Low-Thrust Gravity Assist Trajectory Optimisation using Evolutionary Neurocontrol

Low-Thrust Gravity Assist Trajectory Optimisation using Evolutionary Neurocontrol

Kranen, Tommy (TU Delft Aerospace Engineering)

Cowan, K.J. (mentor)
Dachwald, Bernd (mentor)
Ohndorf, Andreas (mentor)

Delft University of Technology

Aerospace Engineering

2019-07-18

With all major bodies within the Solar System explored by at least a single fly-by, modern-day missions are becoming increasingly more demanding, up to a point where classical chemical propulsion can no longer supply the required ∆V. Increasingly more is relied upon low-thrust propulsion, characterised by its (very) low thrust force; long continuous thrust arcs, often lasting months at a time; and high specific impulse. To even further increase the possible ∆V budget, thereby allowing more intricate missions, more payload, or a lower transfer time; use is made of low-thrust propulsion combined with gravity assists.

With traditional low-thrust gravity assist optimisation tools heavily relying on astrodynamics and optimal control theory expertise, often requiring an initial guess and heavy modification for each new mission scenario; there is a need for a smart low-thrust gravity assist trajectory optimisation tool. Such a tool should be independent from an initial guess, optimise a trajectory from only a broad description of the mission, and be applicable to a wide variety of mission scenarios.

It was the goal of this thesis to develop such a smart low-thrust gravity assist trajectory optimisation tool, which was found in extending the global low-thrust optimisation software package InTrance. InTrance tackles the problem from the novel perspective of artificial intelligence and machine learning using a method termed evolutionary neurocontrol (ENC), which combines biologically inspired artificial neural networks (ANNs) with evolutionary algorithms (EAs). The internal parameters of the ANN and initial conditions of each phase are optimised by the EA, which then serves as an agent; supplying the spacecraft with a steering strategy at each integration step.

A novel tool capable of optimising the low-thrust gravity assist problem has been developed and has been shown to find more optimal results than available reference trajectories in select cases. Two prominent low-thrust gravity assist missions have been re-optimised: a double-asteroid rendezvous mission with an intermediate Mars gravity assist similar to the Dawn mission, and a low-thrust version of the New Horizons mission to Pluto with an intermediate Jupiter gravity assist. InTrance found a low-thrust New Horizons like trajectory which used 41% less propellant for a similar flight time as the actual mission, even though having a higher drymass and being launched with a lower C3. The results of the Dawn re-optimisation showed excellent agreement with the actual Dawn mission in terms of flight and dwell times, and used significantly less propellant. The efficiency increase due to the inclusion of the gravity assists has furthermore been investigated. The gravity assist at Jupiter in the New Horizons trajectory resulted in a decrease of 3.5% in flight time and over 75% in propellant saving. Dawn’s mission could be flown without a gravity assist w.r.t flight time and propellant usage, however, the inclusion resulted in an increase of 20% in dwell time at Vesta.

Low-thrust
trajectory optimisation
Reinforcement Learning
neurocontrol
neuroevolution
gravity assist

http://resolver.tudelft.nl/uuid:39da41df-8f5f-488f-ba57-33757e760487

Student theses

master thesis

© 2019 Tommy Kranen