基于终端滑模和神经网络的多目标姿态跟踪鲁棒控制  

Robust Attitude Tracking Control of Multi Spacecraft Based on RBF Neural Network

作　　者：袁长清[1] 李政广 于海莉[1] 左晨熠 YUAN Changqing;LI Zhengguang;YU Haili;ZUO Chenyi(Air Force Aviation University,Changchun 130022,China)

机构地区：[1]空军航空大学

出　　处：《空间控制技术与应用》2019年第3期39-43,52,共6页Aerospace Control and Application

基　　金：国家自然科学基金资助项目(11372353,10902125)~~

摘　　要：研究了航天器编队飞行多目标姿态跟踪的鲁棒控制问题.主航天器由中心刚体和一个快速机动天线组成,星载相机跟踪某一特定目标,同时天线与从航天器保持通信.在考虑模型不确定性和外部干扰情况下,基于非奇异终端滑模技术和RBF神经网络,设计了多目标姿态跟踪鲁棒控制器.鲁棒控制器由RBF神经网络和一个自适应控制器组成.自适应控制器用于抵消神经网络的逼近误差和实现期望的控制性能.RBF神经网络用于逼近模型不确定部分与外部干扰力矩,并且根据非奇异终端滑模的有限时间收敛属性,提出了一种RBF网络的在线学习算法,提高了RBF网络的逼近效率.应用Lyapunov稳定性理论,证明了闭环系统稳定性.数值仿真结果表明所设计的控制器对外部干扰与模型不确定具有良好的鲁棒性.A robust control scheme is proposed to perform attitude tracking of multi-body spacecraft with mobile antenna. The mass parameter uncertainties and external disturbances are considered in this paper. The control scheme consists of radial basis function neural network (RBFN) and an adaptive controller. Utilizing the finite time convergent property of non-singular terminal sliding mode,an online learning algorithm based on Non-Singular Terminal Sliding Mode (NTSM) for updating all the parameters of the RBFN is derived,which ensures the RBFN has fast approximation under the parameter uncertainties and external disturbances. Based on NTSM,an adaptive controller is designed,which can compensate the RBFN approximation error and realize the anticipative stability and performance properties. By the Lyapunov stability theory,the closed loop system stability can be achieved. Simulation results demonstrate the good tracking characteristics of the proposed control scheme during spacecraft inertia changes and in presence of external disturbances.

关 键 词：动力学与控制 多体航天器 姿态跟踪 非奇异终端滑模 RBF神经网络 

分 类 号：V44[航空宇航科学与技术—飞行器设计]