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

This study is focused on improving the behavior of the "armored 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 optimize 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 armored vehicle. Thus, hardware-in-the-loop (HIL) simulation testing has been adopted in this study to analyze 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.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01897011
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 23 2023 3:08PM