Multivariable adaptive satellite attitude controller design using RBF neural network

In this paper a new control strategy for adaptive attitude control of multivariable satellite system has been presented. The approach is based on radial basis function neural network (RBFNN). By using four reaction wheels and modified Rodrigues parameters (MRPs) for attitude representation, the attitude dynamic of satellite has been considered. The Lyapunov stability theory has been used to achieve a stable closed loop system. Also to enhance the robustness of the controller, the RBF neural network has been employed to estimate the model base terms in control law. The control objective is the plant to track a reference model. Simulation results illustrate the performance of the on-line trained neural network based adaptation algorithm.