Filtering for Discrete-Time Switched Fuzzy Systems With Quantization

Dong, Shanling; Su, Hongye; Shi, Peng; Lu, Renquan; Wu, Zheng‐Guang

doi:10.1109/tfuzz.2016.2612699

Cited by 120 publications

(37 citation statements)

References 47 publications

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“…With the aid of statistical technique, the information of statistic information φ f is easy to be achieved, that is,

φ_{f} = \lim_{k \to \infty} \frac{k_{f}}{k}

, where k f is the times of ϑ k = f in the interval [1, k ]. Therefore, compared with TP method, the study of DTSSs with SP approach is a fascinating topic …”

Section: Problem Formulationmentioning

confidence: 99%

“…Remark Quantization issue has been widely studied in other works . With the consideration of mode‐dependent phenomenon, a more general model is constructed, where quantization effects are encountered from plant to controller and from controller to plant.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Therefore, it can be concluded that the SP approach plays an important role in stability analysis and control design of SSs. So far, the SPs studied in other works were assumed to be time‐invariant (time‐independent) and given in prior. Inspired by the work of Song et al, a charming but challenging work about SSs constructed a new class of SPs that can be characterized by time dependency and reflected the change of SSs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

Cheng

Park

Zhao

et al. 2019

Intl J Robust & Nonlinear

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Summary The present work deals with the problem of static output feedback control for discrete‐time switched systems (SSs) with quantization effects. Different from the existing results on SSs, a more general model with respect to a new switching rule (SR) is explored by utilizing a nonhomogeneous sojourn probability approach. Both control input and measured output are quantized by the mode‐dependent static logarithmic quantizers, respectively. To decrease the conservatism and eliminate the coupling parameters, a descriptor technology is employed in the static output feedback control scheme. In the framework of the time‐dependent SR, sufficient conditions are proposed to guarantee the mean square stability of resulting systems. Finally, an example is expressed to demonstrate the applicability and validity of the developed theoretical results.

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“…With the aid of statistical technique, the information of statistic information φ f is easy to be achieved, that is,

φ_{f} = \lim_{k \to \infty} \frac{k_{f}}{k}

, where k f is the times of ϑ k = f in the interval [1, k ]. Therefore, compared with TP method, the study of DTSSs with SP approach is a fascinating topic …”

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

Cheng

Park

Zhao

et al. 2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The guiding principle of soft computing is to explore the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost that are not handled with conventional hard computing. Initially, soft computing is composed of three main branches: fuzzy systems [20,21], evolutionary computation [22,23], artificial neural computing [24]. Up to now, many new methods or techniques have been proposed for imprecision, uncertainty and partial truth, which are belong to soft computing.…”

Section: Soft Computingmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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“…The information is exchanged among control system components (sensor, controller, actuator, etc.). Due to the advantages such as simple installation, reduced wiring, increased system agility, and high reliability, NCSs have been widely used in broad areas, for example, unmanned aerial vehicles, mobile sensor networks, environment monitoring, and automated highway systems [3][4][5]. However, the introduction of communication networks also brings communication constraints to the control systems, for example, network-induced time-delays and packet dropouts [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Robust H_∞ Fault Detection for Networked Markov Jump Systems with Random Time‐Delay

Wang

Zhou

2017

Mathematical Problems in Engineering

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This paper investigates the problem of robust ∞ fault detection for networked Markov jump systems with random time-delay which is introduced by the network. The random time-delay is modeled as a Markov process, and the networked Markov jump systems are modeled as control systems containing two Markov chains. The delay-dependent fault detection filter is constructed. Furthermore, the sufficient and necessary conditions which make the closed-loop system stochastically stable and achieve prescribed ∞ performance are derived. The method of calculating controller, fault detection filter gain matrices, and the minimal ∞ attenuation level is also obtained. Finally, one numerical example is used to illustrate the effectiveness of the proposed method.

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Filtering for Discrete-Time Switched Fuzzy Systems With Quantization

Cited by 120 publications

References 47 publications

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

When social computing meets soft computing: opportunities and insights

Robust H_∞ Fault Detection for Networked Markov Jump Systems with Random Time‐Delay

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Filtering for Discrete-Time Switched Fuzzy Systems With Quantization

Cited by 120 publications

References 47 publications

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

Static output feedback control of switched systems with quantization: A nonhomogeneous sojourn probability approach

When social computing meets soft computing: opportunities and insights

Robust H∞ Fault Detection for Networked Markov Jump Systems with Random Time‐Delay

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Product

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Robust H_∞ Fault Detection for Networked Markov Jump Systems with Random Time‐Delay