一种基于RBF神经网络增益调节的三维鲁棒导引律设计  

A three dimensional robust guidance law design based on RBF neural network gain adjustment

作　　者：陈勃[1,2] 杨开红[1] 季海波[1] 

机构地区：[1]中国科学技术大学信息科学技术学院自动化系,安徽合肥230027 [2]海军蚌埠士官学校一系,安徽蚌埠233012

出　　处：《中国科学技术大学学报》2015年第4期280-285,共6页JUSTC

基　　金：国家自然科学基金(61273090)资助

摘　　要：针对导弹-目标相对运动三维非线性模型,采用满足输入-状态稳定性理论的非线性导弹导引律,利用径向基函数(radial basis function,RBF)神经网络动态调节自主学习能力,得到一种能根据视线角速度变化情况动态调节非线性导引律增益的控制律,可以避免因增益固定而目标机动性大引起脱靶量增大的情况.该导引律在目标做多种机动时也能对其进行跟踪和有效拦截.仿真结果表明,该控制律具有良好的自适应能力且便于实现.By adopting the three dimensional nonlinear model tor the relative motion ot mlssl^es aIlCl tet^get~, a scheme of guidance law was presented. The theoretical basis of the guidance law includes input-to-state stability （ISS） as well as the dynamic adjustment and self-study ability of the radial basis function （RBF） neural network. The control law is capable of dynamically adjusting the gain of nonlinear guidance law with the angular rate change of LOS （line of sight）. The guidance law can avoid the undershoot augment caused by gain fixation and large-scale target-maneuvering, and also effectively trace as well as intercept the target making a variety of maneuvers. The numerical simulation results demonstrate the adaptivity and easy implementation of the control law.

关 键 词：导引律 径向基函数神经网络(RBFNN) 输入-状态稳定性 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]