Drone reference tracking in a non-inertial frame using sliding mode control based Kalman filter with unknown input

Yasmine Marani, Kuat Telegenov, Eric Feron, Meriem Taous Laleg Kirati

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

1 Scopus citations

Abstract

As surprising as it seems, drones and mobile robots in general experience motion sickness when put in a moving environment. This navigation problem has been little if ever explored in the literature. Therefore we propose a formulation of the problem in the simplest possible way as a starting point. The objective of simplifying the problem is to avoid using sophisticated control and measurement devices, such as cameras, and rely instead on control system strategies. In this paper, the moving environment to which is associated a non-inertial frame is considered to have translation motion with respect to the inertial reference frame. The goal is to make the drone track a desired trajectory inside the moving environment based only on the measurements obtained with respect to the non-inertial frame. First, a model representing the dynamics of the drone in the non-inertial frame is developed using the relative motion principles. The new model takes into account the accelerations of the moving environment where they are considered as bounded unknown inputs. Then, a Kalman Filter with Unknown Inputs (KF-UI) is used to estimate simultaneously the states of the drone and the accelerations of the non-inertial frame. Finally, a Sliding Mode controller is implemented. Two numerical simulations were conducted to illustrate the performance of the combined KF-UI and Sliding Mode controller: the first one represents an ideal case where the non-inertial frame's accelerations are constant. The second one illustrates flying a drone in an elevator. The obtained results form an encouraging foundation for follow-on experiments.

Original languageEnglish (US)
Title of host publication2022 IEEE Conference on Control Technology and Applications, CCTA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-16
Number of pages8
ISBN (Electronic)9781665473385
DOIs
StatePublished - 2022
Event2022 IEEE Conference on Control Technology and Applications, CCTA 2022 - Trieste, Italy
Duration: Aug 23 2022Aug 25 2022

Publication series

Name2022 IEEE Conference on Control Technology and Applications, CCTA 2022

Conference

Conference2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Country/TerritoryItaly
CityTrieste
Period08/23/2208/25/22

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Control and Systems Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Drone reference tracking in a non-inertial frame using sliding mode control based Kalman filter with unknown input'. Together they form a unique fingerprint.

Cite this