Finite-Memory-Structured Online Training Algorithm for System Identification of Unmanned Aerial Vehicles With Neural Networks

Hyun Ho Kang, Dong Kyu Lee, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, we propose a novel finite-memory-structured online training algorithm (FiMos-TA) for neural networks to identify and predict the unknown functions and states of an unmanned aerial vehicle (UAV). The proposed FiMos-TA is designed based on a system reconstructed by accumulating the states from the UAV dynamics. The system is redefined by replacing the unknown nonlinear functions of the UAV with neural networks, and a random walk modeling is adopted to design a training algorithm. The proposed FiMos-TA with a finite memory structure updates the weights of the neural network by accumulating the refined measurements of a UAV on the receding horizon. The training law of the proposed FiMos-TA is obtained by introducing the Frobenius norm and confirms a robust performance against modeling uncertainties and identification errors. The robustness and accuracy of the proposed FiMos-TA are verified through experiments.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE/ASME Transactions on Mechatronics
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Autonomous aerial vehicles
  • Finite memory structure
  • Indexes
  • Mathematical models
  • Mechatronics
  • Neural networks
  • Recurrent neural networks
  • Training
  • neural network
  • system identification
  • training law
  • unmanned aerial vehicle (UAV)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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