Development of an extensible dual-core wireless sensing node for cyber-physical systems

Kane, Michael B.; Zhu, Dapeng; Hirose, Mitsuhito; Dong, Xing; Winter, Benjamin; Häckell, Mortiz; Lynch, Jerome P.; Wang, Yang; Swartz, Andrew

doi:10.1117/12.2045325

Cited by 31 publications

(30 citation statements)

References 12 publications

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“…The 20‐DOF numerical simulation employs a full‐scale structural model and simulated wireless communication. Additionally, a simulation and experimental validation were performed on a 9‐DOF small‐scale structure equipped with Martlet wireless structural control units . A Martlet unit's processor can operate at user‐selected speeds between 10 and 80 MHz; it can store programs on‐board with a maximum size of 2 MB (nonvolatile memory) and 800 kB of volatile memory (e.g., data and soft‐coded parameters), and Martlet units can support a 16‐ or 32‐GB micro SD card for large data storage .…”

Section: Simulations and Laboratory Setupmentioning

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: Simulations and Laboratory Setupmentioning

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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“…In this study, validation of the real-time, adaptive control technique proposed for time varying, seismically excited structures is conducted using the dual-core Martlet wireless sensor node developed at the University of Michigan [4]. Martlet (Fig.…”

Section: A Martlet For Wireless Feedback Control Applicationsmentioning

confidence: 99%

“…J.P. Lynch is a Professor with the bottleneck. Recent work in wireless sensors has revealed the benefits of adopting computational cores with two or more pipelines [4]. Multi-core microcontrollers provide true parallel execution of embedded code resulting in major speedup in computational tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The two cores are required to execute in parallel a recursive system identification scheme that tracks structural properties along with recursive derivation of optimal control laws based on real-time plant properties. The dualcore wireless sensor Martlet developed at the University of Michigan [4] is adopted for feedback control of building structures exposed to strong ground motions. Martlet contains a 90 MHz floating point processor with a standard microcontroller core and a secondary control law accelerator (CLA) core.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive feedback control of time varying structures utilizing dual-core wireless sensing and actuation nodes

Hirose

Lynch

2015

2015 American Control Conference (ACC)

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In traditional system designs for feedback structural control systems, the structure is assumed to be linear time invariant (LTI). However, extreme load events on the structure can damage the structure leading to violation of the time invariance assumption. This study proposes an online adaptive control strategy that tracks the time varying characteristics of a structural system with the optimal control law recursively derived for the system. Continuous system identification executed concurrently with derivation of optimal control laws is possible using the low-power, dual-core Martlet wireless node developed at the University of Michigan. The wireless platform executes online system identification recursively on one processor pipeline while a control law based on linear quadratic regulation (LQR) is derived and implemented concurrently on the second pipeline. By parallelizing the implementation of the LQR control law and system identification algorithm, the wireless sensing network is shown to be capable of learning and adapting to system changes in real-time. Simulation and experiments on a 4 story benchmark shear structure is utilized to show the validity and scalability of the proposed adaptive feedback control approach.

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“…Another challenge for dense sensor networks is the required wiring for power and communication between sensor nodes. In the last decade, wireless sensor networks were introduced to overcome such drawbacks, and they have increasingly become a viable choice in SHM systems . Wireless technology in SHM continues to improve steadily, as reflected by the following representative recent research.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification using image-based pattern recognition on event-based binary data generated from self-powered sensor networks

Salehi

Das

Chakrabartty

et al. 2018

Struct Control Health Monit

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Summary A continuing challenge in structural health monitoring is power availability for sensors to collect and communicate data. A way to minimize the communication power demand is to transmit the minimum amount of information, namely, one bit. Event‐based binary signals are generated at sensor nodes according to local rules based on physical measurements, but interpretation at the global level requires dealing with discrete binary data, which implies system information with reduced resolution. This study presents an investigation on approaches for the interpretation of event‐based binary data provided by a self‐powered sensor network using a pulse‐communication protocol for use in structural assessment and damage identification. Pattern recognition (PR) methods based on image data analysis techniques were adapted for such purpose. The methods used were classifiers based on anomaly detection, a Bayesian method, and a nearest neighbor method. To improve the performance of the approach, 2‐dimensional principal component analysis and 2‐dimensional linear discriminant analysis were used as feature extraction techniques along with a nearest neighbor classifier. The PR methods and the performance of the interpretation algorithms were evaluated by using virtual data from finite element simulations and real data from experiments on plates. The ability of the PR methods to identify service demands, load variations, and localized material degradation was examined. Results indicate that image‐based PR methods can be effectively used for structural damage identification in plate‐like structures using event‐based binary data sets in novel wireless self‐powered sensor networks.

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Development of an extensible dual-core wireless sensing node for cyber-physical systems

Cited by 31 publications

References 12 publications

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

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

Adaptive feedback control of time varying structures utilizing dual-core wireless sensing and actuation nodes

Structural damage identification using image-based pattern recognition on event-based binary data generated from self-powered sensor networks

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