Fault-Tolerant Model Predictive Control for Multirotor UAVs

Emil Lykke Diget, Agus Hasan*, Poramate Manoonpong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

This paper presents a method for advanced fault tolerant control (FTC) of multirotor unmanned aerial vehicles (UAVs), which includes anomaly detection on sensor measurements, fault estimation on actuators, and a robust model predictive control (MPC). To detect anomalies on the sensor measurements, an Echo State Network is used. System states and faults are estimated using an adaptive extended Kalman filter. The system is further controlled using MPC. The method is tested in numerical simulations with a hexacopter dynamic model. Simulation results show the ability of the FTC to handle failure with different even and uneven actuator faults.
Original languageEnglish
Title of host publication2022 American Control Conference, ACC 2022
PublisherIEEE
Publication date2022
Pages4305-4310
ISBN (Electronic)9781665451963
DOIs
Publication statusPublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: 8. Jun 202210. Jun 2022

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period08/06/202210/06/2022
SponsorBoeing, et al., General Motors Co., MathWorks, Mitsubishi Electric Research Laboratory ((MERL), Quanser
SeriesProceedings of the American Control Conference
Volume2022-June
ISSN0743-1619

Keywords

  • adaptive extended Kalman filter
  • AI-based methods
  • echo state network
  • fault-tolerant control
  • hexacopter

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