Path Tracking Control Algorithm of Tractor-Implement
Received date: 2023-08-10
Online published: 2023-12-20
Supported by
National Key Research and Development Program of China(2022YFD200150302)
National Major Agricultural Science and Technology Project(NK202216010303)
Beijing postdoctoral work funding project(2023-ZZ-112)
Copyright
[Objective] The usual agricultural machinery navigation focuses on the tracking accuracy of the tractor, while the tracking effect of the trailed implement in the trailed agricultural vehicle is the core of the work quality. The connection mode of the tractor and the implement is non-rigid, and the implement can rotate around the hinge joint. In path tracking, this non-rigid structure, leads to the phenomenon of non-overlapping trajectories of the tractor and the implement, reduce the path tracking accuracy. In addition, problems such as large hysteresis and poor anti-interference ability are also very obvious. In order to solve the above problems, a tractor-implement path tracking control method based on variable structure sliding mode control was proposed, taking the tractor front wheel angle as the control variable and the trailed implement as the control target. [Methods] Firstly, the linear deviation model was established. Based on the structural relationship between the tractor and the trailed agricultural implements, the overall kinematics model of the vehicle was established by considering the four degrees of freedom of the vehicle: transverse, longitudinal, heading and articulation angle, ignoring the lateral force of the vehicle and the slip in the forward process. The geometric relationship between the vehicle and the reference path was integrated to establish the linear deviation model of vehicle-road based on the vehicle kinematic model and an approximate linearization method. Then, the control algorithm was designed. The switching function was designed considering three evaluation indexes: lateral deviation, course deviation and hinged angle deviation. The exponential reaching law was used as the reaching mode, the saturation function was used instead of the sign function to reduce the control variable jitter, and the convergence of the control law was verified by combining the Lyapunov function. The system was three-dimensional, in order to improve the dynamic response and steady-state characteristics of the system, the two conjugate dominant poles of the system were assigned within the required range, and the third point was kept away from the two dominant poles to reduce the interference on the system performance. The coefficient matrix of the switching function was solved based on the Ackermann formula, then the calculation formula of the tractor front wheel angle was obtained, and the whole control algorithm was designed. Finally, the path tracking control simulation experiment was carried out. The sliding mode controller was built in the MATLAB/Simulink environment, the controller was composed of the deviation calculation module and the control output calculation module. The tractor-implement model in Carsim software was selected with the front car as a tractor and the rear car as the single-axle implement, and tracking control simulation tests of different reference paths were conducted in the MATLAB/Carsim co-simulation environment. [Results and Discussions] Based on the co-simulation environment, the tracking simulation experiments of three reference paths were carried out. When tracking the double lane change path, the lateral deviation and heading deviation of the agricultural implement converged to 0 m and 0° after 8 s. When the reference heading changed, the lateral deviation and heading deviation were less than 0.1 m and less than 7°. When tracking the circular reference path, the lateral deviation of agricultural machinery tended to be stable after 7 s and was always less than 0.03 m, and the heading deviation of agricultural machinery tended to be stable after 7 s and remained at 0°. The simulation results of the double lane change path and the circular path showed that the controller could maintain good performance when tracking the constant curvature reference path. When tracking the reference path of the S-shaped curve, the tracking performance of the agricultural machinery on the section with constant curvature was the same as the previous two road conditions, and the maximum lateral deviation of the agricultural machinery at the curvature change was less than 0.05 m, the controller still maintained good tracking performance when tracking the variable curvature path. [Conclusions] The sliding mode variable structure controller designed in this study can effectively track the linear and circular reference paths, and still maintain a good tracking effect when tracking the variable curvature paths. Agricultural machinery can be on-line in a short time, which meets the requirements of speediness. In the tracking simulation test, the angle of the tractor front wheel and the articulated angle between the tractor and agricultural implement are kept in a small range, which meets the needs of actual production and reduces the possibility of safety accidents. In summary, the agricultural implement can effectively track the reference path and meet the requirements of precision, rapidity and safety. The model and method proposed in this study provide a reference for the automatic navigation of tractive agricultural implement. In future research, special attention will be paid to the tracking control effect of the control algorithm in the actual field operation and under the condition of large speed changes.
LIU Zhiyong , WEN Changkai , XIAO Yuejin , FU Weiqiang , WANG Hao , MENG Zhijun . Path Tracking Control Algorithm of Tractor-Implement[J]. Smart Agriculture, 2023 , 5(4) : 58 -67 . DOI: 10.12133/j.smartag.SA202308012
本研究不存在研究者以及与公开研究成果有关的利益冲突。
1 |
郝思佳, 李丽霞, 付卫强, 等. 牵引式农机-机具二维三自由度运动学模型研究[J]. 中国农机化学报, 2020, 41(10): 111-117.
|
2 |
孟庆宽. 基于机器视觉的农业车辆-农具组合导航系统路径识别及控制方法研究[D]. 北京: 中国农业大学, 2014.
|
3 |
|
4 |
|
5 |
|
6 |
冯雷. 基于GPS和传感技术的农用车辆自动导航系统的研究[D]. 杭州: 浙江大学, 2004.
|
7 |
|
8 |
|
9 |
|
10 |
林洪振, 李彦明, 袁正华, 等. 水田植保机自主作业滑模抗干扰路径跟踪方法[J]. 农业机械学报, 2021, 52(9): 383-388.
|
11 |
赵翾, 杨珏, 张文明, 等. 农用轮式铰接车辆滑模轨迹跟踪控制算法[J]. 农业工程学报, 2015, 31(10): 198-203.
|
12 |
张培培, 杨自栋, 赵相君. 拖拉机—牵引式农机具的运动建模与跟踪控制[J]. 中国农机化学报, 2021, 42(4): 128-133.
|
13 |
|
14 |
张硕, 刘进一, 杜岳峰, 等. 基于速度自适应的拖拉机自动导航控制方法[J]. 农业工程学报, 2017, 33(23): 48-55.
|
15 |
|
16 |
宋本嘉. 拖挂式移动机器人运动规划方法研究[D]. 济南: 济南大学, 2011.
|
17 |
白国星, 刘立, 孟宇, 等. 基于非线性模型预测控制的农用拖挂车避障控制器研究[J]. 农业机械学报, 2019, 50(4): 356-362.
|
18 |
赵熙俊, 刘海鸥, 熊光明, 等. 自动转向滑模变结构控制参数选取方法[J]. 北京理工大学学报, 2011, 31(10): 1174-1178.
|
19 |
刘国鹏. 挂载式无人农机跟踪控制与仿真[D]. 武汉: 华中科技大学, 2019.
|
20 |
丁晨, 魏新华, 梅珂琪. 农用拖拉机的自适应二阶滑模路径跟踪控制[J]. 控制理论与应用, 2023, 40(7): 1287-1295.
|
21 |
|
22 |
|
23 |
冀杰, 贺庆, 赵立军, 等. 除草机器人自适应快速积分终端滑模跟踪控制技术[J]. 农业机械学报, 2023, 54(6): 55-64.
|
24 |
张培培, 杨自栋, 赵相君. 基于滑膜控制的半挂汽车自动倒车路径跟踪[J]. 汽车实用技术, 2021, 46(2): 31-34.
|
25 |
|
/
〈 | 〉 |