Optimal Control and Neural Porkchop Analysis for Low-Thrust Asteroid Rendezvous Mission

TL;DR

This study compares optimal control methods and neural network estimators for asteroid rendezvous mission design, demonstrating neural networks' efficiency and accuracy in preliminary analysis while highlighting limitations with complex constraints.

Contribution

It introduces a neural network framework for estimating transfer feasibility and fuel consumption, enhancing early-stage mission planning for low-thrust asteroid rendezvous.

Findings

Neural networks achieve low relative errors in simplified scenarios.

Neural networks provide smooth, globally consistent predictions.

Path constraints cause discrepancies, limiting detailed design accuracy.

Abstract

This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers an effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications.