An optimal backstepping design for blended aero and reaction-jet missile autopilot

A novel robust adaptive backstepping control approach is presented to design a pitch controller for missiles employing blended aerodynamic control and Reaction-jet Control System (RCS). The main features of this study are that a certain type of control Lyapunov function is obtained by function reconstruction to decrease the control-input fluctuation caused by mismatched controller parameters, optimal backstepping parameter set and Lyapunov function choice are gotten by Genetic Algorithm (GA). So both system stability and dynamic performance are guaranteed. Since the model of a missile with RCS is inaccurate with uncertain effect of RCS and aerodynamic parameters, a Fuzzy Cerebellar Model Articulation Controller (FCMAC) neural network is used to guarantee controller robustness, thus the bounds of model uncertainties are not needed. Finally, simulation results demonstrate the efficiency and advantages of the proposed method.