Sang Hong, Zhao Jun
IEEE Trans Neural Netw Learn Syst. 2019 Dec;30(12):3722-3734. doi: 10.1109/TNNLS.2019.2896162. Epub 2019 Feb 22.
By using an intermittent control approach, this paper is concerned with the exponential synchronization and L -gain analysis for a class of delayed master-slave chaotic neural networks subject to actuator saturation. Based on a switching strategy, the synchronization error system is modeled as a switched synchronization error system consisting of two subsystems, and each subsystem of the switched system satisfies a dwell time constraint due to the characteristics of intermittent control. A piecewise Lyapunov-Krasovskii functional depending on the control rate and control period is then introduced, under which sufficient conditions for the exponential stability of the constructed switched synchronization error system are developed. In addition, the influence of the exogenous perturbations on synchronization performance is constrained at a prescribed level. In the meantime, the intermittent linear state feedback controller can be derived by solving a set of linear matrix inequalities. More incisively, the proposed method is also proved to be valid in the case of aperiodically intermittent control. Finally, two simulation examples are employed to demonstrate the effectiveness and potential of the obtained results.
通过采用间歇控制方法，本文研究了一类受执行器饱和影响的时滞主从混沌神经网络的指数同步和L增益分析。基于一种切换策略，同步误差系统被建模为一个由两个子系统组成的切换同步误差系统，由于间歇控制的特性，切换系统的每个子系统都满足驻留时间约束。然后引入一个依赖于控制率和控制周期的分段Lyapunov-Krasovskii泛函，在此泛函下，建立了所构造的切换同步误差系统指数稳定的充分条件。此外，将外部扰动对同步性能的影响限制在规定水平。同时，通过求解一组线性矩阵不等式可以得到间歇线性状态反馈控制器。更确切地说，所提方法在非周期间歇控制情况下也被证明是有效的。最后，通过两个仿真例子验证了所得结果的有效性和潜力。
