A study of cognitive computing in nanosatellite constellations for synergic autonomy in CisLunar space

Rashed, Mohammed Irfan; Bang, Hyochoong

doi:10.1016/j.asr.2023.05.039

Cited by 1 publication

(1 citation statement)

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“…It thereby secures the successful execution of remote sensing missions using CubeSats. (2) We introduce a temporal expansion geomagnetic vector calculation algorithm. The algorithm decouples the observation equations in three axes through control segmentation in the time domain to address the singularity issue of the Kalman coefficient matrix, and enables the real-time acquisition of magnetic field vectors with low computational consumption.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Fault-Tolerant Attitude Control with Dynamic Sensing for Remote Sensing CubeSats

Fan,

Cui,

Chen

et al. 2023

Remote Sensing

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CubeSats have extensive applications in the realm of remote sensing. However, due to size constraints, attitude determination and control systems (ADCS) often adopt an integrated and densely packed structure. This leads to simple interference in the magnetic field measurements from components like reaction wheels and magnetorquers, resulting in magnetometer failures. Furthermore, the magnetic interference compromises the ability to dump the angular momentum of CubeSats. This leads to attitude control and remote sensing mission failures. To address these challenges, we introduce a dynamics-sensing, magnetic, fault-tolerant attitude control method that achieves attitude control without a priori magnetic field information generated from a magnetometer, restoring the remote sensing capabilities of CubeSats under magnetic failure. The proposed geomagnetic field sensing method, based on the temporal expansion geomagnetic vector calculate algorithm (GVCA), decouples the observation equations in three axes through control segmentation in the time domain to address the singularity issue of the Kalman coefficient matrix. Additionally, the magnetic field vector can be obtained in real time with acceptable computational consumption. Moreover, utilizing this real-time magnetic field information, the fault-tolerant control strategies and multi-mode control laws can progressively restore destabilized CubeSats to their regular states without performance degradation. We conducted numerical simulation experiments to evaluate the effectiveness of our method and system. Beyond the numerical simulations, we also built hardware systems. We designed and implemented a plug-and-play (PnP) ADCS to apply our methodology, further supporting CubeSats’ high-precision remote sensing. Furthermore, with the aid of a space environment simulation platform, we verified the performance of our system and method under conditions simulating the actual space environment. Ground testing demonstrated that our proposed method was able to identify the magnetic field and achieve high-performance attitude control with magnetic field measurement failures. The system’s pointing accuracy was better than 0.02 degrees, and the attitude stability surpassed 0.003 degrees per second.

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Section: Introductionmentioning

confidence: 99%