Modeling and Controller Designing on the Large Attitude Adjusting Phase of Reusable Boosted Vehicle

According to the characteristics of Reusable Boost Vehicle (RBV) during the large attitude adjusting phase, like strong nonlinearity, strong coupled and multi input-output, the nonlinear modeling is established (with the nonlinearity of aerodynamic coefficients varies with factors fully considered, such as Mach number, attack angle, sideslip angle and so on). A RBV flying control strategy based on neural network robust adaptive inverse is proposed. First, RBV system is completely decoupled using nonlinear dynamic inverse; secondly, the effect of model uncertainty is eliminated using robust adaptive method; finally, the feasibility and validity of the control strategy during the large attitude adjusting phase are verified with nonlinear simulation.