IJMA0502-0309 AL-MAYYAHI.pdf (539.25 kB)
Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles
journal contribution
posted on 2023-06-09, 01:53 authored by Auday Basheer Essa Al-Mayyahi, William WangWilliam Wang, Phil BirchPhil BirchTrajectory 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 AutomationISSN
2045-1067Publisher
InderscienceExternal DOI
Issue
2/3Volume
5Page range
140-153Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-06-27First Open Access (FOA) Date
2016-06-27First Compliant Deposit (FCD) Date
2016-06-26Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC