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Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

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posted on 2023-06-09, 01:53 authored by Auday Basheer Essa Al-Mayyahi, William WangWilliam Wang, Phil BirchPhil Birch
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

International Journal of Mechatronics and Automation

ISSN

2045-1067

Publisher

Inderscience

Issue

2/3

Volume

5

Page range

140-153

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-06-27

First Open Access (FOA) Date

2016-06-27

First Compliant Deposit (FCD) Date

2016-06-26

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