基于迭代学习的农业车辆路径跟踪控制

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.

Iterative Learning Control for Trajectory Tracking of Farm Vehicles

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.