摘要
针对存在不确定性的BTT导弹系统,基于神经网络提出了一种鲁棒动态逆控制系统设计方法。首先应用双时标假设将BTT导弹动力学分离为快变状态动力学和慢变状态动力学。然后,在巧妙地利用导弹气动参数特性设计Lyapunov函数的基础上,对快变状态动力学和慢变状态动力学分别进行动态逆控制设计。设计中应用RBF神经网络来逼近系统中存在的不确定性,证明了闭环系统的所有信号均有界且指数收敛至系统原点的一个邻域。最后给出的BTT导弹非线性六自由度数字仿真结果验证了该算法的有效性。
Based on neural networks, a robust dynamic inversion control scheme is proposed for BTT missile control systems with uncertainties. Firstly, the dynamics of the bank-to-turn （BTT） missile are separated into two parts, the dynamics of fast state and the dynamics of slow state, using the two-timescale assumption. Then, the novel Lyapunov function is designed using the properties of aerodynamic coefficients. The dynamics of fast State and the dynamics of slow state are designed using dynamic inversion, The RBF neural network is adopted to identify the uncertainties of the system. 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 BTT missile model are presented to demonstrate the effectiveness of the proposed method.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2008年第2期327-330,共4页
Systems Engineering and Electronics
关键词
BTT导弹
反馈线性化
鲁棒控制
神经网络
BTT missile
feedback linearization
robust control
neural networks
作者简介
晋玉强（1977-），男，讲师，博士，主要研究方向为非线性控制理论。E-mail：jyq301@yahoo．com．cn
