This paper addresses the recursive filtering problem for shift-varying linear repetitive processes (LRPs) with limited network resources. To reduce the resource occupancy, a novel event-triggered strategy is proposed where the concern is to broadcast those necessary measurements to update the innovation information only when certain events appear. The primary goal of this paper is to design a recursive filter rendering that, under the event-triggered communication mechanism, an upper bound (UB) on the filtering error variance is ensured and then optimized by properly determining the filter gains. As a distinct kind of 2-D systems, the LRPs are cast into a general Fornasini–Marchesini model by using the lifting technique. A new definition of the triggering-shift sequence is introduced and an event-triggered rule is then constructed for the transformed system. With the aid of mathematical induction, the filtering error variance is guaranteed to have a UB which is subsequently optimized with appropriate filter parameters via solving two series of Riccati-like difference equations. Theoretical analysis further reveals the monotonicity of the filtering performance with regard to the event-triggering threshold. Finally, an illustrative simulation is given to show the feasibility of the designed filtering scheme.

中文翻译：

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事件触发的递归滤波，用于移位可变线性重复过程

本文针对网络资源有限的时变线性重复过程（LRP），解决了递归过滤问题。为了减少资源占用，提出了一种新颖的事件触发策略，其中的关注点是仅当某些事件出现时，广播那些必要的度量以更新创新信息。本文的主要目的是设计一种递归滤波器渲染，在事件触发的通信机制下，确保滤波误差方差的上限（UB），然后通过适当确定滤波器增益来对其进行优化。作为一种独特的二维系统，LRP通过使用提升技术被铸造为普通的Fornasini–Marchesini模型。引入了触发-移位序列的新定义，然后为转换后的系统构造了事件触发规则。借助数学归纳法，可以确保滤波误差方差具有UB，随后通过求解两个类似Riccati的差分方程系列，使用适当的滤波器参数对其进行优化。理论分析进一步揭示了关于事件触发阈值的滤波性能的单调性。最后，给出了一个说明性的仿真，以表明所设计的过滤方案的可行性。理论分析进一步揭示了关于事件触发阈值的滤波性能的单调性。最后，给出了一个说明性的仿真，以表明所设计的过滤方案的可行性。理论分析进一步揭示了关于事件触发阈值的滤波性能的单调性。最后，给出了一个说明性的仿真，以表明所设计的过滤方案的可行性。