Zhu Wei, Wang Dandan, Liu Lu, Feng Gang
IEEE Trans Neural Netw Learn Syst. 2018 Aug;29(8):3599-3609. doi: 10.1109/TNNLS.2017.2731865. Epub 2017 Aug 18.
This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.
本文通过基于事件的脉冲控制（EIC）方法研究连续时间动态系统（CDS）的指数镇定问题，其中脉冲时刻由特定的状态依赖触发条件确定。分别针对非线性和线性CDS推导了基于EIC的全局指数稳定性准则。还证明了所关注的闭环控制系统不存在芝诺行为。此外，将所提出的基于事件的脉冲控制方案应用于主从忆阻神经网络的同步问题。进一步地，开发了一种自触发脉冲控制方案以避免主系统和从系统之间的持续通信。最后，给出两个数值仿真例子来说明所提出的基于事件的脉冲控制器的有效性。
