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Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control

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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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Authors and Affiliations

  1. Computer Science and Engineering Department, University of South Florida, 4202 East Fowler Avenue, ENB 118, Tampa, FL, 33620, USA

    Konstantinos Dalamagkidis & Les A. Piegl

  2. Department of Electrical and Computer Engineering, University of Denver, Clarence M. Knudson Hall, 300, 2390 S. York Street, Denver, CO, 80208, USA

    Kimon P. Valavanis

Authors
  1. Konstantinos Dalamagkidis
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  2. Kimon P. Valavanis
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  3. Les A. Piegl
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Correspondence to Konstantinos Dalamagkidis.

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Dalamagkidis, K., Valavanis, K.P. & Piegl, L.A. Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control. J Intell Robot Syst 57, 351–369 (2010). https://doi.org/10.1007/s10846-009-9366-2

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  • DOI: https://doi.org/10.1007/s10846-009-9366-2

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