Abstract
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 language | English (US) |
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Title of host publication | 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665486071 |
DOIs | |
State | Published - 2022 |
Event | 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 - Portsmouth, United States Duration: Sep 18 2022 → Sep 22 2022 |
Publication series
Name | AIAA/IEEE Digital Avionics Systems Conference - Proceedings |
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Volume | 2022-September |
ISSN (Print) | 2155-7195 |
ISSN (Electronic) | 2155-7209 |
Conference
Conference | 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 |
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Country/Territory | United States |
City | Portsmouth |
Period | 09/18/22 → 09/22/22 |
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.
Keywords
- 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