Trajectory linearization control of an aerospace vehicle based on RBF neural network

An enhanced trajectory linearization control (TLC) structure based on radial basis function neural network (RBFNN) and its application on an aerospace vehicle (ASV) flight control system are presensted. The influence of unknown disturbances and uncertainties is reduced by RBFNN thanks to its approaching ability, and a robustifying item is used to overcome the approximate error of RBFNN. The parameters adaptive adjusting laws are designed on the Lyapunov theory. The uniform ultimate boundedness of all signals of the composite closed-loop system is proved based on Lyapunov theory. Finally, the flight control system of an ASV is designed based on the proposed method. Simulation results demonstrate the effectiveness and robustness of the designed approach.