Adaptive RBF Neural Network-based Attitude Control Design for Boost Phase of Launch Vehicle

This paper deals with the adaptive attitude control problem during the boost phase of a launch vehicle. A three-dimensional nonlinear attitude dynamics model for boost phase is firstly formulated. Then, an RBFNN based adaptive attitude control law is proposed following the backstepping procedure, where an adaptive RBFNN law is designed to approximate complex external disturbances and model uncertainties, and meanwhile the saturation issue in view of the swing angle amplitude constraint of the engines is taken into account. Furthermore, the closed-loop stability is analyzed in the Lyapunov framework. Finally, a numerical simulations verifies the effectiveness of the proposed adaptive RBFNN based attitude control law.