Fault-Tolerant Control for Networked Control Systems with Partly Unknown Transition Probabilities

Wang, Yanfeng; Li, Zuxin; Mohamed, Ttalbi; Wang, Yuling

doi:10.1109/safeprocess45799.2019.9213249

Cited by 2 publications

(7 citation statements)

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“…Furthermore, under the same transition probability matrices, the disturbance suppression performance index

μ_{\min}

obtained by the proposed method is smaller than that obtained by the method in Ref. [32].…”

Section: Numerical Examplesmentioning

confidence: 69%

“…In order to obtain the relationship between transition possibility and system performance and to illustrate the superiority of the method proposed in this study over that in Ref. [32], consider the following four different cases of transition possibility matrices:

\begin{matrix} align-starleft \\ left Q_{1} = [\begin{matrix} matrix \\ center ? & center ? \\ center ? & center ? \end{matrix}], {normalΠ}_{1} = [\begin{matrix} matrix \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \end{matrix}]; \\ left Q_{2} = [\begin{matrix} matrix \\ center 0.3 & center 0.7 \\ center ? & center ? \end{matrix}], {normalΠ}_{2} = [\begin{matrix} matrix \\ center 0.1 & center ? & center ? & center 0.2 \\ center ? & center ? & center 0.1 & center 0.3 \\ center 0.2 & center 0.1 & center ? \end{matrix}] \end{matrix}

…”

Section: Numerical Examplesmentioning

confidence: 99%

“…For this controlled plant, we cannot get the controller and observer gain matrices by the method in Ref. [32] due to its conservativeness.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…According to Theorem 4, the gain matrix K of the controller, the gain matrix L of the observer, and the minimum disturbance suppression performance index μ min can be obtained as follows: In order to obtain the relationship between transition possibility and system performance and to illustrate the superiority of the method proposed in this study over that in Ref. [32], consider the following four different cases of transition possibility matrices: - According to Theorem 3 and Theorem 4, the external disturbance suppression performance index μ min is as shown in Table 1.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…The state equation of the converter is as follows: The initial state of the system ( 12) is assumed as x 0 ¼ 2:0 −1:0 1:0 ½ � T , x0 ¼ 2:1 −1:1 1:1 ½ � T , and the For this controlled plant, we cannot get the controller and observer gain matrices by the method in Ref. [32] due to its conservativeness.…”

Section: Numerical Examplesmentioning

confidence: 99%

See 4 more Smart Citations

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

Wang

Sun

et al. 2021

IET Cyber-Syst and Robotics

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This study researches the H ∞ control issue for a networked Markovian jump system with data packet loss occurring not only in the channel from sensor to controller but also in the channel from controller to actuator via an observer. The mathematical model for the closed-loop networked Markovian jump system with data packet loss is established. The necessary and sufficient conditions for the closed-loop system to be stochastically stable are derived. The design approach of the controller and the minimal performance index of the external disturbance suppression are also given in the case that the transition possibilities of the system modes and the data packet loss are both partially unavailable. Finally, two numerical examples are used to illustrate the effectiveness of the proposed method. K E Y W O R D Sdata packet loss, Markovian jump system, networked control system, observer | INTRODUCTIONAs the networked control system (NCS) has the advantages of easy installation and maintenance, as well as convenient expansion, the application range of the NCS has become more and more extensive, including the fields of industrial systems, aerospace, robot manufacturing, remote sensing technology and so on [1][2][3][4]. Coupled with the increasing development of information technology, the NCS will be used in more fields in the future [5][6][7][8]. However, data packet loss and time-delay induced by the network would make the system's performance degrade and may even lead to instability, and these are two basic issues of the NCS [9][10][11][12][13].The research of data packet loss is a significant direction of NCS research, which has attracted considerable attention. For the current research study, there are mainly two approaches to deal with the problem of data packet loss. The first approach is to use a Bernoulli probability distribution sequence to describe the data packet loss as a random variable, which takes a value from f0; 1g [14][15][16][17]. The stability of the NCS whose communication channel was influenced by data packet loss was discussed [14], and a Bernoulli variable was used to model the data packet loss. Sliding mode control based on the observer for the NCS with quantisation was taken into consideration [15], in which data packet loss occurred when the measurement output was transmitted, and a compensating scheme was put forward to cope with influences of modelled data packet loss, which was treated as a Bernoulli process. A novel H ∞ approach for the NCS with multiple data packet losses was studied [16], and a control system containing two Bernoulli variables was applied to model the NCS, which contained not only measurement data packet loss but also control data packet loss. The problem of linear quadratic Gaussian control for the NCS with data packet loss was discussed [17], where dataThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Furthermore, under the same transition probability matrices, the disturbance suppression performance index

μ_{\min}

obtained by the proposed method is smaller than that obtained by the method in Ref. [32].…”

Section: Numerical Examplesmentioning

confidence: 69%

\begin{matrix} align-starleft \\ left Q_{1} = [\begin{matrix} matrix \\ center ? & center ? \\ center ? & center ? \end{matrix}], {normalΠ}_{1} = [\begin{matrix} matrix \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \\ center ? & center ? & center ? & center ? \end{matrix}]; \\ left Q_{2} = [\begin{matrix} matrix \\ center 0.3 & center 0.7 \\ center ? & center ? \end{matrix}], {normalΠ}_{2} = [\begin{matrix} matrix \\ center 0.1 & center ? & center ? & center 0.2 \\ center ? & center ? & center 0.1 & center 0.3 \\ center 0.2 & center 0.1 & center ? \end{matrix}] \end{matrix}

…”

Section: Numerical Examplesmentioning

confidence: 99%

“…For this controlled plant, we cannot get the controller and observer gain matrices by the method in Ref. [32] due to its conservativeness.…”

Section: Numerical Examplesmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

See 3 more Smart Citations

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

Wang

Sun

et al. 2021

IET Cyber-Syst and Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

Liu

Hao

2022

Asian Journal of Control

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Summary This paper is concerned with covariance intersection (CI) fusion for multi‐sensor linear time‐varying systems with unknown cross‐covariance. Firstly, a CI fusion weighted by diagonal matrix (DCI) is proposed, and it is proved to be unbiased and robust and has higher accuracy than classical CI fusion. Secondly, the genetic simulated annealing (GSA) algorithm is used for multi‐parameter optimization problem caused by diagonal matrix weights. Considering the serious time‐consuming problem in optimization process of the GSA algorithm, Back Propagation (BP) network is used to obtain the optimal weights. Eventually, the DCI based on GSA algorithm and BP network is proposed. The proposed algorithm has higher accuracy and better stability than classic CI fusion algorithms. Simulation analyses verify the effectiveness and correctness of the conclusion.

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Fault-Tolerant Control for Networked Control Systems with Partly Unknown Transition Probabilities

Cited by 2 publications

References 11 publications

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

H∞ control for a networked Markovian jump system subject to data packet loss based on the observer

The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

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