时变线性分布参数系统的鲁棒指数稳定性分析

研究了一类不确定性分布参数系统的鲁棒指数稳定性和稳定化问题.利用推广到Hilbert 空间的Lyapunov-Krasovskii方法和不等式技巧,证明了线性时滞系统的鲁棒指数稳定性,并且依赖时滞的鲁棒指数稳定性和稳定化的充分条件可以表示成线性算子不等式(LOI)形式,其中决策变量是Hilbert空间的算子.把得到的结果应用到一个抛物型方程,这些条件归结为线性矩阵不等式(LMI).最后,一个数值例子说明了稳定性分析的有效性.     

Global exponential stability of reaction diffusion neural networks with discrete and distributed time-varying delays

This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays.By constructing a more general type of Lyapunov-Krasovskii functional combined with a free-weighting matrix approach and analysis techniques,delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities.The obtained results are dependent on the size of the time-varying delays and the measure of the space,which are usually less conservative than delay-independent and space-independent ones.These results are easy to check,and improve upon the existing stability results.Some remarks are given to show the advantages of the obtained results over the previous results.A numerical example has been presented to show the usefulness of the derived linear matrix inequality(LMI)-based stability conditions.