Sliding mode control based on fuzzy neural network for variable structure spacecraft

Variable structure spacecraft is an important development direction of astronautics. The mass distribution of the spacecraft will change significantly during the configuration variation, and this will generate new problems of dynamic modeling and controller design. To solve these problems, the hybrid coordinate method and Lagrange equation were used to build the dynamic model of the spacecraft, and changing rule of the dynamical parameters was approximated by several typical working conditions. Sliding mode controller was designed to control the attitude during the variation of the spacecraft. To improve the adjustment of the controller, fuzzy neural network(FNN) was designed to adjust the parameters of the controller adaptively. The radial -based function(RBF) neural network was designed to approximate dynamic model, and thus the relationship between the control torque and attitude variation was obtained, which was used to optimize the FNN. The attitude of the variable structure spacecraft during the structure variation with no control, sliding mode control and fuzzy neural network was acquired in simulation. The results verify the effectiveness of the fuzzy neural network adaptive sliding mode controller, and comparisons were made to verify the good properties of the proposed controller.