Affiliation of Author(s):自动化学院
Journal:Harbin Gongcheng Daxue Xuebao
Abstract:Considering the modeling error and carrier airwake problems when landing carrier-based aircraft, a dynamic inverse control method based on an improved neural network is proposed to improve the accuracy and speed of the landing controller. The neural network is introduced into the dynamic inverse control to dynamically compensate the model error in order to meet the controller requirement. The convergence speed of the neural network is improved by introducing an adaptive learning rate. The simulation results show that the dynamic inversion controller based on the improved neural network has good inhibitory and compensatory effects on the system modeling error. The convergence rate is greatly improved and the steady-state error tends to zero. It also has a good inhibitory effect on the ship's airstream. It effectively maintains the attitude and speed of carrier-based aircraft and ensures safe landing. © 2018, Editorial Department of Journal of HEU. All right reserved.
ISSN No.:1006-7043
Translation or Not:no
Date of Publication:2018-10-05
Co-author:Zhou, Jun,Yu, Chaojun,wxd
Correspondence Author:Jiang Ju
Date of Publication:2018-10-05