Regularized Integration of Gravity-Perturbed Trajectories-A Numerical Efficiency Study

Kraige, L. G.; Junkins, John L.; Ziems, L.D.

doi:10.2514/3.62255

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The value of the inclination is set to range in the interval [0, 1] radians. The time of flight does not exceed one orbital period T, which is approximately calculated using the following formula (8) where a is the semi-major axis, a = ( ra + rp ) / 2.…”

Section: B Performance Analysis Of Different Sample Formsmentioning

confidence: 99%

“…Fast solver for J2-perturbed Lambert problem using deep neural network 3 Shooting methods transcribe the perturbed Lambert problem into the search for the initial velocity vector that provides the desired terminal conditions at a given time. Kraige et al [8] investigated the efficiency of different shooting approaches and found that a straightforward differential correction algorithm combined with the Rectangular Encke's motion predictor is more efficient than the analytical KS approach. Junkins and Schaub [9] transformed the problem into a two-point boundary value problem and applied Newton iteration method to solve it.…”

Section: Iintroductionmentioning

confidence: 99%

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Fast Solver for J2-Perturbed Lambert Problem Using Deep Neural Network

Yang¹,

Li²,

Feng³

et al. 2022

Journal of Guidance, Control, and Dynamics

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This paper presents a novel and fast solver for the J2-perturbed Lambert problem. The solver consists of an intelligent initial guess generator combined with a differential correction procedure. The intelligent initial guess generator is a deep neural network that is trained to correct the initial velocity vector coming from the solution of the unperturbed Lambert problem. The differential correction module takes the initial guess and uses a forward shooting procedure to further update the initial velocity and exactly meet the terminal conditions. Eight sample forms are analyzed and compared to find the optimum form to train the neural network on the J2-perturbed Lambert problem. The accuracy and performance of this novel approach will be demonstrated on a representative test case: the solution of a multi-revolution J2-perturbed Lambert problem in the Jupiter system. We will compare the performance of the proposed approach against a classical standard shooting

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Section: B Performance Analysis Of Different Sample Formsmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

Fast Solver for J2-Perturbed Lambert Problem Using Deep Neural Network

Yang¹,

Li²,

Feng³

et al. 2022

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

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Encke's special perturbation technique associated with the KS regularized variables

Awad

1988

Earth Moon Planet

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In this paper of the series, a special perturbation technique of Encke-type associated with the KS regularized variables will be developed for satellite motions in the Earth's gravitational field with axial symmetry. Its computational algorithm is of recursive nature and could be applied for any perturbed conic motion whatever the number of the zonal harmonic coefficients may be. Applications of the algorithm are also included.

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Regularized Integration of Gravity-Perturbed Trajectories-A Numerical Efficiency Study

Cited by 2 publications

References 3 publications

Fast Solver for J2-Perturbed Lambert Problem Using Deep Neural Network

Fast Solver for J2-Perturbed Lambert Problem Using Deep Neural Network

Encke's special perturbation technique associated with the KS regularized variables

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