A hybrid learning approach for the simulation of unmanned aircraft vehicle dynamics

Yang Yu, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The computation costs of predicting flight trajectories of unmanned aircraft vehicle (UAV) can be expensive or even prohibitive especially for the large number of UAVs in UAV traffic management (UTM). This study proposes the concept of hybrid learning, a novel approach based on data-driven learning and underlying physics, as a computationally efficient method for the simulation of UAV dynamics. The hybrid learning integrates the underlying physics of UAV systems into learning models such as neural networks to reduce the training and simulation costs. The application of hybrid learning for simulating UAV dynamics is demonstrated using a recently introduced physics-aware network known as the deep residual recurrent neural network (DR-RNN) on a quadcopter model. The UAV dynamics are described using a six degrees-of-freedom model. The DR-RNN is used to predict the response of UAV under arbitrary control inputs. The results show that the DR-RNN can accurately predict UAV responses and has excellent extrapolation capabilities. Moreover, the DR-RNN exhibits superior computation efficiency compared with a classical numerical method, the first-order Runge-Kutta method, highlighting its suitability in serving as surrogate models for UAV systems.

Original languageEnglish (US)
Title of host publicationAIAA Aviation 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105890
DOIs
StatePublished - 2019
EventAIAA Aviation 2019 Forum - Dallas, United States
Duration: Jun 17 2019Jun 21 2019

Publication series

NameAIAA Aviation 2019 Forum

Conference

ConferenceAIAA Aviation 2019 Forum
CountryUnited States
CityDallas
Period6/17/196/21/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Aerospace Engineering

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