Intelligent feedforward gust alleviation based on neural network

作     者：Yitao ZHOU Zhigang WU Chao YANG Yitao ZHOU;Zhigang WU;Chao YANG

作者机构：School of Aeronautic Science and EngineeringBeihang UniversityBeijing 100191China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第3期

页      面：116-132页

核心收录：

学科分类：08[工学] 081105[工学-导航、制导与控制] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

主　　题：Gust alleviation Intelligent control Feedforward control Neural networks Time-varying aircraft 

摘      要：This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight.A neural network identification model is first trained to accurately predict the aircraft’s ***,based on the output of the identification model and the collected flight data,the parameters of the time-delay neural network controller are obtained through a learning *** simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust *** example,when the aircraft is 40000 kg and the flight speed is 0.81Ma,the controller achieves a 67.82%reduction in wingtip acceleration and a 35.90%reduction in center of mass acceleration under continuous turbulence *** considering the measurement uncertainties,such as noise existing in the collected data,the trained controller can still achieve an acceptable gust alleviation ***,considering a flight in which the aircraft mass is constantly changing,the intelligent controller,which continuously learns from new flight data,maintains a good gust alleviation effect throughout the flight.