GA-BPNN Based Unmanned Guided Aerial Vehicle Gliding Heeling Behavior Research
This study introduces the integration of unmanned driving technology into airport flight zone op-erations,focusing on the platooning model between Unmanned Ground Vehicles(UGVs)and manned air-craft on main taxiways.Acknowledging the kinematic differences between UGVs and manned aircraft,we developed distinct trapezoidal and S-type kinematic models to characterize their motion.To enhance ve-locity prediction accuracy,a neural network-based velocity optimization model was established.Given the substantial deviation in the regression coefficients of the hidden layer in traditional Neural Networks(NN),we introduced a Genetic Algorithm-optimized Backpropagation Neural Network(GA-BPNN).Simulation experiments conducted at Ezhou Huahu Airport validated the superiority of GA-BPNN in ve-locity error,aircraft predicted velocity fluctuation range,and stability of unmanned vehicle velocity predic-tion.The GA-BPNN achieved a velocity error of less than 1％,with aircraft predicted velocity fluctuations within±2 m/s,and unmanned vehicle velocity prediction stability with a motion error of less than 0.5％.
car followingunmanned vehicleGAneural networkspeed prediction
万方数据
维普
