Tao Jie, Xu Muxi, Chen Depeng, Xiao Zehui, Rao Hongxia, Xu Yong
IEEE Trans Cybern. 2023 Dec;53(12):7834-7843. doi: 10.1109/TCYB.2022.3227446. Epub 2023 Nov 29.
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.
本文研究了马尔可夫跳跃系统的事件触发弹性滤波问题。采用隐马尔可夫模型来刻画滤波器与系统之间的异步约束。本文中弹性滤波器的增益不确定性为区间型，相比于范数有界型，它在对不确定现象建模时更为精确。通过分离不确定区间的顶点，可显著减少线性矩阵不等式约束的数量，从而降低计算难度和计算时间。此外，应用事件触发方案来降低网络资源消耗。为了在减少带宽消耗与提高系统性能之间找到平衡，在事件触发方案中将阈值参数设计为对角矩阵。利用凸优化方法，推导了保证滤波误差系统随机稳定并满足扩展耗散性能的充分条件。最后，给出了一个单连杆机器人手臂系统来验证所提方法的有效性和优势。
