Abstract
This paper proposes a practical prediction procedure for vertical displacement of a Rotarywing Unmanned Aerial Vehicle (RUAV) landing deck in the presence of stochastic sea state disturbances. A proper time series model tending to capture characteristics of the dynamic relationship between an observer and a landing deck is constructed, with model orders determined by a novel principle based on Bayes Information Criterion (BIC) and coefficients identified using the Forgetting Factor Recursive Least Square (FFRLS) method. In addition, a fast-converging online multi-step predictor is developed, which can be implemented more rapidly than the Auto-Regressive (AR) predictor as it requires less memory allocations when updating coefficients. Simulation results demonstrate that the proposed prediction approach exhibits satisfactory prediction performance, making it suitable for integration into ship-helicopter approach and landing guidance systems in consideration of computational capacity of the flight computer.
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Recommended by Editorial Board member Wen-Hua Chen under the direction of Editor Myotaeg Lim.
Xilin Yang received his M.E. degree from the Northwestern Polytechnical University in 2007, and his Ph.D. degree from the University of New South Wales, Australia, in 2010. Currently, he is a research fellow with the Australian Research Centre for Aerospace Automation, Brisbane, Australia. His research interests include nonlinear estimation and control, system identification, UAV collision avoidance and vision-based flight control.
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Yang, X. Displacement motion prediction of a landing deck for recovery operations of rotary UAVs. Int. J. Control Autom. Syst. 11, 58–64 (2013). https://doi.org/10.1007/s12555-011-0157-8
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DOI: https://doi.org/10.1007/s12555-011-0157-8