Optimal Slewing Maneuvers For Inertia Tensor Observability

2022 ◽  
Author(s):  
John C. Helmuth ◽  
Kyle J. DeMars
Keyword(s):  
Inertia Tensor

The Evaluation of Moments of Regions With Piecewise-Linearly Approximated Boundaries via Line Integration

1989 ◽  
Author(s):  
J. Angeles ◽  
M. J. Al-Daccak
Keyword(s):  
Euclidean Space ◽  
Three Dimensional ◽  
Inertia Tensor ◽  
Divergence Theorem ◽  
Dimensional Euclidean Space ◽  
Repeated Application ◽  
The Subject ◽  
First Moment

Abstract The subject of this paper is the computation of the first three moments of bounded regions imbedded in the three-dimensional Euclidean space. The method adopted here is based upon a repeated application of Gauss’s Divergence Theorem to reduce the computation of the said moments — volume, vector first moment and inertia tensor — to line integration. Explicit, readily implementable formulae are developed to evaluate the said moments for arbitrary solids, given their piecewise-linearly approximated boundary. An example is included that illustrates the applicability of the formulae.

Controlled inertia tensor of a transformable spacecraft

2021 ◽  
Author(s):  
R.P. Simonyants ◽  
N.A. Alekhin ◽  
V.A. Tarasov
Keyword(s):  
Attitude Control ◽  
Dynamic Properties ◽  
Operating Conditions ◽  
Inertia Tensor ◽  
Simplified Model ◽  
Diagonal Form ◽  
Transformation Mechanism ◽  
External Disturbances ◽  
Type Transformation ◽  
Gravitational Moment

A simplified model of a transformable spacecraft is considered, including a rod-type transformation mechanism with movable weights. The mechanism can be used to adapt the dynamic properties of the spacecraft to the environment or the operating conditions of on-board systems, for example, to counter the moments of external disturbances during attitude control and angular stabilization. By changing the position of the transformation mechanism, the spacecraft inertia tensor can be put in diagonal form, which makes it possible to exclude the force interconnections between the channels and to eliminate the constant component of the gravitational moment. For a simplified model of the transformation mechanism, we establish the analytical dependence of the components of the inertia tensor on the parameters determining the position of the transformation mechanism. It is shown that by adjusting the moving mass, which is 0.5% of the entire spacecraft mass, we obtain the spacecraft configuration that ensures the diagonality of the inertia tensor.

Role of the inertia tensor in haptically perceiving where an object is grasped.

1994 ◽  
Vol 20 (2) ◽  
pp. 276-285 ◽  
Author(s):  
Christopher C. Pagano ◽  
J. M. Kinsella-Shaw ◽  
Paul E. Cassidy ◽  
M. T. Turvey
Keyword(s):  
Inertia Tensor

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

2020 ◽  
Vol 106 ◽  
pp. 106189
Author(s):  
Weimeng Chu ◽  
Shunan Wu ◽  
Zhigang Wu ◽  
Yuefang Wang
Keyword(s):  
Deep Learning ◽  
Inertia Tensor ◽  
Least Square

scholarly journals Bra-Ket Representation of the Inertia Tensor

2020 ◽  
Vol 77 (11) ◽  
pp. 945-951
Author(s):  
U.-Rae Kim ◽  
Dohyun Kim ◽  
Jungil Lee
Keyword(s):  
Inertia Tensor

Deep learning-based inertia tensor identification of the combined spacecraft

2020 ◽  
Vol 234 (7) ◽  
pp. 1356-1366
Author(s):  
Weimeng Chu ◽  
Shunan Wu ◽  
Xiao He ◽  
Yufei Liu ◽  
Zhigang Wu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Angular Rate ◽  
Inertia Tensor ◽  
Measurement Noise ◽  
Identification Performance ◽  
Operating Environments

The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

Singular Cases of the Inertia Tensor

1967 ◽  
Vol 35 (3) ◽  
pp. 281-282
Author(s):  
James E. Howard
Keyword(s):  
Inertia Tensor
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