Control of Tethered Drones with state and input Constraints - A Unified Model Approach

Tal Glick, Shai Arogeti

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

Abstract

This paper focuses on the control design problem of a tethered drone. The suggested concept has the potential to solve some basic difficulties related to the use of small drones, such as their short flight duration, limited lifting ability and the need of precise indoor position sensing. Here, the end of the tether is connected to the drone, while the other side is wrapped on a spool driven by a DC motor, fixed on the ground. Under the assumption that the tether is kept taut at all time, a full autonomous flight can be achieved indoors, where the tether functions as a position sensor. The tether is pulled from its two sides, by the ground motor and the drone. Critical constraints, driven from this configuration, demand positive tether tension and limited flight space. Hence, a unified model is developed for the combined ground-aerial system, and a model predictive control approach is taken, to allow for optimal control under state and input constraints. Simulations results show the performance of the design for both set-point and trajectory tracking cases.

Original languageAmerican English
Title of host publication2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018
Pages995-1002
Number of pages8
DOIs
StatePublished - 31 Aug 2018
Event2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018 - Dallas, United States
Duration: 12 Jun 201815 Jun 2018

Publication series

Name2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018

Conference

Conference2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018
Country/TerritoryUnited States
CityDallas
Period12/06/1815/06/18

Keywords

  • Model Predictive Control
  • constrained control
  • position control
  • tethered Autonomous vehicles

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Optimization

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