不确定时滞混沌系统的自适应动态神经网络控制

研究了一类具有不确定时滞的非自治混沌系统的控制问题. 通过结合Lyapunov-Krasovskii函数和Lyapunov函数设计参数可调的不确定时滞补偿器，使得反馈控制输入信号不受时延的影响；同时引入动态结构自适应神经网络，以消除系统的不确定性，其隐层神经元的个数可以随着逼近误差的增大而自适应增加，改善了逼近速度与网络复杂度的关系；最后，用Duffing混沌系统的控制仿真示例表明该方法的有效性. 关键词： 混沌系统 自适应控制 不确定时滞 动态结构神经网络

Adaptive control of uncertain chaotic systems with time delays using dynamic structure neural network

A kind of control methods is surveyed to deal with a class of nonlinear systems with uncertain time delay. By combining the Lyapunov-Krasovskii function and Lyapunov function, the time-delay compensator with adjustable parameters is presented to make the control input independent of the multiple time delays. At the same time, the dynamic structure adaptive neural network is introduced to eliminate the uncertainties in the chaotic system, which approximates the function with better relationship between the calculation rate and structure complexity by increasing hidden units when the tracking error is beyond the allowable bound. The demonstration with a given Duffing chaotic system shows the presented control method is effective.