摘要
舰载机的最终着舰过程受到强烈的舰尾流的干扰,为了抑制舰尾流扰动,本文提出一种基于径向基函数神经网络(radial basis functions neural network,RBFNN)结合线性自抗扰控制(liner active disturbance rejection control,LADRC)的创新设计方法,确保舰载机直接升力控制自动着舰系统的鲁棒性。LADRC将阵风扰动和系统不确定项作为总扰动,通过线性扩展状态观测器(liner extended state observer,LESO)对总扰动进行估计,并通过线性反馈控制律进行补偿,然后根据系统状态利用RBFNN在线调整LADRC控制器的参数,并构建Lyapunov函数以证明闭环系统的稳定性。舰载机跟踪理想下滑道的仿真结果表明,RBF-LADRC的抗干扰性、鲁棒性和跟踪精度均优于与之对比的控制方法。
The final landing process of an carrier-based aircraft is disturbed by strong carrier air-wake.In order to suppress the disturbance of carrier air-wake,an innovative design method based on radial basis functions neural network(RBFNN)combined with linear active disturbance rejection control(LADRC)is proposed to assure the robustness of direct lift automatic landing system of carrier-based aircraft.The LADRC takes the gust wind disturbance and internal uncertainty as the total disturbance,estimates the total disturbance by a linear extended state observer(LESO),and conducts compensation by a linear feedback control law,then the parameters of the LADRC controller are adjusted on line by using RBFNN according to the system state,Lyapunov functions are constructed to demonstrate the stability of the closed-loop system.Based on the simulation results of the carrier-based aircraft tracking the ideal slide path,it is shown that the anti-interference,robustness and tracking accuracy of RBF-LADRC are superior to other comparative control methods.
作者
吴启龙
朱齐丹
WU Qilong;ZHU Qidan(College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《智能系统学报》
CSCD
北大核心
2024年第1期142-152,共11页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(52171299)。
关键词
稳定性分析
直接升力控制
径向基函数神经网络
线性自抗扰控制
自动着舰
姿态控制
舰尾流
轨迹跟踪
stability analysis
direct lift control
radial basis function neural network
linear active disturbance rejection control
automatic carrier landing
attitude control
carrier air-wake
trajectory tracking
