摘要
The advanced missile uses blended control of nero-fin and reaction-jet to improve missile maneuverability. The blended control design, which is multi-inputs and multi-outputs （MIMO）, severe nonlinear, and model uncertain, is much more complex than conventional nero-fin control. A novel nonlinear backstepping control approach is proposed to design the blended autopilot. Missile model is reformed to a new one by state reconstruction technique so that it is easy to be handled by the backstepping method. Then a Lyapunov function is chosen to avoid oscillation caused in normal backstepping way when control parameters are mismatched. In distribution of both inputs, optimal energy logic is proposed. In addition, a fuzzy cerebellar model articulation controller （FCMAC） neural network is used to guarantee controller robustness to uncertainties. Finally, simulation results demonstrate the efficiency and advantages of the proposed method.
The advanced missile uses blended control of nero-fin and reaction-jet to improve missile maneuverability. The blended control design, which is multi-inputs and multi-outputs （MIMO）, severe nonlinear, and model uncertain, is much more complex than conventional nero-fin control. A novel nonlinear backstepping control approach is proposed to design the blended autopilot. Missile model is reformed to a new one by state reconstruction technique so that it is easy to be handled by the backstepping method. Then a Lyapunov function is chosen to avoid oscillation caused in normal backstepping way when control parameters are mismatched. In distribution of both inputs, optimal energy logic is proposed. In addition, a fuzzy cerebellar model articulation controller （FCMAC） neural network is used to guarantee controller robustness to uncertainties. Finally, simulation results demonstrate the efficiency and advantages of the proposed method.
基金
the China Aviation Science Foundation (03D12004)
