Analysis of Networked Structural Control With Packet Loss

Truong, Thao H.T.; Seiler, Peter; Linderman, Lauren E.

doi:10.1109/tcst.2021.3051578

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Limited payload capacity, congestion and connection interruptions can cause data packet loss in network communication, which is inevitable and fundamentally random. For example, when network congestion causes the number of data transmissions to exceed the system's allowed waiting time, the system will automatically abandon data reception and consider it as lost [5][6][7][8]. In addition, signal distortion caused by ionospheric flicker or interference can also affect the tracking performance of loops [9].…”

Section: Introductionmentioning

confidence: 99%

Robust packet loss compensation in the cloud‐based TT&C receiver using a predictive tracking loop with RBF network identification

Fan,

Liu,

et al. 2023

IET Radar Sonar & Navi

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In recent years, ground‐based aerospace tracking, telemetry and control (TT&C) systems have been shifting towards an IP‐based cloud TT&C network architecture to improve signal transmission efficiency and system flexibility. However, this new architecture presents new challenges for the performance of tracking loops in the cloud‐based TT&C receiver. The authors present a predictive compensation tracking loop that addresses the problem of packet loss. The loop utilises radial basis function (RBF) online network identification for robust carrier and code frequency offset tracking. To account for packet loss, the phenomenon is modelled as a Bernoulli random process, and the predictive compensation loop uses the trained RBF network to maintain the system's tracking state. When normal data are received again, the loop can track the received information well and align the frequency offset locking state. The authors compare the performance of the proposed predictive compensation tracking loop with traditional loop tracking performance using data hold strategies under different packet loss rates. The results demonstrate the effectiveness of the proposed approach in maintaining stable tracking and solving in the discontinuous reception of the loop under packet loss environments. The proposed predictive compensation tracking loop offers a practical solution for addressing packet loss, which is a common issue in real‐world applications.

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Section: Introductionmentioning

confidence: 99%

Robust packet loss compensation in the cloud‐based TT&C receiver using a predictive tracking loop with RBF network identification

Fan,

Liu,

et al. 2023

IET Radar Sonar & Navi

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“…cost functions in the optimal control). Switched system models are used in a variety of applications including controlling a Mars rover exploring an unknown heterogeneous terrain, solar power generation, investments in financial markets, and communications with packet losses [1]- [7]. However, these benefits are accompanied by new complexity challenges: the number of modes that is needed to model systems accurately and thoroughly may grow undesirably large.…”

Section: Introductionmentioning

confidence: 99%

Mode Reduction for Markov Jump Systems

Du,

Balzano,

Ozay

2022

Preprint

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Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time. To achieve accurate modeling in practice, one may need a large number of modes, but this may in turn increase the model complexity drastically. Existing work on reducing system complexity mainly considers state space reduction, yet reducing the number of modes is less studied. In this work, we consider Markov jump linear systems (MJSs), a special class of switched systems where the active mode switches according to a Markov chain, and several issues associated with its mode complexity. Specifically, inspired by clustering techniques from unsupervised learning, we are able to construct a reduced MJS with fewer modes that approximates well the original MJS under various metrics. Furthermore, both theoretically and empirically, we show how one can use the reduced MJS to analyze stability and design controllers with significant reduction in computational cost while achieving guaranteed accuracy.

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