智能收获机器人侧滑及滞后性控制策略研究

Research on the control strategy of sideslip and hysteresis of intelligent harvesting robot

针对智能重载收获机器人在田间自动作业时容易发生侧滑及控制系统滞后性特点,提出一种能够补偿侧滑和校正滞后性的机器人路径追踪控制策略。建立收获机器人的运动学模型,根据上一时刻和当前时刻的位姿信息估算等效侧滑角,补偿期望转向角;根据当前时刻的速度和位姿信息预测未来时刻的位姿信息,校正当前时刻的横向偏差和航向偏差,优化由路径追踪算法计算的期望转向角,以克服田间作业侧滑问题,校正控制系统的滞后性、非线性对路径追踪精度的影响。通过试验验证了在0.6m/s和1.0m/s的行驶速度下该算法的有效性,收获机最大横向误差分别为8.3cm和13.9cm,标准偏差分别为3.01cm和4.09cm。结果表明,所提出的补偿侧滑和校正滞后性的纯追踪策略能有效降低横向误差,实现准确的路径追踪控制。

Aiming at the characteristic that the heavy-load harvesters would easily slip and sideslip and its nonlinear large-lag control system,a method of compensating the sideslip and relieving the hysteresis in path tracking for heavy load harvesters was proposed in this paper.After establishing a kinematic model,the equivalent sideslipping angle was estimated according to the previous and the present state of the harvester,to compensate for the expected wheel steering angle.The featured state of the harvester can be predicted according to the present state and speed of the harvester to adjust the present lateral error and heading angle error to optimize the expected wheel steering angle calculated by the path tracking algorithm,which can improve the accuracy and stability when the heavy load harvester is working.The validity of the proposed algorithm was verified by experiments at the speeds of 0.6 m/s and 1.0 m/s,respectively.The maximum lateral errors of the harvester were 8.3 cm and 13.9 cm,respectively,and the standard deviations were 3.01 cm and 4.09 cm respectively.The results show that the strategy proposed in this paper can efctively reduce the lateral error and achieve accurate path tracking control..