Online Learning and Control for Data-Augmented Quadrotor Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00600830" target="_blank" >RIV/67985807:_____/24:00600830 - isvavai.cz</a>
Alternative codes found
RIV/49777513:23520/24:43973102
Result on the web
<a href="https://doi.org/10.1016/j.ifacol.2024.08.532" target="_blank" >https://doi.org/10.1016/j.ifacol.2024.08.532</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2024.08.532" target="_blank" >10.1016/j.ifacol.2024.08.532</a>
Alternative languages
Result language
angličtina
Original language name
Online Learning and Control for Data-Augmented Quadrotor Model
Original language description
The ability to adapt to changing conditions is a key feature of a successful autonomous system. In this work, we use the Recursive Gaussian Processes (RGP) for identification of the quadrotor air drag model online, without the need to precollect training data. The identified drag model then augments a physics-based model of the quadrotor dynamics, which allows more accurate quadrotor state prediction with increased ability to adapt to changing conditions. This data-augmented physics-based model is utilized for precise quadrotor trajectory tracking using the suitably modified Model Predictive Control (MPC) algorithm. The proposed modelling and control approach is evaluated using the Gazebo simulator and it is shown that the proposed approach tracks a desired trajectory with a higher accuracy compared to the MPC with the non-augmented (purely physics-based) model.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IFAC-PapersOnLine. Volume 58, Issue 15. 20th IFAC Symposium on System Identification SYSID 2024
ISBN
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ISSN
2405-8971
e-ISSN
2405-8963
Number of pages
6
Pages from-to
223-228
Publisher name
Elsevier
Place of publication
Amsterdam
Event location
Boston
Event date
Jul 17, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001316057100038