农业工程学报2017，Vol.33Issue(23)：48-55,8.DOI:10.11975/j.issn.1002-6819.2017.23.007
基于速度自适应的拖拉机自动导航控制方法
Method on automatic navigation control of tractor based on speed adaptation
摘要
Abstract
It has been widely accepted that the application of tractor automatic navigation technology plays a key role in promoting the development of precision agriculture. The following fact is the increase in tractor operation speed and improvement of working efficiency. Considering the diversity of terrain condition and environmental noises, however, the nonlinearity and instability of tractor automatic navigation control system might be magnified significantly along with higher tractor speeds. Conventional control methods have been found to be difficult to meet the requirements for tractor automatic tracking. To investigate the influence of tractor speed on system stability, the sliding mode method was proposed based on the integrated control algorithm of deviations of lateral position and course angle. Firstly, deviation model of tractor linear path tracking was established based on the two-wheel tractor dynamic model with the introduction of velocity. The motion of tractor was simplified as the description of motion in two-dimensional plane, and only 3 degrees of freedom, i.e. the longitudinal and transverse direction and yaw of the tractor, were considered and some simplifications were made in the process of modeling. The visibility distance was set along the tractor forwarding direction. According to the kinematic relationship, the deviations of tractor lateral position and course angle at the visibility distance were obtained. In the actual path-tracking control process, it was difficult to achieve the ideal control effect at the same time. Therefore, the mixing coefficient was introduced into the controller to adjust the mixing degree of the 2 control strategies. Adaptive control of different deviations and speeds could be achieved when the coefficient changed between 0 and 1. When it approached 0, the control system tended to complete lateral position control; while the strategy of course angle deviation control was preferred with its value approaching 1. The reliability of the proposed approach was verified by simulation using MATLAB/Simulink. The simulation results show that with the disturbance of tractor speed, rapid and stable tracking could be achieved for low speed conditions, too. Furthermore, an automatic navigation test platform was built on a Foton Lovol TG1254 tractor. For tractor operation conditions at constant and variable speeds, a series of field experiments were conducted for the straight-path tracking control. By analyzing the effect of dynamic tracking control at various speeds using the mean absolute deviation, the maximum deviation and the standard deviation, the performance of the proposed automatic navigation control system was verified. Experimental results showed that in the tractor path-tracking process, the maximum deviation of tractor lateral position was 10.60 cm; the mean absolute deviation of lateral position was below 3.50 cm; the maximum deviation of course angle was 3.87°; the mean absolute deviation of course angle was below 1.7°; the maximum swing angle of the front wheels was 3° in the steady state, and the corresponding standard deviation was 0.80°. Therefore, the speed-adaption-based tractor navigation system proposed in this study was experimentally verified and could achieve the basic speed-adaptive tracking control and further improve the precision of tractor navigation. Furthermore, it is possible to meet the agronomic requirements of actual operations in the field. The approach proposed in this study provides potential theoretical guidance and certain significance for practical tractor field operations.关键词
机械化/控制系统/导航/拖拉机自动导航/拖拉机-路径动力学模型/滑模控制/速度自适应/仿真分析Key words
mechanization/control systems/navigation/tractor auto-navigation/tractor-path dynamics model/sliding mode control/speed adaptation/simulation analysis分类
信息技术与安全科学引用本文复制引用
张硕,刘进一,杜岳峰,朱忠祥,毛恩荣,宋正河..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):48-55,8.基金项目
国家重点研发计划项目(2017YFD0700403) （2017YFD0700403）
国家高技术研究发展计划(863计划)项目(2013AA102307) （863计划）
