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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Dalamagkidis, K., Valavanis, K.P., Piegl, L.A. (2009). Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control. In: Valavanis, K.P., Beard, R., Oh, P., Ollero, A., Piegl, L.A., Shim, H. (eds) Selected papers from the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A. June 8–10, 2009. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8764-5_18
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DOI: https://doi.org/10.1007/978-90-481-8764-5_18
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