Space shuttle attitude control by reinforcement learning and fuzzy logic

The authors discuss the results of applying two fuzzy reinforcement learning architectures to the difficult control problem of space shuttle attitude control. They demonstrate that it is possible to control the pitch, roll, and yaw of the space shuttle within a specified deadband by using fuzzy control rules and to adapt automatically to a reduced error tolerance. The performance of this controller is compared with a controller using conventional control theory and also a nonadaptive fuzzy controller. The results, using the orbital operations simulator system, demonstrate that more difficult tasks can be learned by the controller while the fuel efficiency remains very high