Genetic Fuzzy System-based Control for Final Approach of Spacecraft Rendezvous and Proximity Operations

2026-08-12

AI Summary

This study addresses the control challenges encountered during the final approach phase of spacecraft rendezvous and docking for on-orbit servicing. To this end, an intelligent controller integrating genetic algorithms with a fuzzy inference system is proposed. The approach optimizes fuzzy rules through offline training, achieving strong generalization across diverse initial relative positions while maintaining low energy consumption. Simulation results based on an orbital dynamics model demonstrate that the designed controller reliably accomplishes the proximity operations with high efficiency and stability under varying initial conditions. Moreover, it exhibits remarkable robustness and energy-saving performance even in perturbed environments not included in the training scenarios.

Abstract

In-space servicing has been receiving great attention to extend the operation of spacecraft with defective components. This requires rendezvous and proximity operations for a chaser to provide service to a target. This work constructs a fuzzy inference system-based controller for the chaser to reach the cooperative target on a circular orbit in the final approach phase while minimizing the energy consumption of the chaser. The offline training process performed by a genetic algorithm deals with multiple initial relative positions of the chaser, and the trained controller is validated using a testing environment with disturbances, which differs from the training scenarios.

Problem

• spacecraft rendezvous
• proximity operations
• final approach
• energy consumption
• control

Innovation

• Genetic Fuzzy System
• Spacecraft Rendezvous
• Proximity Operations
• Energy-efficient Control
• Robust Controller