The Attitude Control Algorithm of Agile Optical Satellite Oriented to Nonparallel-Ground-Track-Imaging

Zhao, Lin; Zhao, Kun; Hao, Yong; Zhang, Xinyu; He, Yushan

doi:10.1109/access.2019.2951170

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…To further benefit the agile characteristics of EOSs, the super-AEOS with real-time attitude control system has been investigated recently [101,102,103]. As illustrated in Figure 6, the developed attitude control system allows super-AEOS to execute the following three types of observation tasks with real-time attitude adjustment: nonparallelground-track, active pushbroom and nonlinear trajectory tasks.…”

Section: Impact Of Super-aeosmentioning

confidence: 99%

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Wang,

Wu,

Xing

et al. 2020

Preprint

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Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

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Section: Impact Of Super-aeosmentioning

confidence: 99%

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Wang,

Wu,

Xing

et al. 2020

Preprint

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“…The attitude profile of the target-pointing segment of the mission is determined by the user’s observation requirements. Typically, the target-pointing attitude is designed to have low angular rates to minimize drift during observations [ 2 , 5 ]. Observation mission requirements, such as the imaging area and location, are used as input conditions to generate the attitude profile of the target-pointing segment.…”

Section: Introductionmentioning

confidence: 99%

“…Research on satellite maneuver optimization has been conducted since the 1970s [ 3 , 4 , 5 ]. The satellite’s actuator performance limitations, transmission capacity in orbit, and throughput should be considered for the optimal maneuvering of LEO satellites.…”

Section: Introductionmentioning

confidence: 99%

Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks

Yun

2023

Sensors

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To perform Earth observations, low-Earth orbit (LEO) satellites require attitude maneuvers, which can be classified into two types: maintenance of a target-pointing attitude and maneuvering between target-pointing attitudes. The former depends on the observation target, while the latter has nonlinear characteristics and must consider various conditions. Therefore, generating an optimal reference attitude profile is difficult. Mission performance and satellite antenna position-to-ground communication are also determined by the maneuver profile between the target-pointing attitudes. Generating a reference maneuver profile with small errors before target pointing can enhance the quality of the observation images and increase the maximum possible number of missions and accuracy of ground contact. Therefore, herein we proposed a technique for optimizing the maneuver profile between target-pointing attitudes based on data-based learning. We used a deep neural network based on bidirectional long short-term memory to model the quaternion profiles of LEO satellites. This model was used to predict the maneuvers between target-pointing attitudes. After predicting the attitude profile, it was differentiated to obtain the time and angular acceleration profiles. The optimal maneuver reference profile was obtained by Bayesian-based optimization. To verify the performance of the proposed technique, the results of maneuvers in the 2–68° range were analyzed.

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Attitude Optimization Control Method of Agile Optical Small Satellite for Nonparallel Ground Track Imaging

Fan

Song

et al. 2022

Int. J. Control Autom. Syst.

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The Attitude Control Algorithm of Agile Optical Satellite Oriented to Nonparallel-Ground-Track-Imaging

Cited by 6 publications

References 29 publications

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks

Attitude Optimization Control Method of Agile Optical Small Satellite for Nonparallel Ground Track Imaging

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