导弹非线性自适应鲁棒控制系统设计

提出了一种基于全调节RBF神经网络的导弹非线性自适应鲁棒控制系统的设计方法，应用全调节RBF神经网络在线辨识系统中存在的不确定性，利用反演和鲁棒控制技术设计了导弹控制系统，成功地处理了非匹配不确定性，并在虚拟控制中引入了微分阻尼项，有效地改善了系统动态性能。最后，应用Lyapunov稳定性理论推导出RBF神经网络各参数的调节律，并证明了系统状态全局渐近收敛于原点的一个邻域，仿真结果验证了该设计方法的有效性和可行性。

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.