Least square based ensemble deep learning for inertia tensor identification of combined spacecraft

Chu, Weimeng; Wu, Shunan; Wu, Zhigang; Wang, Yuefang

doi:10.1016/j.ast.2020.106189

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…The main advantage of GPR is that it not only provides the estimated mean of the unknown function, but also its variance, which implies the regression accuracy, namely, the model confidence. To approximate the unknown function ∆, we consider ∆( x) as a GP which is trained based on the following data set consisting of N collected sampled measurements: (10) where the state-input pairs…”

Section: A Gaussian Process Regressionmentioning

confidence: 99%

“…Christidi-Loumpasefski et al in [7], [8] have proposed momentum-conservationbased methods to fully identify the parameters of freeflying system dynamics with unmeasurable sloshing states. In recent years, visual CCD cameras [9] and deep learning [10] have also been proposed for estimation of inertia parameters of the combined spacecraft. However, these methods are not applicable to scenarios where the target still has attitude maneuverability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes With Online Model Learning

Liu,

Wang,

Lee

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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Section: A Gaussian Process Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes With Online Model Learning

Liu,

Wang,

Lee

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…For this reason, many researchers used MLP neural networks to carry out the task. For instance, Chu et al [36] proposed a deep network MLP to estimate inertia parameters. The angular rates and control torques of combined spacecraft are set as the input of a deep neural network model, and conversely, the inertia tensor is then set as the output.…”

Section: System Identification Through Reconstruction Of Parametersmentioning

confidence: 99%

Deep Learning and Artificial Neural Networks for Spacecraft Dynamics, Navigation and Control

Silvestrini

Lavagna

2022

Drones

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The growing interest in Artificial Intelligence is pervading several domains of technology and robotics research. Only recently has the space community started to investigate deep learning methods and artificial neural networks for space systems. This paper aims at introducing the most relevant characteristics of these topics for spacecraft dynamics control, guidance and navigation. The most common artificial neural network architectures and the associated training methods are examined, trying to highlight the advantages and disadvantages of their employment for specific problems. In particular, the applications of artificial neural networks to system identification, control synthesis and optical navigation are reviewed and compared using quantitative and qualitative metrics. This overview presents the end-to-end deep learning frameworks for spacecraft guidance, navigation and control together with the hybrid methods in which the neural techniques are coupled with traditional algorithms to enhance their performance levels.

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“…It is a critical way to reduce the unbalanced excitation force and to improve the starting, stopping, acceleration, and deceleration performance to effectively control the spindle inertia of the aero-engine through reasonable assembly methods. 8–11…”

Section: Introductionmentioning

confidence: 99%

A novel assembly method of aero-engine rotors with minimized spindle inertia

Liu

Sun

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Assembly is an essential part of aero-engine manufacturing, and the assembly quality will affect the engine’s stability and service life. Spindle inertia is an essential factor affecting aero-engine dynamic performance. Assembly can affect the spindle inertia of the aircraft engine, which in turn affects the dynamic performance of the engine. This paper proposes a transfer model of spindle inertia based on machining error transmission, studying the variation law of spindle inertia of multi-stage rotors under the influence of assembly, providing an optimized assembly method with minimized spindle inertia. The influence law of machining errors on spindle inertia is analyzed through the simulation and probability density method. The assembly results of the designed rotors verify the effectiveness of the inertia transfer and assembly model. The results show that the spindle inertia of multi-stage rotors is affected by machining errors, and the spindle inertia can be adjusted by changing the assembly methods. This paper provides a new idea for the transmission and adjustment of spindle inertia of assembly rotors.

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Least square based ensemble deep learning for inertia tensor identification of combined spacecraft

Cited by 13 publications

References 18 publications

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes With Online Model Learning

Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes With Online Model Learning

Deep Learning and Artificial Neural Networks for Spacecraft Dynamics, Navigation and Control

A novel assembly method of aero-engine rotors with minimized spindle inertia

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