航空学报 > 2019, Vol. 40 Issue (7): 322827-322827   doi: 10.7527/S1000-6893.2018.22827

混合小推力航天器轨道保持高性能滑模控制

陈弈澄1,2, 齐瑞云1,2, 张嘉芮1,2, 王焕杰3,4   

  1. 1. 南京航空航天大学 自动化学院, 南京 211100;
    2. 南京航空航天大学 先进飞行器导航、控制与健康管理工业和信息化部重点实验室, 南京 211100;
    3. 上海航天控制技术研究所, 上海 201109;
    4. 上海市空间智能控制技术重点实验室, 上海 201109
  • 收稿日期:2018-12-03 修回日期:2019-01-14 出版日期:2019-07-15 发布日期:2019-07-24
  • 通讯作者: 齐瑞云 E-mail:ruiyun.qi@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金(61873127);航空科学基金(2017ZA52013);江苏省"六大人才高峰"高层次人才资助项目(HKHT-010)

High-performance sliding mode control for orbit keeping of spacecraft using hybrid low-thrust propulsion

CHEN Yicheng1,2, QI Ruiyun1,2, ZHANG Jiarui1,2, WANG Huanjie3,4   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
    2. Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced Aerocraft, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
    3. Shanghai Aerospace Control Technology Institute, Shanghai 201109, China;
    4. Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai 201109, China
  • Received:2018-12-03 Revised:2019-01-14 Online:2019-07-15 Published:2019-07-24
  • Supported by:
    National Natural Science Foundation of China (61873127); Aeronautical Science Foundation of China(2017ZA52013); "Six Talents Peaks" High-level Talents Funding Project in Jiangsu Province of China (HKHT-010)

摘要: 针对采用太阳帆、太阳电混合小推力推进的航天器,研究了其在日心悬浮轨道的保持控制问题。为解决已有控制方法中未综合考虑内部未建模动态和外部未知扰动的问题,以及进一步提高系统控制性能,设计了一种高性能滑模控制策略。首先,考虑模型不确定性,建立了混合小推力航天器在日心悬浮轨道柱面坐标系的动力学方程;其次,基于改进型条件积分滑模面和径向基(RBF)神经网络设计了控制律,结合自适应方法在线估计不确定参数;接着,将求取的虚拟控制量在推进剂最优条件下转换成实际控制量,即太阳帆姿态角和太阳电推进力;最后,数值仿真验证了上述设计方法提高了系统鲁棒性,减小了轨道位置超调,并且混合推进相比于单一太阳帆推进,在更短收敛时间内控制精度提高了4个数量级,相比于单一太阳电推进,一年可以节省约89.6%的推进剂。

关键词: 混合小推力, 太阳帆, 日心悬浮轨道, 条件积分滑模面, 径向基(RBF)神经网络, 自适应控制

Abstract: For a spacecraft using hybrid solar sail and solar electric propulsion, the station-keeping control of the heliocentric displaced orbit is investigated. To solve the problem that internal unmodeled dynamics and external unknown disturbances are not considered comprehensively in the existing methods, and to further improve the performance of the system, a high-performance sliding mode control strategy is designed. Firstly, considering the uncertainty of the model, the dynamic equation of the hybrid low-thrust spacecraft keeping on heliocentric displaced orbit is established in the cylindrical coordinate system. Secondly, the control law is designed based on the improved conditional integral sliding surface and Radial Basis Function (RBF) neural network, and the uncertain parameters are estimated online by combining the adaptive method. Then, under the optimum condition of propellant, the virtual control variables are converted into actual control variables, namely attitude angles of solar sail and solar electric propulsion. Finally, numerical simulation verifies that the above design enhances the robustness of the system, reduces the overshoot of orbit position, and hybrid propulsion improves the control accuracy by 4 orders of magnitude in shorter convergence time compared to single solar sail propulsion, while it can save about 89.6% propellants a year compared to single solar electric propulsion.

Key words: hybrid low-thrust propulsion, solar sails, heliocentric displaced orbit, conditional integral sliding surface, Radial Basis Function (RBF) neural network, adaptive control

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