多农机重复路径协同作业下的导航路径迭代学习控制

在农作物从种到收多个作业环节中,多农机大都按同一参考路径(存在实时干扰)协同作业。针对"重复路径行驶"特点和借鉴"迭代学习"思想,可以逐渐提高协同作业时的农机导航路径跟踪精度,因此提出一种基于迭代学习控制的农机导航路径跟踪方法。首先,建立了离散的车辆运动学非线性模型;然后,设计了开闭环迭代学习控制律,并进行了收敛性分析;最后,进行了直线和圆形路径跟踪仿真实验。结果表明:迭代学习控制能够实现农机导航路径的完全跟踪;相同迭代次数下,与开环、闭环控制仿真结果比较,开闭环迭代学习控制收敛速度更快,路径跟踪误差逐渐趋近于0。

Navigation Control of Collaborative Operation in the Repeat Path for Multiple Agricultural Machinery Based on Iterative Learning

In the whole process of crops planting to harvest, multiple agricultural machineries operate in the repeat path. According to “repeat path” and iterative learning theory, navigation accuracy can be improved. So navigation control of collaborative operation in the repeat path for multiple agricultural machinery based on iterative learning is proposed. Firstly, discrete and nonlinear vehicle kinematic model is established.Secondly, open-loop iterative learning control is designed and astringent is analyzed. Finally, straight and circular path tracking experiments are implemented. Results shows that iterative learning control can realize path tracking with 0 errors, compared with open iterative and loop iterative, the speed of open-loop iterative is faster and the error is going to 0.