IEEE Trans Neural Netw Learn Syst. 2013 Mar;24(3):410-21. doi: 10.1109/TNNLS.2012.2236356.
This paper studies the problem of sampled-data control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems with time delays. It is assumed that the sampling periods are arbitrarily varying but bounded. In order to take full advantage of the available information about the actual sampling pattern, a novel Lyapunov functional is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Based on the Lyapunov functional, an exponential synchronization criterion is derived by analyzing the corresponding synchronization error systems. The desired sampled-data controller is designed by a linear matrix inequality approach. The effectiveness and reduced conservatism of the developed results are demonstrated by the numerical simulations of Chua's circuit and neural network.
本文研究了由具有时滞的相同混沌 Lur'e 系统组成的主从同步方案的抽样数据控制问题。假设采样周期是任意变化但有界的。为了充分利用实际采样模式的可用信息,提出了一种新的李雅普诺夫泛函,该泛函在采样时刻是正定的,但在采样间隔内不一定是正定的。基于李雅普诺夫泛函,通过分析相应的同步误差系统,导出了指数同步判据。通过 Chua 电路和神经网络的数值仿真,验证了所提出的结果的有效性和降低的保守性。