Modelling and fault tolerant control for near space vehicles with vertical tail loss

This work focuses on modelling and fault tolerant algorithm design of a damaged near space vehicle. A novel radial basis function (RBF) neural network backstepping fault-tolerant control methodology is developed for attitude control system with damage, which can make system stable and accurately track the desired signals in the presence of vertical tail loss. First, the changing aerodynamic parameters caused by vertical tail loss are used to model damaged attitude dynamics. Second, in view of the established damaged model, a nominal backstepping controller is designed. Then a RBF neural network technology is designed to update the related parameters, which can compensate effects caused by damage and make the system still stable and track the desired signals. Finally, simulation results are provided to show the effectiveness of the proposed RBF neural network backstepping fault-tolerant control scheme.