UAV Takeoff Decision Based on Neural Network Model of Takeoff Capability

Authors

Yongtao Peng, 1. Key Laboratory of National Defense for Flight Control Integration, Research Institute of Xi’an Flight Automatic Control, Xi’an 710065, China; ;
Yueping Wang, 2. Department of Flight Control, Flight Automatic Control Research Institute, Shanxi Xi’an 710065, China;
Xiaoting Wang, 2. Department of Flight Control, Flight Automatic Control Research Institute, Shanxi Xi’an 710065, China;

Abstract

Abstract: To enhance the safety in case of engine flameout failure, a new type of UAV takeoff decision based on neural network capacity model was proposed. Two capacity parameters of takeoff safety in case of engine flameout failure were defined, one is the maximum velocity for a safe takeoff and the other is the minimum velocity for a safe shut down. A calculation method based on iterative simulations for those parameters under multiple flight conditions was introduced. Double layer neural networks were used to model the relationship between flight conditions and the capacity parameters, to realize the compressive storage and high precision adopted of the parameters. A takeoff decision based on online capacity calculation values from neural network capacity model of the UAV was derived. Simulation results show the strong practicality and benefit for enhancing UAV safety robustness of the proposed take off decision strategy.

Recommended Citation

Peng, Yongtao; Wang, Yueping; and Wang, Xiaoting (2020) "UAV Takeoff Decision Based on Neural Network Model of Takeoff Capability," Journal of System Simulation: Vol. 27: Iss. 11, Article 25.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss11/25

First Page

2797

Revised Date

2015-03-27

DOI Link

https://doi.org/

Last Page

2803

CLC

V238

Recommended Citation

Peng Yongtao, Wang Yueping, Wang Xiaoting. UAV Takeoff Decision Based on Neural Network Model of Takeoff Capability[J]. Journal of System Simulation, 2015, 27(11): 2797-2803.