Design of sliding mode controller based on radial basis function neural network for spacecraft autonomous proximity

Jianfang Jia, Yongjun Wang, Hong Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Since the dynamic model of spacecraft has the characteristics of non-linear, kinematic couplings, uncertainties and nonstationary disturbance, it has become a challenging problem to accurately control the relative position and attitude of the spacecraft. A radial basis function neural network (RBFNN)-based sliding mode controller (SMC) is proposed for trajectory tracking of spacecraft autonomous proximity in this paper. Firstly, a six degree-of-freedom (DOF) relative motion dynamics model is developed for close proximity operations. The modified Rodrigues parameters are applied to solve the problem of singularity. Then, a SMC that does not require accurate model information is designed. RBFNN is used to adaptively eliminated the model uncertainty impacts on the system. Finally, the stability of the relative motion dynamics is proved via Lyapunov stability theory. Simulation results illustrate that the method can attenuate the attitude and position errors, reduce the chattering of the input and decrease the overshoot of the control torque effectively.
Original languageEnglish
Title of host publication22nd IFAC World Congress
Subtitle of host publicationProceedings
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Place of PublicationLaxenburg
Number of pages6
Publication statusPublished - 22 Nov 2023
EventIFAC World Congress 2023: The 22nd World Congress of the International Federation of Automatic Control - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22

Publication series

PublisherInternational Federation of Automatic Control (IFAC)
ISSN (Print)2405-8963


ConferenceIFAC World Congress 2023
Internet address


  • spacecraft
  • relative motion
  • sliding mode control
  • neural network
  • Lyapunov stability


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