Nonlinear Controller Design for Missile System with a General Set of Uncertainties

Based on RBF neural networks and backstepping control techniques, a nonlinear adaptive controller design method is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized into one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The control problem is resolved while the control coefficient matrix is unknown. At the same time, the rigorous conditions on the uncertainties, which exist in the literature at the present stage, are relaxed. The adaptive tuning rules of RBF neural network weight matrix are derived by the Lyapunov stability theorem. All signals of the closed-loop system are bounded and exponentially converge to the neighborhood of the origin globally. Finally, nonlinear six-degree-of-freedom (6-DOF) numerical simulation results for a bank-to-turn (BTT) missile model are presented to demonstrate the effectiveness of the proposed method.