丘陵山地姿态调整轮式拖拉机运动控制研究

Motion Control of Attitude Adjustment Wheeled Tractor in Hilly and Mountain Areas

摘要:

以丘陵山地姿态调整轮式拖拉机为研究对象，提出了一种基于改进遗传算法的运动控制方法，可根据地形条件实现对拖拉机的实时调平控制，提高其车身稳定性。首先，根据拖拉机机构间的运动关系，建立表征其轮心位置与车身姿态参数关系的运动学模型，并进行算例求解，验证了运动学模型的正确与准确性。然后，以提高拖拉机车身稳定性为控制目标，在运动学建模的基础上设计了一种基于改进遗传算法的运动控制方法。最后，对算法进行仿真验证，结果表明，使用算法进行运动控制可有效降低其车身姿态角，横向坡地最大侧倾角降低13.3°，纵向坡地最大俯仰角降低4.3°；在两种坡度兼有的路面上进行综合调整，其最大侧倾角和最大俯仰角分别降低13.8°和4°，极大提高了车身稳定性。同时将改进遗传算法与传统遗传算法进行对比，结果表明，改进遗传算法在响应时间和控制精度方面均优于传统遗传算法，其算法响应时间较传统遗传算法缩短63.93%，大幅提高了算法效率。

Abstract:

In order to improve the stability of the tractors in hilly and mountainous areas, a motion control method was designed based on improved genetic algorithm, which can achieve real-time leveling control according to terrain conditions. Firstly, according to the kinematic relationship of the tractor attitude adjustment mechanism, a kinematic model representing the relationship between the wheel center position and the body attitude parameters was established, and a numerical example was given to solve the problem to verify the correctness and accuracy of the kinematic model. Then, with the aim of improving the stability of tractor body, a tractor motion control method based on improved genetic algorithm was designed while considering the kinematics model. Finally, the simulation results showed that the motion control algorithm can effectively reduce the body attitude angle, the maximum lateral slope angle was reduced by 13.3°, and the maximum pitch angle of the longitudinal slope was reduced by 4.3°. The comprehensive adjustment on the road with both slopes can reduce the maximum side angle and the maximum pitch angle by 13.8° and 4°, respectively, which greatly improved the stability of the body. At the same time, the improved genetic algorithm was compared with the traditional genetic algorithm. The results showed that the improved genetic algorithm was superior to the traditional genetic algorithm both in response time and control accuracy. The response time of the improved genetic algorithm was reduced by 63.93% compared with that of the traditional genetic algorithm, and the efficiency of the algorithm was greatly improved.