Title: Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles
Authors: Auday Al-Mayyahi; Weiji Wang; Phil Birch
Addresses: School of Engineering and Informatics, Department of Engineering and Design, University of Sussex, Brighton, UK ' School of Engineering and Informatics, Department of Engineering and Design, University of Sussex, Brighton, UK ' School of Engineering and Informatics, Department of Engineering and Design, University of Sussex, Brighton, UK
Abstract: 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 AGV. 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.
Keywords: autonomous ground vehicles; AGVs; trajectory tracking; artificial neural networks; ANNs; FOPID controllers; fractional order PID; proportional integral derivative; Levenberg-Marquardt; trajectory tracking; controller design; non-holonomic modelling; vehicle kinematics; vehicle dynamics; vehicle control.
International Journal of Mechatronics and Automation, 2015 Vol.5 No.2/3, pp.140 - 153
Received: 27 Jun 2015
Accepted: 24 Nov 2015
Published online: 18 Apr 2016 *