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 proceedingChapter in a book


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 languageEnglish
Title of host publicationRobot Operating System (ROS)
Subtitle of host publicationThe Complete Reference
EditorsAnis Koubaa
PublisherSpringer Verlag
Number of pages25
ISBN (Electronic)978-3-030-20190-6
ISBN (Print)978-3-030-20189-0
Publication statusE-pub ahead of print - 29 Jun 2019

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Nonlinear control
  • Numerical optimization
  • Quadcopter
  • Unmanned aerial vehicle


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