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首页> 外文期刊>International Journal of Mechatronics and Automation >Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles
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Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

机译：Levenberg-Marquardt优化的神经网络，用于自动地面车辆的轨迹跟踪

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摘要

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks, Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error over the FOPID controller.
机译：轨迹跟踪是工业自动化中机器人操作的一项基本功能。在本文中，提出了一种人工神经控制器来解决自动地面车辆（AGV）的轨迹跟踪问题。该控制器是基于早先已经设计的分数阶比例积分微分（FOPID）控制来实现的。分析并提出了非完整的AGV模型类型。该模型包括VGA的运动，动态特性和驱动系统。人工神经控制器由两个人工神经网络（ANN）组成，这些神经网络旨在控制AGV的输入。为了训练这两个人工神经网络，使用Levenberg-Marquardt（LM）算法获取ANN的参数。通过给定的参考轨迹已经验证了所提出的控制器的有效性。所获得的结果表明，与FOPID控制器相比，在最小化轨迹跟踪误差方面有相当大的改进。

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