Design of Nonlinear Adaptive Robust Control Systems for a Missile

A design method of nonlinear adaptive robust control systems for a missile is proposed based on fully tuned RBF neural networks. RBF neural networks are used to identify the uncertainty of the system, then nonlinear missile control systems are designed using backstepping and robust control techniques which deal with the mismatched uncertainty of the system successfully, the differential damp terms are introduced into the fictitious control terms that improve the transient performance of the system effectively. Finally, the tuning law for updating all the parameters of the RBF neural networks is derived by the Lyapunov stability theorem, and the states of the system converge to the neighborhood of the origin globally and asymptotically.The simulation results show the effectiveness and feasibility of the proposed method.