Wireless structural control using stochastic bandwidth allocation and dynamic state estimation with measurement fusion
Copyright © 2017 John Wiley & Sons, Ltd. 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.
Structural Control and Health Monitoring
Andrew Swartz, R.
Wireless structural control using stochastic bandwidth allocation and dynamic state estimation with measurement fusion.
Structural Control and Health Monitoring,
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