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This paper develops a fuzzy inference system-based controller for spacecraft rendezvous and proximity operations, specifically targeting the final approach phase to a cooperative target in circular orbit. Utilizing a genetic algorithm for offline training, the controller is optimized to minimize energy consumption while accommodating various initial relative positions of the chaser. Validation in a testing environment with disturbances demonstrates the controller's robustness and effectiveness in real-world scenarios.
Energy-efficient spacecraft rendezvous is now achievable, even in the presence of unexpected disturbances, thanks to a novel fuzzy inference controller trained with genetic algorithms.
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.
