Embedded model predictive control of unmanned micro aerial vehicles

Tomas Baca, Giuseppe Loianno, Martin Saska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

Original languageEnglish (US)
Title of host publication2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages992-997
Number of pages6
ISBN (Electronic)9781509018666
DOIs
StatePublished - Sep 22 2016
Event21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016 - Miedzyzdroje, Poland
Duration: Aug 29 2016Sep 1 2016

Other

Other21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016
CountryPoland
CityMiedzyzdroje
Period8/29/169/1/16

Fingerprint

Model predictive control
Model Predictive Control
Antennas
Disturbance rejection
Unmanned aerial vehicles (UAV)
Helicopters
Embedded systems
Global positioning system
Stabilization
Disturbance Rejection
Trajectory Tracking
Experiments
Trajectories
Aircraft
Helicopter
Embedded Systems
Controllers
Experiment
Sensors
Optimal Control

ASJC Scopus subject areas

  • Control and Optimization
  • Control and Systems Engineering
  • Artificial Intelligence

Cite this

Baca, T., Loianno, G., & Saska, M. (2016). Embedded model predictive control of unmanned micro aerial vehicles. In 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016 (pp. 992-997). [7575273] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MMAR.2016.7575273

Embedded model predictive control of unmanned micro aerial vehicles. / Baca, Tomas; Loianno, Giuseppe; Saska, Martin.

2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 992-997 7575273.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Baca, T, Loianno, G & Saska, M 2016, Embedded model predictive control of unmanned micro aerial vehicles. in 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016., 7575273, Institute of Electrical and Electronics Engineers Inc., pp. 992-997, 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016, Miedzyzdroje, Poland, 8/29/16. https://doi.org/10.1109/MMAR.2016.7575273
Baca T, Loianno G, Saska M. Embedded model predictive control of unmanned micro aerial vehicles. In 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 992-997. 7575273 https://doi.org/10.1109/MMAR.2016.7575273
Baca, Tomas ; Loianno, Giuseppe ; Saska, Martin. / Embedded model predictive control of unmanned micro aerial vehicles. 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 992-997
@inproceedings{ae2116952092460cb4b4a19be6aa7633,
title = "Embedded model predictive control of unmanned micro aerial vehicles",
abstract = "We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.",
author = "Tomas Baca and Giuseppe Loianno and Martin Saska",
year = "2016",
month = "9",
day = "22",
doi = "10.1109/MMAR.2016.7575273",
language = "English (US)",
pages = "992--997",
booktitle = "2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Embedded model predictive control of unmanned micro aerial vehicles

AU - Baca, Tomas

AU - Loianno, Giuseppe

AU - Saska, Martin

PY - 2016/9/22

Y1 - 2016/9/22

N2 - We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

AB - We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

UR - http://www.scopus.com/inward/record.url?scp=84991782118&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84991782118&partnerID=8YFLogxK

U2 - 10.1109/MMAR.2016.7575273

DO - 10.1109/MMAR.2016.7575273

M3 - Conference contribution

AN - SCOPUS:84991782118

SP - 992

EP - 997

BT - 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -