低轨无拖曳卫星的自适应神经网络控制器设计

低轨无拖曳(Drag-free)卫星为相对论的验证、引力波探测以及地球重力场的测量提供了低干扰的试验环境。目前已有的工作主要对无拖曳卫星模型进行线性化,然后进行控制器设计,此种方法忽略了无拖曳卫星控制系统的非线性环节,因此降低了控制器的精度。本文将基于Lyapunov稳定性理论和自适应反步控制,直接针对无拖曳卫星控制系统的非线性模型进行分析,设计一种自适应神经网络控制器。针对系统建模过程中的线性化和未建模动态,利用RBF神经网络对非线性项进行拟合和补偿,建立自适应神经网络权值自适应律,保证闭环系统具有较好的鲁棒稳定性能和抗干扰性能,实现无拖曳卫星控制系统的设计要求。仿真结果表明控制器的有效性,满足了无拖曳卫星的控制精度要求。

Design of Adaptive Neural Network Controllers for LEO Drag-free Satellite

Low disturbance environment can be achieved by the LEO（Low Earth Orbit） drag-free satellite,which bene fits the validation of relativity,detection of gravitational waves and measurement of gravity field.For drag free control put pose,most researches proposed controllers with linearized model and ignoring the nonlinear characteristics,which lower the accuracy of controllers.In this paper,by taking into account of the nonlinear characteristics,an adaptive neural network controller is established based on Lyapunov methods and adaptive backstepping control theory.For nonlinear characteristics and unmodeled dynamics,RBF neural network is employed for approximation.At the same time,we introduce the update laws of adaptive neural network weights,which guarantee the stability of the closed-loop system and satisfy requirements of the drag free satellite control system.The simulation results indicate that the controller is effective and the accuracy of the drag free satellite can be satisfied.