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Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control

Authors
  • Dalamagkidis, Konstantinos1
  • Valavanis, Kimon P.2
  • Piegl, Les A.1
  • 1 University of South Florida, Computer Science and Engineering Department, 4202 East Fowler Avenue, ENB 118, Tampa, FL, 33620, USA , Tampa (United States)
  • 2 University of Denver, Department of Electrical and Computer Engineering, Clarence M. Knudson Hall, 300, 2390 S. York Street, Denver, CO, 80208, USA , Denver (United States)
Type
Published Article
Journal
Journal of Intelligent & Robotic Systems
Publisher
Springer-Verlag
Publication Date
Sep 16, 2009
Volume
57
Issue
1-4
Identifiers
DOI: 10.1007/s10846-009-9366-2
Source
Springer Nature
Keywords
License
Yellow

Abstract

Safe operations of unmanned rotorcraft hinge on successfully accommodating failures during flight, either via control reconfiguration or by terminating flight early in a controlled manner. This paper focuses on autorotation, a common maneuver used to bring helicopters safely to the ground even in the case of loss of power to the main rotor. A novel nonlinear model predictive controller augmented with a recurrent neural network is presented that is capable of performing an autonomous autorotation. Main advantages of the proposed approach are on-line, real-time trajectory optimization and reduced hardware requirements.

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