Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

Antonio Matus-Vargas; Gustavo Rodriguez-Gomez; Jose Martinez-Carranza

doi:10.1007/978-3-030-20190-6_3

Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

Antonio Matus-Vargas^*, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

Abstract

A key activity in the deployment of quadcopters is controller tuning. This research chapter addresses the problem of how to optimize the parameter set of a controller for a quadcopter. Existing research in iterative controller optimization has centered on the use of linear models of the process. However, in this research chapter, we propose a procedure based on conjugate gradient optimization for controller tuning when the dynamic model is nonlinear and the test signals are stochastic. To validate the findings, a bipartite ROS application was implemented. The first part corresponds to the orientation controller of the drone which runs on the onboard computer. The second part carries out the position controller and runs on a ground station computer. ROS Indigo Igloo is used for the code of this chapter.

Original language	English
Title of host publication	Robot Operating System (ROS)
Subtitle of host publication	The Complete Reference
Editors	Anis Koubaa
Publisher	Springer Verlag
Pages	55-79
Number of pages	25
Volume	4
ISBN (Electronic)	978-3-030-20190-6
ISBN (Print)	978-3-030-20189-0
DOIs	https://doi.org/10.1007/978-3-030-20190-6_3
Publication status	E-pub ahead of print - 29 Jun 2019

Publication series

Name	Studies in Computational Intelligence
Volume	831
ISSN (Print)	1860-949X

Keywords

Nonlinear control
Numerical optimization
Quadcopter
Unmanned aerial vehicle

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10.1007/978-3-030-20190-6_3

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Matus-Vargas, A., Rodriguez-Gomez, G., & Martinez-Carranza, J. (2019). Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals. In A. Koubaa (Ed.), Robot Operating System (ROS): The Complete Reference (Vol. 4, pp. 55-79). (Studies in Computational Intelligence; Vol. 831). Springer Verlag. Advance online publication. https://doi.org/10.1007/978-3-030-20190-6_3

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author = "Antonio Matus-Vargas and Gustavo Rodriguez-Gomez and Jose Martinez-Carranza",

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Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals. / Matus-Vargas, Antonio; Rodriguez-Gomez, Gustavo; Martinez-Carranza, Jose.
Robot Operating System (ROS): The Complete Reference . ed. / Anis Koubaa. Vol. 4 Springer Verlag, 2019. p. 55-79 (Studies in Computational Intelligence; Vol. 831).

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

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T1 - Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

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AB - A key activity in the deployment of quadcopters is controller tuning. This research chapter addresses the problem of how to optimize the parameter set of a controller for a quadcopter. Existing research in iterative controller optimization has centered on the use of linear models of the process. However, in this research chapter, we propose a procedure based on conjugate gradient optimization for controller tuning when the dynamic model is nonlinear and the test signals are stochastic. To validate the findings, a bipartite ROS application was implemented. The first part corresponds to the orientation controller of the drone which runs on the onboard computer. The second part carries out the position controller and runs on a ground station computer. ROS Indigo Igloo is used for the code of this chapter.

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Parametric Optimization for Nonlinear Quadcopter Control Using Stochastic Test Signals

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