A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

Diao, Jing-Dong; Guo, Jin; Sun, Changyin

doi:10.1007/s11432-019-9802-0

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…Therefore, it is necessary to directly measure or indirectly estimate the packet loss probabilities. Direct detection of packet loss occurrences is possible in the measurement data path (Cheng et al, 2020; Diao et al, 2020). However, it may be infeasible to detect the packet loss occurrences if it should be carried out at the transmitting node or when the underlying communication protocol does not support some required features (Kocak and Zaim, 2017; Luckie et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

System identification in presence of undetectable packet losses in the actuation path

Mohammadzadeh,

Tavassoli

2023

Transactions of the Institute of Measurement and Control

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Identification of networked systems over communication links with packet losses is considered. It is assumed that there is no connection from the actuators to the system identifier such that no information is available on loss of data packets carrying the actuation signals. While the packet loss occurrences in the sensor links are naturally detectable by the system identifier as the receiving node, the packet loss occurrences in the actuation paths are not directly detectable. The existing system identification methods fail to be applicable without the information on packet loss occurrences. Presenting a solution for this issue is the objective of the present work. Two recursive algorithms are proposed to solve the problem in real time. For this purpose, ARMAX input–output representations of the system with Markovian jumping modes are developed. Then, filters are designed for estimation of the packet loss occurrences. The estimated mode is used in the recursive algorithms for identification of the whole system based on the maximum likelihood approach. A main algorithm and its simplified version are proposed that are capable of finding the various kinds of system parameters including the physical plant parameters and the communication mode transition probabilities. Effectiveness of the algorithms is verified through simulation examples where the system performance is evaluated and compared.

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Section: Introductionmentioning

confidence: 99%

System identification in presence of undetectable packet losses in the actuation path

Mohammadzadeh,

Tavassoli

2023

Transactions of the Institute of Measurement and Control

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“…After decades of development, a considerable number of literatures (e.g., previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]) on identification, estimation, and control with quantized data have appeared. There are various forms of quantized data in different application fields and different quantizers will produce different quantized data, such as set-valued quantized data, uniform quantized data, and so on.…”

Section: Introductionmentioning

confidence: 99%

Quantized‐output‐based least squares of ARX systems

Jing

2022

Asian Journal of Control

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Summary This paper considers the estimates of parameters of stochastic autoregressive exogenous input (ARX) systems with quantized data. The quantized error caused by quantized data brings difficulties to the parameter estimation of ARX systems. Therefore, I start with the boundedness of quantized error and design the size of quantized error thanks to good properties of the least squares. Then the influences of the noise of the system itself and the bounded noise caused by quantized error on the parameter estimation error are analyzed, respectively. The inputs of the system are designed to make the system satisfy the persistent excitation condition, so as to ensure the boundedness of the parameter estimation error. A numerical example is given to demonstrate the theorem.

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“…In the past two decades, identification with quantized data has become a hot topic and a large number of literature (e.g. [1][2][3][4][5][6][7][8][9][10]) emerged. Among others, [1] provided two different parameter estimation frameworks, respectively, for deterministic systems and stochastic systems with binary data.…”

Section: Introductionmentioning

confidence: 99%

LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

Jing

Zhang

2021

J Syst Sci Complex

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This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with uniform quantization error. The authors analyse the properties of the LS and the forgetting factor LS, and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error, which implies that the smaller the quantization error is, the smaller the estimation error is. A numerical example is given to demonstrate theorems. Keywords Discrete-time linear time-invariant systems, parameter estimation, quantized output.

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

Cited by 13 publications

References 7 publications

System identification in presence of undetectable packet losses in the actuation path

System identification in presence of undetectable packet losses in the actuation path

Quantized‐output‐based least squares of ARX systems

LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

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