This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

中文翻译：

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具有模式相关平均停留时间的开关神经网络的事件触发滑模控制

本文涉及一类具有模式相关平均停留时间 (MDADT) 的连续时间切换神经网络的滑模控制问题。所考虑的连续时间切换神经网络由包含非线性项和可变切换信号的生物神经网络驱动。引入了 MDADT 的概念，其中每个子系统在切换到另一个子系统之前都有自己的停留时间。此外，基于观察者误差和系统模式的事件触发机制设计了一种新颖的滑模控制器，其触发条件比现有的触发条件更加保守和实用。推导出足够的条件以确保闭环系统在线性矩阵不等式方面是随机指数稳定的。设计的滑模控制器可以促进系统状态的滑模运动。最后，提供了一个说明性示例来证明所提出方法的有效性和优点。