Hybrid-optimization scheme for low-thrust trajectory design to the lunar gateway

Ravada, Pallavi

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https://hdl.handle.net/2142/122185 Copy

Description

Title

Hybrid-optimization scheme for low-thrust trajectory design to the lunar gateway

Author(s)

Ravada, Pallavi

Issue Date

2023-12-08

Director of Research (if dissertation) or Advisor (if thesis)

Woollands, Robyn

Department of Study

Aerospace Engineering

Discipline

Aerospace Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• low-thrust
• trajectory optimization
• particle swarm optimization
• optimal control
• minimum-time
• minimum-fuel
• high-fidelity
• Artemis
• sustainability
• NASA
• invariant manifolds
• Lunar Gateway
• indirect optimization
• single shooting
• multiple shooting

Abstract

With the return to the Moon and NASA’s Artemis program, low-thrust trajectory missions are of substantial interest for resupply to the Lunar Gateway. Gateway, when launched in 2024, will reside in a near rectilinear halo orbit (NRHO) of the L2 southern family. This operational orbit marries the benefits of a low lunar orbit (LLO), low perilune radius good for surface access, and a distant retrograde orbit (DRO), fuel efficiency, to provide an advantageous staging orbit balancing a variety of potential mission objectives. Transportation of humans and supplies to Gateway will be integral to the success of the Artemis program and solar electric propulsion will play a pivotal role in supply chain sustainability for these Cislunar operations. To maximize payload deliveries for future resupply missions, low-thrust time and fuel optimal trajectories will need to be computed for large resupply spacecraft. Coupled with invariant manifolds, gravitationally determined pathways invariant under the dynamics of the surrounding system, travel to the NRHO can be facilitated without additional thrust expenditure. In this thesis a Hybrid-Optimization scheme is introduced for transferring spacecraft from Earth orbit to the NRHO, including a terminal manifold coast, for the minimum-time problem. This methodology wraps a piece-wise trajectory generation algorithm within a particle swarm optimizer, addressing the challenges inherent to designing high-fidelity low-thrust time-optimal transfers due to initial guess sensitivity of indirect optimization methods and low spacecraft thrust-to-mass ratios. The simulations employ a high-fidelity dynamical model which includes third body perturbations of the Sun and Moon, J2 spherical harmonics, and JPL provided planetary ephemeris to determine real time position of celestial bodies. The optimal control problem is then solved using indirect methods (single shooting and multiple shooting methods), adjoining costate variables to state dynamics, and the Hamiltonian minimized using Pontryagin’s minimum principle and Primer Vector Theory. The spacecraft being modeled utilizes one NEXT-C ion engine with a maximum thrust of 0.235 N and a mass of 500 kg. With a starting geosynchronous equatorial or geostationary transfer orbit a suitable manifold injection point is targeted, and the interior optimal control problem is solved within the PSO wrapper function. Particle swarm optimization (PSO) has demonstrated viability as a suitable optimization method for iterating on the low-thrust transfer problem to produce solutions with significant time-of-flight (TOF) reductions without the need for human in-the-loop adjustments. Over multiple reiterations PSO is able to determine the optimal order of intermediate way-points between the Earth departure orbit and manifold injection point for the piece-wise algorithm to target to minimize the trajectory time span. The proposed Hybrid-Optimization scheme will be especially useful for rapidly designing and iterating on transfer trajectories to Gateway to support preliminary mission design.

Graduation Semester

2023-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/122185

Copyright and License Information

Copyright 2023 Pallavi Ravada

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