有输入未建模动态的导弹鲁棒控制器设计

Robust Controller Design for Missile System with Input Unmodeled Dynamics

在导弹系统俯仰通道中存在输入未建模动态情况下,提出了一种基于RBF神经网络和反演控制技术的非线性鲁棒控制器的设计方法。首先应用两个RBF神经网络对输入未建模动态设计了神经网络逆补偿器,然后利用反演控制技术设计了导弹非线性控制器,最后应用Lyapunov稳定性理论推导出RBF神经网络权重矢量调节律,证明了系统的所有信号均有界且全局指数收敛至原点。最后给出的BTT导弹非线性六自由度数字仿真结果显示了该设计方法的有效性。

Based on RBF neural networks and backstepping control techniques, a nonlinear robust controller design method is proposed for missile control systems with input unmodeled dynamics in pitch channel. The neural inverse compensator is designed using two RBF neural networks. Then the nonlinear controller is designed using backstepping control techniques. The 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 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.