Drone reference tracking in a non-inertial frame: control, design and experiment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Drones and mobile robots in general experience motion sickness when put inside a GPS denied moving environment. This navigation problem is of a novel nature and barely explored in the literature. The objective of this paper is to design a control strategy for drone reference tracking inside the moving environment. First, we provide an initial formulation of the problem where the non-inertial frame is assumed to have only a translation motion relative to the inertial reference frame. Then we derive the dynamic model of the drone in the non-inertial frame using the relative motion principle. After that, we use a combined Sliding mode controller and Extended Kalman Filter with Unknown Inputs (EKF-UI) for trajectory tracking. The EKF-UI enables to jointly estimates the states of the drone and the non-inertial frame accelerations. The sliding mode control laws are computed using the measured and estimated states to ensure the asymptotic convergence of the whole observer-based control strategy. The performance of the proposed observer-based control strategy is tested through simulation and experiment.

Original languageEnglish (US)
Title of host publication2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665486071
StatePublished - 2022
Event41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 - Portsmouth, United States
Duration: Sep 18 2022Sep 22 2022

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209


Conference41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Funds (BAS/1/1627-01-01, BAS/1/1682-01-01), and KAUST AI-Initiative. The authors would also like to thank Mr. Olivier Toupet from the Jet Propulsion Laboratory for suggesting the idea for this research.

Publisher Copyright:
© 2022 IEEE.


  • Extended Kalman Filter with Unknown Inputs
  • non-inertial frame
  • reference tracking
  • sliding mode
  • Unmanned Aerial Vehicles

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering


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