Minimal Delta-v Autonomous Spacecraft Inspection Using Genetic Fuzzy-Driven Control

Abstract In-space servicing missions for extending the mission life of spacecraft have received significant attention, and autonomous spacecraft inspection plays a critical supporting role. While optimal control and learning-based approaches have been studied, difficulties in real-time implementation and a lack of interpretability make them hard to be widely used. To address these challenges, this study explores an energy-efficient control strategy for a deputy spacecraft to inspect a chief spacecraft using a fuzzy inference system combined with a bio-inspired optimization technique to incorporate learning capability into the control process. Through multiple training scenarios with various starting positions, the optimized fuzzy controller produces a minimal fuel-consuming force while maintaining reliable inspection within constraints, such as illumination, restricted field of view, thrust limits, and safe regions. The performance of the proposed control strategy is validated through Monte Carlo simulations.