Real-Time Optimal Guidance and Control for Interplanetary Transfers Using Deep Networks

TL;DR

This paper introduces a deep learning-based guidance system for interplanetary transfers, achieving near-optimal propellant use and real-time onboard implementation by training neural networks with a novel data generation method.

Contribution

It presents a new methodology for generating optimal transfer data and demonstrates neural networks can accurately replicate optimal guidance and control for space missions.

Findings

Neural networks reach target conditions with only 0.2% more propellant than optimal.

Propellant mass can be predicted within 1% error using value function learning.

The approach enables real-time onboard guidance for interplanetary spacecraft.

Abstract

We consider the Earth-Venus mass-optimal interplanetary transfer of a low-thrust spacecraft and show how the optimal guidance can be represented by deep networks in a large portion of the state space and to a high degree of accuracy. Imitation (supervised) learning of optimal examples is used as a network training paradigm. The resulting models are suitable for an on-board, real-time, implementation of the optimal guidance and control system of the spacecraft and are called G&CNETs. A new general methodology called Backward Generation of Optimal Examples is introduced and shown to be able to efficiently create all the optimal state action pairs necessary to train G&CNETs without solving optimal control problems. With respect to previous works, we are able to produce datasets containing a few orders of magnitude more optimal trajectories and obtain network performances compatible with real missions requirements. Several schemes able to train representations of either the optimal policy (thrust profile) or the value function (optimal mass) are proposed and tested. We find that both policy learning and value function learning successfully and accurately learn the optimal thrust and that a spacecraft employing the learned thrust is able to reach the target conditions orbit spending only 2 permil more propellant than in the corresponding mathematically optimal transfer. Moreover, the optimal propellant mass can be predicted (in case of value function learning) within an error well within 1%. All G&CNETs produced are tested during simulations of interplanetary transfers with respect to their ability to reach the target conditions optimally starting from nominal and off-nominal conditions.