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Data-Driven Control Strategies for Rotary Wing Aerial Vehicles

Simão Caeiro, Bruno J. Guerreiro, Rita Cunha

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

Abstract

To evaluate the performance of data-driven control methods applied to Unmanned Aerial Vehicles (UAVs), this work addresses the implementation of these strategies, particularly the Data-enabled Predictive Control (DeePC) algorithm. This strategy computes optimal controls for unknown systems through real-time output feedback using a receding horizon implementation. Moreover, this research investigates the influence of different hyperparameters on the DeePC’s performance and conducts a realistic comparison between this method and two model-based control approaches: Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC). The simulation results validate the applicability of DeePC algorithm and highlight its superior robustness to system degradation and yaw calibration errors. However, it is less suitable for complex nonlinear systems subject to aggressive trajectories.

Original languageEnglish
Title of host publicationRobot 2023
Subtitle of host publication6th Iberian Robotics Conference Advances in Robotics, Volume 2
EditorsLino Marques, Cristina Santos, José Luís Lima, Danilo Tardioli, Manuel Ferre
PublisherSpringer Science and Business Media Deutschland GmbH
Pages62-73
Number of pages12
ISBN (Print)9783031591662
DOIs
Publication statusPublished - 2024
Event6th Iberian Robotics Conference, Robot 2023 - Coimbra, Portugal
Duration: 22 Nov 202324 Nov 2023

Publication series

NameLecture Notes in Networks and Systems
Volume978 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference6th Iberian Robotics Conference, Robot 2023
Country/TerritoryPortugal
CityCoimbra
Period22/11/2324/11/23

Keywords

  • Data-driven Control
  • Data-enabled Predictive Control
  • Performance Evaluation
  • Unmanned Aerial Vehicles

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