Abstract In this paper, we investigate the dual-channel event-triggered deployment control for the space tethered system with intermittent output. Two different dynamic event-triggered mechanisms are designed to implement the event-based state sampling and the event-based input sampling, in which the data transmission frequencies are reduced at the dual channel, namely sensor-to-controller and controller-to-actuator. Then, the neural network (NN) state observer based on the intermittent output is designed to estimate the unmeasurable state under external disturbance, and the observer-based sliding mode controller is designed. Furthermore, because of the non-periodic sampling of the state signal and the output signal, the closed-loop system is proved via the analysis of the hybrid system, and the Zeno behavior is avoidance under these two event-triggered conditions. Finally, the simulation tests are implemented to verify the effectiveness of the proposed scheme.

    Highlights A control scheme incorporating a dual-channel event-triggered mechanism is investigated. A neural network-based state observer on intermittent output is designed. Two dynamic event-triggered mechanisms are designed to reduce the event-triggered numbers at two channels.


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    Title :

    Neural learning-based dual channel event-triggered deployment control of space tethered system with intermittent output


    Contributors:

    Published in:

    Acta Astronautica ; 213 ; 537-546


    Publication date :

    2023-09-20


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English