This paper addresses the tracking control of an autonomous tractor-trailer robot with a desired distance and orientation angle relative to a combine harvester in the presence of model uncertainties for the autonomous unloading of the harvested cereals in agriculture applications. A coordinate transformation and the prescribed performance technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive proportional-integral-derivative (PID) tracking controller is proposed to guarantee that the tracking errors exponentially converge to an arbitrary small ultimate bound with a pre-specified maximum overshoot and convergence rate. By an effective application of the prescribed performance technique, the controller non-singularity, the collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a multi-layer neural network and an adaptive robust control law. Lyapunov's direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a prescribed performance. Finally, the computer simulation results are presented to demonstrate the effectiveness of the proposed controller.

本文介绍了在模型不确定性存在的情况下，相对于联合收割机，具有所需距离和方向角的自主式拖拉机-拖车机器人的跟踪控制，该模型不确定性用于在农业应用中自动收获谷物。使用坐标变换和规定的性能技术来开发跟踪误差的二阶Euler-Lagrange公式。然后，提出了一种神经自适应比例积分微分（PID）跟踪控制器，以确保跟踪误差以预定的最大过冲和收敛速度指数收敛到任意小的最终边界。通过有效地运用规定的性能技术，控制器非奇异，两辆车之间的防撞性和连通性得以连续保留。模型不确定性包括多层神经网络和自适应鲁棒控制律的有效补偿，这些不确定性包括未知的车辆参数，可变的拖车质量和农作物收割期间的惯性矩，表面摩擦，气候和农作物状况以及外部干扰。利用李雅普诺夫的直接方法来证明跟踪误差最终在半全局均匀地有界，并且以规定的性能收敛到原点的邻域。最后，给出了计算机仿真结果，以证明所提出的控制器的有效性。多层神经网络和自适应鲁棒控制定律的有效结合，可以补偿农作物收割过程中拖车质量和惯性矩的变化，表面摩擦，气候和农作物状况以及外部干扰。利用李雅普诺夫的直接方法来证明跟踪误差最终在半全局均匀地有界，并且以规定的性能收敛到原点的邻域。最后，给出了计算机仿真结果，以证明所提出的控制器的有效性。多层神经网络和自适应鲁棒控制定律的有效结合，可以补偿农作物收割过程中拖车质量和惯性矩的变化，表面摩擦，气候和农作物状况以及外部干扰。利用李雅普诺夫的直接方法来证明跟踪误差最终在半全局均匀地有界，并且以规定的性能收敛到原点的邻域。最后，给出了计算机仿真结果，以证明所提出的控制器的有效性。直接法被用来证明跟踪误差是半全局一致地最终定界的，并且以规定的性能收敛到原点的邻域。最后，给出了计算机仿真结果，以证明所提出的控制器的有效性。直接法被用来证明跟踪误差是半全局一致地最终定界的，并且以规定的性能收敛到原点的邻域。最后，给出了计算机仿真结果，以证明所提出的控制器的有效性。