Sandeep Singh

Sandeep Singh
Rensselaer Polytechnic Institute | RPI · Department of Mechanical, Aerospace and Nuclear Engineering

Doctor of Philosophy
Trajectory Optimization, Low-thrust transfers in High-Fidelity dynamical systems, Machine Learning & Regression methods.

About

17
Publications
4,817
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60
Citations
Introduction
Professor (Assistant) at Rensselaer Polytechnic Institute. Current research interests include optimal control of flexible structures, low thrust spacecraft trajectories, asteroid mining missions and sensor-fusion based autonomous guidance and navigation.
Additional affiliations
June 2020 - August 2020
NASA
Position
  • Fellow
June 2019 - August 2019
NASA
Position
  • Fellow
January 2018 - March 2020
Texas A&M University
Position
  • Research Assistant
Education
December 2017 - July 2022
Texas A&M University
Field of study
  • Astrodynamics, Aerospace Engineering
August 2009 - August 2013
Indian Institute of Space Science and Technology
Field of study
  • Aerospace Engineering

Publications

Publications (17)
Article
Full-text available
Structural optimization of a 1.2 m diameter space mirror is carried out using OptiStruct tool of HyperWorks 12 to minimize mass and constrain optical aberrations. Zerodur is used as the material for the mirror. A typical open-back mirror of the same diameter and material weighs around 90 kg. For optimization, constraints were imposed on defocus, co...
Conference Paper
Full-text available
The presence of extremely low-altitude, lunar quasi-frozen orbits (QFOs) has given rise to interesting mission opportunities. These QFOs are ideal for close-range, high-resolution mapping of the lunar south pole, and their inherent stability translates into minimal station-keeping efforts. Despite the aforementioned desirable characteristics, desig...
Preprint
Full-text available
Designing long-duration lunar orbiter missions is challenging due to the Moon’s highly nonlinear gravity field and the third-body perturbations induced by the Earth, Sun and other large bodies. The absence of a Lunar atmosphere has offered the possibility for mission designers to search for extremely low-altitude, quasi-stable lunar orbits. In additi...
Article
Full-text available
In this work, end-to-end low-thrust transfers from a GTO orbit to a low-altitude lunar orbit by exploiting the manifolds of a chosen Earth-Moon L1 halo orbit was studied. The practicality of piece-wise, minimum-time transfers that exploit halo orbit manifolds is demonstrated, which offers more flexibility to meet mission objectives. It is known tha...
Article
Full-text available
A novel methodology is proposed for designing low-thrust trajectories to quasi-periodic, near rectilinear Halo orbits that leverages ephemeris-driven, "invariant manifold analogues" as long-duration asymptotic terminal coast arcs. The proposed methodology generates end-to-end, eclipse-conscious, fuel-optimal transfers in an ephemeris model using an...
Article
In this study, a supervised machine learning approach called Gaussian process regression (GPR) was applied to approximate optimal bi-impulse rendezvous maneuvers in the cis-lunar space. We demonstrate the use of the GPR approximation of the optimal bi-impulse transfer to patch points associated with various invariant manifolds in the cis-lunar spac...
Article
Full-text available
In this paper, we investigate the manifolds of three Near-Rectilinear Halo Orbits (NRHOs) and optimal low-thrust transfer trajectories using a high-fidelity dynamical model. Time- and fuel-optimal low-thrust transfers to (and from) these NRHOs are generated leveraging their ‘invariant’ manifolds, which serve as long terminal coast arcs. Analyses ar...
Preprint
Full-text available
A supervised machine learning approach called the Gaussian Process Regression (GPR) is applied to approximate the optimal bi-impulse rendezvous maneuvers in cis-lunar space. The use of GPR approximation of the optimal bi-impulse transfer to patch-points associated with various invariant manifolds in the cis-lunar space is demonstrated. The proposed...
Conference Paper
Full-text available
A novel indirect-based trajectory optimization framework is proposed that leverages ephemeris-driven, "invariant manifold analogues" as long-duration asymptotic terminal coast arcs while incorporating eclipses and perturbations during the optimization process in an ephemeris model; a feature lacking in state of the art software like MYSTIC and Cope...
Conference Paper
Full-text available
Near-Rectilinear Halo Orbits (NRHOs) are deemed to be favorable candidates for establishing a near-future crewed space station in the cis-lunar space. Although the 9:2 resonant southern $L_2$ NRHO has been earmarked as the working orbit for the Lunar Gateway Mission, a plethora of other neighboring resonant NRHOs are also viable options. The invari...
Conference Paper
Full-text available
Lyapunov methods are well established as a versatile approach for generating feasible and robustly converging spiral-type low-thrust trajectories. The present study introduces Lya-punov optimal methods for low-thrust guidance. The approach makes use of the regularized modified equinoctial orbit elements in such a way that a nominal trajectory can b...
Conference Paper
Full-text available
A renewed interest in revisiting the Moon has blown wide open the previously ajar door to research avenues in the field of Earth-Moon transfer trajectories. While the advent of low-thrust propulsion systems has opened up possibilities to undertake more complicated missions, designing optimal transfer trajectories in this domain is no easy feat. His...
Conference Paper
Full-text available
Design of long-duration lunar orbiter missions is challenging due to the Moon's highly non-linear gravity field and third-body perturbations induced by the Earth, Sun and other large bodies, on the orbiting spacecraft. The absence of a Lunar atmosphere, and hence the lack of orbital atmospheric drag, has encouraged mission designers to search for e...
Conference Paper
Full-text available
The problem of time-optimal, rest-to-rest slewing of a flexible spacecraft through a large angle is studied. These maneuvers are known to have bang-bang control profiles, which lead to undesirable excitation of higher frequency vibrations. It is common to approximate the dynamics using the assumed modes method; and formulate and solve the resulting...
Article
Full-text available
Optical payloads comprise optical elements like lens or mirrors which are fragile and most susceptible to failure. It is paramount that the design enables the payload to give best performance in terms of optical parameters and survivability is the rudimentary design constraint. Simulation is carried out based on the load levels on a vibration shake...
Article
Full-text available
A Tuned Mass Damper (TMD) is a device consisting of a mass, a spring and a damper which is attached to a structure in order to reduce dynamic response of a structure. In this work, optimisation of TMD parameters, i.e. it’s mass, stiffness and damping coefficient, is carried out for a single degree of freedom TMD. Both ground as well as external for...

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Projects

Projects (5)
Project
In this project, we take a general approach, studying and developing algorithms to invent a rapid 3D digital twin renderer which combines data from different sensors to generate both data and data-based inference rich cloud models. Eventually, the concept is envisioned to be used for applications like Rapid Prototyping, Quality Assurance, Remote Sensing and Medical Research.
Project
Explore and demonstrate the effectiveness of using stochastic supervised learning methods based on rapid database production comprising of an extremal field map which satisfy necessary optimality conditions to perform autonomous post-launch mission re-planning and guidance of spacecraft.
Project
The goal is to explore modern regression techniques providing statistical inference and apply them to various problems of interest across different fields. We want to begin with understanding the Gaussian Process Regression (GPR) as a powerful ML tool and work our way to more advanced topics.