Abstract
The presented work investigates how to improve the accuracy of tracking a tumbling satellite for flying space robot system with the problem of time delay and unknown inertial frame. It is the time delay existing in the controller that results in manipulator swinging. Besides, in space, it is impossible to find an accurate inertial frame and initial conditions. For this, the dynamic predictive model is established, but it is difficult to be calculated owing to the coupling and non-linearity. The Long Short-Term Memory Network is introduced to solve the problem and the tracking model is fitted by training neurons. A novel dynamic model predictive control based on the Long Short-Term Memory Network is proposed. To overcome measurement noise, the Bayesian Filter is employed and strengthens the stability of the method. The proposed control method is verified by the semi-physical simulation experiment of tracking a tumbling satellite. It shows that the control method improves the low accuracy resulting from time delay and the seesaw phenomenon.
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Liu, Q., Jin, M., Wang, B., Liu, H. (2021). LSTM Based Model Predictive Control for Flying Space Robot to Track Uncooperative Target. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13016. Springer, Cham. https://doi.org/10.1007/978-3-030-89092-6_39
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DOI: https://doi.org/10.1007/978-3-030-89092-6_39
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