A fuzzy-neural bang-bang controller for satellite attitude control

This paper presents an alternative bang-bang solution for a satellite attitude control problem. The controller used is composed of a fuzzy logic controller and a neural network. In this case, the fuzzy logic controller serves as the main bang-bang controller while the neural network acts as a support unit to the main controller. The previous study has shown that the appropriate setting of each membership function of the fuzzy logic controller depends on the initial attitude of the satellite. This limits the possibility of a real-time implementation since generally the initial attitude is not known in advance. In order to overcome this difficulty, in this paper various values of the initial attitude are used as samples for determining the corresponding fuzzy configurations. The neural network is then taught to generalize the relationship between the fuzzy controller configuration and the value of the initial attitude. After the training, the neural network is used for providing the configuration of the fuzzy controller in real-time operations. The results indicate that the time required to drive the system to the final state when the proposed hybrid controller is used is less than that when the well-known linearized bang-bang control law is implemented.