基于迭代学习的农业车辆路径跟踪控制

卜旭辉; 侯忠生; 余发山; 付子义

doi:10.3724/SP.J.1004.2014.00368

基于迭代学习的农业车辆路径跟踪控制

doi: 10.3724/SP.J.1004.2014.00368

1.
河南理工大学电气工程与自动化学院焦作 454003;
2.
北京交通大学电子信息工程学院北京 100044

基金项目:

国家自然科学基金（61203065，61120106009）;河南省高等学校控制工程重点学科开放实验室项目（KG2011-10）;河南理工大学青年骨干教师计划项目资助

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出版历程
- 收稿日期: 2012-10-08
- 修回日期: 2013-06-17
- 刊出日期: 2014-02-20

Iterative Learning Control for Trajectory Tracking of Farm Vehicles

1.
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003;
2.
School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044

Funds:

Supported by National Natural Science Foundation of China (61203065, 61120106009), the Program of Open Laboratory Foundation of Control Engineering Key Discipline of Henan Provincial High Education (KG 2011-10), and the Program of Key Young Teacher of Henan Polytechnic University

摘要

摘要: 由于农作物的播种、收获、除草和农药化肥喷洒具有周期性的特点，农业车辆在执行农田作业时具有较强的重复性. 基于迭代学习控制（Iterative learning control，ILC）方法研究农业车辆的路径跟踪问题，建立了农业车辆的两轮移动机器人运动学模型，设计了车辆路径跟踪的迭代学习控制算法，并基于压缩映射方法理论上证明了算法的收敛性. 研究表明，迭代学习控制可有效利用农业车辆运行的重复信息，实现车辆期望路径有限区间内的高精度完全跟踪控制. 仿真示例验证了本文方法的有效性.
- 迭代学习控制 /
- 农业车辆 /
- 移动机器人 /
- 路径跟踪
Abstract: Duo to the periodicity of farm harvesting, seeding, cropping and spraying, the farm vehicle often does repetitive tasks. The problem of iterative learning control for farm vehicle trajectory tracking is considered. A two-wheel mobile robot kinematic model of farm vehicle is first established, then the D-type iterative learning control law is given. The convergence of the proposed iterative learning control (ILC) law is analyzed based on the contraction mapping approach. It is shown that the iterative learning control law can obtain perfect tracking performance after some iterations. A simulation example is also given to illustrate the effectiveness of the proposed approach.
- Iterative learning control (ILC) /
- farm vehicles /
- mobile robot /
- trajectory tracking

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参考文献(21)

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