An iterative learning approach for motion control and performance enhancement of quadcopter UAVs

Mohammad Shaqura, Jeff S. Shamma

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

Abstract

Mobile robot modeling is a challenging task especially for vehicles with complex nonlinear dynamics. Quadcopter UAVs are agile systems that are often described with a nominal nonlinear model that neglects various complicated dynamic phenomena for the sake of easier analysis and control design. This simplification leads to limiting the vehicle performance. To overcome this issue, an iterative learning approach is presented where a nominal representation of the system dynamics is used in conjunction with flight trials to improve performance. The objective is to learn to aggressively navigate a quadcopter through a course while avoiding obstacles. The performance is assessed by overall navigation time. The trajectory is optimized iteratively by blending an approximate gradient from the simplified nominal model with actual realized flight trajectories. The resulting optimization is a quadratic program, which can be solved efficiently. High fidelity quadcopter simulations with multiple test cases show significantly improved performance through repeated trials.

Original languageEnglish (US)
Title of host publicationInternational Conference on Control, Automation and Systems
PublisherIEEE Computer Society
Pages285-290
Number of pages6
ISBN (Electronic)9788993215151
StatePublished - Dec 10 2018
Externally publishedYes
Event18th International Conference on Control, Automation and Systems, ICCAS 2018 - PyeongChang, Korea, Republic of
Duration: Oct 17 2018Oct 20 2018

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2018-October
ISSN (Print)1598-7833

Conference

Conference18th International Conference on Control, Automation and Systems, ICCAS 2018
CountryKorea, Republic of
CityPyeongChang
Period10/17/1810/20/18

Keywords

  • Autonomous mobile robots
  • Iterative improvement
  • Learning control
  • Modeling errors.
  • Robot navigation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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