Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems
研究了马尔可夫跳变系统的事件触发弹性滤波问题，采用隐马尔可夫模型处理异步约束，用区间型增益不确定性替代范数有界型，通过分离不确定区间顶点减少线性矩阵不等式约束数量，并设计对角阈值参数平衡网络资源消耗与系统性能，最后用单连杆机器人臂系统验证方法有效性。
The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.