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Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network

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Abstract

This paper describes a new non-linear control technique applied to the heave control of an unmanned rotorcraft. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Desired trajectories are calculated to smoothly achieve a sequence of random step changes in desired height according to certain optimal criterion and plant limitations. Control inputs are then determined using the MATLAB® optimisation toolbox to achieve those desired trajectories for the plant heave model. Finally, a neural network is trained to mimic the control inputs resulting from the optimisation process. The neural network controller produces trajectories closely resembling the results from the optimisation process but with a much reduced computation time. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controller’s ability to operate in high disturbance conditions and outperform a proportional-derivative (PD) controller.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University College, University of New South Wales, Canberra, ACT, Australia

    Matthew Garratt & Sreenatha Anavatti

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  1. Matthew Garratt
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  2. Sreenatha Anavatti
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Correspondence to Matthew Garratt.

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Garratt, M., Anavatti, S. Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network. J Intell Robot Syst 66, 495–504 (2012). https://doi.org/10.1007/s10846-011-9634-9

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  • DOI: https://doi.org/10.1007/s10846-011-9634-9

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