Neural network-based Predefined-time attitude control for rigid spacecraft with uncertainties and disturbances

This paper presents a novel predefined-time sliding mode control method for rigid spacecraft that takes into account modelling uncertainties and external disturbances. Utilizing a specially designed predefined-time sliding function and the radial basis function neural network, we introduce an adaptive neural predefined-time controller aimed at ensuring the convergence of both attitude and velocity errors of the rigid spacecraft to a small neiborhood of zero within a predefined time, which can overcome the effect of the modeling uncerrtainties and external disturbances. Finally, numerical simulations are conducted to validate the effectiveness of the proposed control method.