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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2019, Vol. 40 ›› Issue (7): 322827-322827.doi: 10.7527/S1000-6893.2018.22827

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

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)

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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