一类不确定时滞混沌系统的全局鲁棒自适应神经网络同步控制器设计

Global Robust Adaptive Neural Network Synchronization Controller Design for a Class of Time-delay Uncertain Systems

研究一类不确定时滞混沌系统的全局鲁棒自适应神经网络同步控制器设计，其系统中的不确定时滞项不是简单的线性有界条件，而是允许其存在高阶项，因此具有全局特性．在控制器的设计上；首先通过选取合适的径向基函数（RBF）神经网络的权向量去逼近时滞系统中的未知连续有界部分；然后在RBF神经网络输出的基础上，选用一个鲁棒自适应控制器来趋近时滞系统的不确定部分；同时，利用Lyapunov稳定性理论对混沌同步的条件给出了论证；最后，数据仿真的结果表明该方法的有效性．

In this paper,global robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The uncertainty time-delay items in the system meet not only the linear and bound conditions but also the condition of allowing the existence of loftily rank item. Hence the studied chaotic system has the global identity. The radical basis function （RBF）neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Strict theoretical proofs are given by using the method of Lyapunov function. Finally,numerical simulations show the effectiveness of the method.