摘要
研究部分充液航天器存在系统参数不确定、外部未知扰动、液体燃料大幅晃动的姿态机动控制问题。首先,将大幅晃动的液体燃料等效为运动脉动球模型,建立刚-液耦合航天器动力学方程;其次,针对充液航天器姿态稳定问题,提出一种具有固定时间收敛的分数阶非奇异快速终端滑模算法;同时,设计扰动观测器来估计系统的综合扰动,并且利用径向基函数神经网络来估计液体大幅晃动引起的扰动;设计Lyapunov函数分析并证明系统固定时间稳定性;最后,通过对比的仿真结果验证本文提出控制策略的可行性和有效性。
The attitude maneuver control problem of liquid-filled spacecraft with system parameter uncertainty,external disturbance and large-amplitude liquid sloshing are studied.Firstly,the liquid fuel with large-amplitude liquid sloshing is equivalent to a moving pulsating ball model,and the dynamic equation of the spacecraft with large liquid sloshing is established.A fractional-order nonsingular fast terminal sliding mode algorithm with fixed time convergence is proposed for the attitude stability of liquid-filled spacecraft.Additionally,a perturbation observer is designed to estimate the combined perturbations including parameter uncertainties and external perturbations,and the perturbations caused by large-amplitude liquid sloshing are estimated using a radial basis function neural network.The Lyapunov function is designed to analyze and prove the fixed-time stability of the system.Finally,the feasibility and effectiveness of the control strategy proposed in this paper are verified by the comparative simulation results.
作者
宋晓娟
范志文
吕书锋
岳宝增
SONG Xiaojuan;FAN Zhiwen;LYU Shufeng;YUE Baozeng(College of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Inner Mongolia Key Laboratory of Special Service Intelligent Robotics,Hohhot 010051,China;College of science,Inner Mongolia University of Technology,Hohhot 010051,China;School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2024年第7期1146-1154,共9页
Journal of Astronautics
基金
国家自然科学基金项目(12362004,11962020,12172182,12132002)
内蒙古自治区高等学校创新团队发展计划支持(NMGIRT2213)
内蒙古自治区高等学校青年科技人才项目(NJYT23029,NJYT23067)
内蒙古自治区直属高校基本科研业务费项目(JY20240066,JY20220086,JY20220046)。
关键词
充液航天器
液体晃动
滑模控制
扰动观测器
神经网络
Liquid-filled spacecraft
Liquid sloshing
Sliding mode control
Disturbance observer
Neural network