Attitude tracking control for spacecraft with robust adaptive RBFNN augmenting sliding mode control

This paper investigates the attitude tracking problem for a rigid spacecraft subject to uncertain inertial parameters and external disturbances. A robust adaptive radial basis function neural network (RBFNN) augmenting sliding mode control strategy is developed. The classical sliding mode control serves as the main control framework, which is augmented by a robust adaptive RBFNN to approximate uncertain dynamics consisting of the inertial parameters and external disturbances. The robust adaptive RBFNN approximation combines a conventional RBFNN and a robust adaption, where the conventional RBFNN dominates in the neural active region, the robust adaptive control takes effect in the robust active region, and a smooth switching function is utilized to achieve a smooth transition between two regions. With this adaptive structure, the full-envelop attitude tracking can be realized. This systematic methodology is demonstrated to be able to accomplish the ultimately convergent attitude tracking of the spacecraft. Simulations verify and highlight the theoretical results.