Fully diagnosing the spatial properties of X-ray lasers

Yang, Jun; Sun, Jianwen; Wang, Tao; Fan, Dianyuan; Wang, Shiji

doi:10.1007/bf02872289

Cited by 1 publication

(1 citation statement)

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“…In this study, these delays are based on estimation‐error residuals and observability measures. Utilizing data fusion techniques in Wang et al, Zhu et al, and Kulkarni and Venayagamoorthy, measurement fusion and updates will be performed using incoming data from the neighborhood , but conversely, the proposed methods lack state transmission and state fusion, thus increasing speed. The main difference between the simplified approaches and those cited is the ability to rapidly reconfigure neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

Wireless structural control using stochastic bandwidth allocation and dynamic state estimation with measurement fusion

Winter

Swartz

2017

Struct Control Health Monit

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Summary Wireless sensor networks are becoming more popular for structural monitoring because of their low installation costs; in addition, coupling structural control with wireless data acquisition can also yield advantages. However, these systems have limited communication bandwidth, limiting their effectiveness as the number of devices in control networks grows large if centralized control approaches are used. Traditional methods for collocating data in wireless structural control network rely on time‐budgeted bandwidth or spatial decentralization, where the network is divided into smaller subnetworks. These methods are largely static and typically do not take into account any measure of data quality to prioritize transmissions. This study presents a dynamic approach for bandwidth allocation in wireless structural control networks that relies on an application‐specific, autonomous, and controller‐aware, carrier sense multiple access with collision detection protocol. Stochastic parameters are derived to strategically alter back‐off times in the carrier sense multiple access with collision detection algorithm based on nodal observability and output estimation error. Inspired by data fusion approaches, this paper presents 2 different methods for neighborhood state estimation using a dynamic form of measurement‐only fusion. Upon receiving data from the contended wireless medium, each wireless unit fuses incoming data using a precalculated static Kalman gain matrix for the corresponding dynamic neighborhood. Onboard, each wireless unit contains a library of Kalman gain matrices, to accommodate any possible set of communicated data. Both numerical simulations and small‐scale laboratory experimental results are presented.

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Section: Introductionmentioning

confidence: 99%