Neural adaptive back stepping flight controller for a ducted fan UAV

R. Aruneshwaran, Jianliang Wang, S. Suresh, T. K. Venugopalan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

In this paper, we present a neural adaptive back-stepping flight controller for a ducted fan UAV whose dynamics is characterized by uncertainties and highly coupled nonlinearities. The proposed neural adaptive back-stepping controller can handle unknown nonlinearities, unmodeled dynamics and external wind disturbances. A single layer radial basis function network is used to approximate the virtual control law derived using back stepping approach, which provides necessary stability and tracking performances. The neural controller parameters are adapted online using Lyapunov based update laws. The proposed controller is evaluated using nonlinear desktop simulation model of a typical ducted fan UAV performing bop-up maneuver. Three neural adaptive controllers are implemented to handle attitude command altitude hold system, one in each body axis. A separate neural controller is implemented to track the height command for autonomous takeoff and landing. The results indicate that the proposed controller can stabilize the ducted fan UAV and provide necessary tracking performance.

Original languageEnglish
Title of host publicationWCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
Pages2370-2375
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event10th World Congress on Intelligent Control and Automation, WCICA 2012 - Beijing, China
Duration: 6 Jul 20128 Jul 2012

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference10th World Congress on Intelligent Control and Automation, WCICA 2012
Country/TerritoryChina
CityBeijing
Period6/07/128/07/12

Keywords

  • Adaptive Back stepping
  • Ducted fan
  • Lyapunov stability
  • Radial basis function neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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