Enriquez Fernandez, A., & Everett, L. J., & Cedeno, M. (2021, March), UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING  Paper presented at ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas. 10.18260/1-2--36412

\bibitem{asee_peer_36412}
  Enriquez Fernandez, A., \& Everett, L. J., \& Cedeno, M. (2021, March), \emph{UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING}
  Paper presented at ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas.
  10.18260/1-2--36412

Andres Enriquez Fernandez, Louis J Everett P.E., and Miguel Cedeno.  "UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING".  ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas, 2021, March.  ASEE Conferences, 2021.  https://peer.asee.org/36412 Internet.  26 Apr, 2024

\bibitem{asee_peer_36412}
  Andres Enriquez Fernandez, Louis J Everett P.E., and Miguel Cedeno.
  "UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING".
  \emph{ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas, 2021, March}.
  ASEE Conferences, 2021.
  https://peer.asee.org/36412 Internet.  26 Apr, 2024

@INPROCEEDINGS{asee_peer_36412
author = "Andres Enriquez Fernandez, Louis J Everett P.E., and Miguel Cedeno"
title = "UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING"
booktitle = "ASEE 2021 Gulf-Southwest Annual Conference"
year = "2021"
month = "March"
address = "Waco, Texas"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/36412}
number = {10.18260/1-2--36412}
}

TY  - CPAPER
AB  - Parameter identification of Unmanned Aerial Vehicles (UAV) is very helpful for understanding cause-effect relationships of physical phenomenon, investigating system performance and characteristics, fault diagnostics, control development/tuning, and more. Traditional ways of performing parameter identification involve establishing a mathematical model that describes the system’s behavior. The equations in the model contain parameters that are estimated indirectly from measured flight data. This parameter identification process requires knowledge of the physics involved. Also, it necessitates a careful consideration of the aircraft instrumentation for accurate measurements. It also requires careful design of the flight maneuvers to ensure thorough excitation of the flight dynamics involved. Finally, one must select a suitable identification method. 
The purpose of this paper is to show the application of machine learning for parameter identification of a UAV model. The machine learning algorithm does not require developing parameterized models; hence it is an equation-less identification method. To provide input to the system, a simulation model of the aircraft is generated. The parameters of the model can be modified in the simulation. The aircraft flight measurement data is obtained directly from the model as simulation outputs from a predetermined flight path. The data is submitted to a machine learning algorithm that is able to read and recognize the data. The machine learning algorithm is trained with a set of flight data that incorporates variations in the parameters to be identified. Finally, the algorithm is tested by feeding unknown flight data to predict the output. 
To achieve autonomous and consistent flights, a Software-In-the-Loop (SIL) simulation is constructed between X-Plane and Mission Planner. X-Plane is a realistic flight simulator where the UAV model is created, and flight physics are modeled. Mission Planner is the Ground Control Station (GCS) that generates and sends the flight commands to be executed in X-Plane. Several machine learning regression models are explored including linear regression, regression trees, Gaussian process regression, support vector machines and ensembles of regression trees.  

AU  - Andres Enriquez Fernandez
AU  - Louis J Everett P.E.
AU  - Miguel Cedeno
CY  - Waco, Texas
DA  - 2021/03/24
PB  - ASEE Conferences
TI  - UAV PARAMETER ESTIMATION THROUGH MACHINE LEARNING
UR  - https://peer.asee.org/36412
DO  - 10.18260/1-2--36412
ER  -