Abstract The paper presents a nonlinear dynamic model for DGMSCMGs, sometimes used as actuators to control the attitude of large satellites. The developed models describe the translation dynamics, the rotation dynamics of the Active Magnetic Bearing Rotor, as well as the rotation dynamics of the two mobile gimbals. Two control architectures are initially designed by using the dynamic inversion concept, proportional-integrator-derivative/proportional-derivative dynamic compensators, linear observers, and a neural network to compensate the effect of the dynamic inversion error. One also develops a similar adaptive control architecture consisting of a proportional-integrator dynamic compensator, a feed-forward neural network, and a linear observer. The latter system models two interconnected nonlinear servo-systems and controls the angular rates of the two mobile gimbals actuated by the attitude controller of the satellite. The validation of the novel control architectures is achieved in Matlab/Simulink, the obtained results proving a very good angular rate precision and the robustness of the control systems in relation to the external disturbances.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive control of DGMSCMG using dynamic inversion and neural networks


    Contributors:

    Published in:

    Advances in Space Research ; 68 , 8 ; 3478-3494


    Publication date :

    2021-06-09


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Adaptive Flight Control Using Dynamic Inversion and Neural Networks

    Schumacher, C. / AIAA | British Library Conference Proceedings | 1999



    Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

    Lakshmikanth, Geethalakshmi S. / Padhi, Radhakant / Watkins, John M. et al. | AIAA | 2014


    Large maneuverable flight control using neural networks dynamic inversion [5253-161]

    Yang, E. / Gao, J. / SPIE - The International Society for Optical Engineering | British Library Conference Proceedings | 2003