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Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor

机译:迈向农业机器人:自动拖拉机的偏航动力学和轨迹跟踪的识别

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摘要

More efficient agricultural machinery is needed as agricultural areas become more limited and energy and labor costs increase. To increase their efficiency, trajectory tracking problem of an autonomous tractor, as an agricultural production machine, has been investigated in this study. As a widely used model-based approach, model predictive control is preferred in this paper to control the yaw dynamics of the tractor which can deal with the constraints on the states and the actuators in a system. The yaw dynamics is identified by using nonlinear least squares frequency domain system identification. The speed is controlled by a proportional integral derivative controller and a kinematic trajectory controller is used to calculate the desired speed and the desired yaw rate signals for the subsystems in order to minimize the tracking errors in both the longitudinal and transversal directions. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the euclidean error is below 40 cm for time-based straight line trajectories and 60 cm for time-based curved line trajectories, respectively. (C) 2015 Elsevier B.V. All rights reserved.
机译:随着农业地区越来越有限,能源和劳动力成本增加,需要更高效的农业机械。为了提高其效率,在本研究中研究了作为农业生产机器的自动拖拉机的轨迹跟踪问题。作为一种广泛使用的基于模型的方法,本文优选模型预测控制来控制拖拉机的偏航动力学,该偏航动力学可以处理系统中状态和执行器的约束。偏航动力学通过使用非线性最小二乘频域系统识别来识别。速度由比例积分微分控制器控制,运动轨迹控制器用于计算子系统的所需速度和所需偏航率信号,以最大程度地减小纵向和横向方向的跟踪误差。实验结果表明了所提出的控制方案的精度和效率,其中基于时间的直线轨迹的欧几里德误差分别小于40 cm,基于时间的曲线轨迹的欧几里德误差分别小于60 cm。 (C)2015 Elsevier B.V.保留所有权利。

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