Research on micro-vibration suppression algorithm based on RBF neural network

Yuanyuan Wang; Chen Wang; Junfeng Han; Hua Tian

doi:10.1117/12.2648243

14 October 2022 Research on micro-vibration suppression algorithm based on RBF neural network

Yuanyuan Wang, Chen Wang, Junfeng Han, Hua Tian

Proceedings Volume 12343, 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022); 123432Y (2022) https://doi.org/10.1117/12.2648243
Event: 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022), 2022, Qingdao, China

Abstract

In the inter-satellite laser communication, a reliable communication link requires the boresight tracking accuracy between the optical transceivers to be sub-micro radians, and the control error caused by the vibration of the satellite platform is the most important factor affecting the optical communication link. The vibration and the effect of the adopted suppression scheme largely determine the tracking accuracy of the PAT system. Therefore, the impact of reducing the vibration on the optical communication link is a prerequisite for establishing stable satellite optical communication links. This paper uses the active compensation of the satellite platform vibration as the research direction, and proposes a composite control micro-vibration suppression algorithm based on the RBF neural network. On the basis of traditional PID control, adaptive RBF neural network feedforward control and PID control combination constitute composite control, design and establishing satellite platform vibration suppression system, simulation verifies the performance of traditional PID control and RBF composite control algorithm, result indicates that the RBF composite control algorithm has increased by 80% compared to traditional PID control.

Citation Download Citation

Yuanyuan Wang, Chen Wang, Junfeng Han, and Hua Tian "Research on micro-vibration suppression algorithm based on RBF neural network", Proc. SPIE 12343, 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022), 123432Y (14 October 2022); https://doi.org/10.1117/12.2648243

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