Purpose

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

Design/methodology/approach

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

Findings

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

Originality/value

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

中文翻译：

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基于自适应神经网络的输入饱和自主联合收割机路径跟踪控制

目的

为了减少参数不确定性和输入饱和对自主联合收割机路径跟踪控制的影响，提出了一种路径跟踪控制器，它集成了自适应神经网络估计器和饱和辅助系统。

设计/方法/方法

首先，为了分析和补偿外部因素的影响，结合动力学模型和运动学模型建立车辆模型。同时，为了简化模型，采用综合误差，同时对航向误差和位置误差进行加权。其次，提出了一种自适应神经网络估计器来计算最终改进动态模型的不确定参数。然后，采用反步法设计了基于改进动力学模型的路径跟踪控制器，并通过李雅普诺夫定理证明了其稳定性。第三，为了减轻由于输入饱和引起的执行器的往返操作，在控制设计过程中提供了一个饱和辅助变量。

发现

为了验证所提出控制器的跟踪精度和环境适应性，在三种不同情况下进行了数值模拟，并在收获小麦和稻谷时进行了田间试验。实验结果表明，与传统控制器相比，所提出的控制器的跟踪误差减少了 28% 以上。

原创性/价值

提出了一种基于自适应神经网络的路径跟踪控制，它考虑了参数的不确定性和输入饱和度。据了解，这是第一次针对联合收割机专门设计路径跟踪控制器，充分考虑工作特性。