State estimation via prediction-based scheme for linear time-varying uncertain networks with communication transmission delays and stochastic coupling

Xu, Bing; Hu, Jun; Jia, Chaoqing; Cao, Zhipeng; Huang, Junfeng

doi:10.1080/21642583.2021.1888820

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…Multiple publications discuss the state estimation of distributed dynamic systems using data-driven methods by deploying machine learning and artificial intelligence schemes such as [50,51] and the references therein. Also, the use of a data-driven state estimation method for a time-varying uncertain dynamical network can be found in [52] where the effect of communication transmission delays is considered in the system state estimation between adjacent nodes in the network. However, the estimation 9 ______________________________________________________________________________________________________ This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8416 of the communications network state by utilizing measurement of the physical system has not been discussed in the literature.…”

Section: State Estimation For Industrial Control Systemsmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

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Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.

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Section: State Estimation For Industrial Control Systemsmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

View full text Add to dashboard Cite

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AA-WGAN: Attention augmented Wasserstein generative adversarial network with application to fundus retinal vessel segmentation

Liu

Wang

et al. 2023

Computers in Biology and Medicine

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Encoding–decoding-based fusion estimation with filter-and-forward relays and stochastic measurement delays

et al. 2023

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State estimation via prediction-based scheme for linear time-varying uncertain networks with communication transmission delays and stochastic coupling

Abstract: State estimation via prediction-based scheme for linear time-varying uncertain networks with communication transmission delays and stochastic coupling,

Cited by 8 publications

References 39 publications

Machine learning based wireless interference estimation in a robotic force-seeking application

Machine learning based wireless interference estimation in a robotic force-seeking application

AA-WGAN: Attention augmented Wasserstein generative adversarial network with application to fundus retinal vessel segmentation

Encoding–decoding-based fusion estimation with filter-and-forward relays and stochastic measurement delays

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