Genetic algorithms for fuzzy control of automatic docking with a space station

The spacecraft guidance and control community is engaged in the task of achieving continuous improvements in accuracy. There are two ways to realize this: the improvement in the design and development of sensors and actuators, or the development of new control strategies based on new ideas and principles. The autonomous assembly of two spacecraft in orbit requires intelligent control systems for soft and precise docking operations. We propose the study of a fuzzy logic guidance and navigation system, mounted in a chaser vehicle. This fuzzy controller is a knowledge-based controller that performs the closed-loop operations autonomously. It produces smooth control actions in the proximity of the target and during the docking to avoid disturbance torque in the final assembly orbit. We study the use of genetic algorithms to perform the optimization of the fuzzy controller by finding the best fuzzy sets of the membership functions, and to optimize the docking time and the fuel consumption