Neural Network Based Optimal Control: Resilience to Missed Thrust Events for Long Duration Transfers

A growing number of spacecraft are continuing to adopt new and more efficient forms of in-space propulsion. One shared characteristic of these high-efficiency propulsion techniques is their limited thrust capabilities. This requires them to thrust continuously for long periods of time, making the spacecraft susceptible to potential missed thrust events. This work demonstrates how neural networks can autonomously correct for missed thrust events during a low-thrust long-duration transfer trajectory. The research applies and tests the developed method to au- tonomously correct a Mars return trajectory. Additionally, methods for improving the response of neural networks to missed thrust events are presented and further investigated.