Title: Deep learning based neuro-PI for yaw disturbance rejection control: hardware-in-the-loop simulation using scaled armoured vehicle platform

Authors: Vimal Rau Aparow; Khisbullah Hudha; Hishamuddin Jamaluddin; Zulkiffli Abd Kadir

Addresses: Automated Vehicle Engineering (AVES) Research Group, Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor, Malaysia ' Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 56000, Kuala Lumpur, Malaysia ' Faculty of Engineering and Information Technology, Department of Electrical and Electronics Engineering, Southern University College, PTD 64888, Jalan Selatan Utama, 81300, Skudai, Johor, Malaysia ' Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 56000, Kuala Lumpur, Malaysia

Abstract: This study is focused on improving the behaviour of the "armoured vehicle" in terms of handling responses during firing by enhancing the performance of yaw disturbance rejection control (YDRC). A YDRC is designed to overcome external disturbance using deep learning-based Neuro-PI controller to optimise the variables of the neural network. Moreover, cost-effective approaches are required to evaluate the capability of the controller to enhance the lateral dynamic response of the armoured vehicle. Thus, hardware-in-the-loop (HIL) simulation testing has been adopted in this study to analyse the response of the YDRC. The HIL simulation testing was performed using Cronos Compact data acquisition box developed by integrated measurement and control and integrated with Matlab Simulink. The percentage of error between HIL and software-in-the-loop (SIL) simulation testing using deep learning-based based neuro PI of YDRC is less than 7% for overall simulation testing.

Keywords: YDRC; yaw disturbance rejection control; hardware-in-the-loop simulation; IMC; integrated measurement and control; deep learning based neuro-PI controller; armoured vehicle.

DOI: 10.1504/IJHVS.2023.133364

International Journal of Heavy Vehicle Systems, 2023 Vol.30 No.4, pp.426 - 453

Received: 28 Aug 2021
Accepted: 05 Oct 2021
Published online: 14 Sep 2023 *

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