 cm and ±10 cm were 97.11%, and 100%, respectively. The general technical conditions were also compared for the post-installation of the satellite navigation automatic driving system of agricultural machinery in the industry standard. The connection performance of the crawler peanut harvester can be expected to meet the high requirements of the connection performance. The path tracking control accuracy of the crawler-mounted peanut harvester can also fully meet the harsh requirements of unmanned operation in sandy land. |
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| Keywords: | agricultural machinery harvest path planning peanut preview control virtual Ackerman |
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