Decentralized event-triggering communication scheme for large-scale systems under network environments

Peng, Chen; Tian, Engang; Zhang, Jin; Du, Dajun

doi:10.1016/j.ins.2015.06.036

Cited by 59 publications

(14 citation statements)

References 29 publications

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“…By introducing new variables X N , R 1N , R 2N , L 1N , and L 2N , (13) can be rewritten into (14) and (15).…”

Section: Resultsmentioning

confidence: 99%

“…Compared with time‐triggered mechanism, it is a strategy that the control will be executed only when the triggering condition is violated, rather than the elapse of a certain fixed period of time. In this way, the event‐triggered control can adjust the sampling interval and effectively reduce system resource consumption while preserving a satisfactory closed‐loop performance . Generally, the proposed ETMs can be divided into two broad categories: relative and absolute ones.…”

Section: Introductionmentioning

confidence: 99%

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Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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This paper is concerned with modeling and controlling under the mixed event‐triggered mechanism (ETM) for networked control systems (NCSs) with time‐varying delays and uncertainties. Firstly, an event‐triggered threshold is set by using both state and state‐independent informations in the mixed ETM. Then the event‐triggered NCSs with network‐induced time‐varying delays which exist in both sensor‐to‐controller and controller‐to‐actuator channels are modeled as a general time‐delay system. Based on the piecewise differentiable characteristic of the time‐varying delay and by using the approach of free weighting matrix and reciprocally convex, a less conservative criterion to be globally uniformly ultimately bounded (GUUB) stability and a controller design method are derived. Furthermore, an algorithm is proposed to obtain the desired mixed ETM and state‐feedback controller which can render the network load and control performance to reach an expected level. Compared with the relative and absolute ETMs, the proposed mechanism can effectively improve the transmission efficiency during the whole working time. Finally, a numerical example is given to show the effectiveness of the proposed approach.

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“…By introducing new variables X N , R 1N , R 2N , L 1N , and L 2N , (13) can be rewritten into (14) and (15).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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“…Theorem 2: For given scalars τ ca m ≥ 0, τ sc m ≥ 0, τ M > 0, h > 0, ̺ > 0, γ > 0, and matrices L j and K of appropriate dimensions, the network-based T-S fuzzy DPS (19) is asymptotically stable with an H ∞ norm bound γ, if there exist symmetric positive definite matrices P , Q 1 , Q 2 , R 1 , R 2 , R 3 , R 4 , S 1 , and S 2 such that the inequalities (27) and (28) hold for 1 ≤ i = j ≤ 4,…”

Section: Stability Analysis For Networked T-s Fuzzy Dpssmentioning

confidence: 99%

Network-Based T–S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles

Wang

Han

Fei

et al. 2018

IEEE Trans. Cybern.

Self Cite

240

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This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

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“…Compared with the PPDG, their BNPDGbased localization approach overcomes the limitation across nonadjacent nodes and provides more precise localization by taking its output nodes as the common conditions to calculate the conditional probability of each nonoutput node. Yatish K.Joshi,Shi Zhang,et al [21][22][23][24][25][26][27] proposed a Heuristic Multidimensional Scaling (HMDS) algorithm to improve accuracy of node localization in anisotropic WSNs with holes. By exploring the virtual node and constructing the shortest paths between nodes, the Euclidean distances between nodes are obtained via employing the heuristic approach such that they can be used to calculate more accurate locations of the nodes.…”

Section: Background and Researchmentioning

confidence: 99%

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

Liu

2018

Wireless Communications and Mobile Computing

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With the rapid development of wireless sensor network (WSN) technology and its localization method, localization is one of the basic services for data collection in WSN. The localization accuracy often depends on the accuracy of distance estimation. Because of the constraint in size, power, and cost of sensor nodes, the investigation of efficient location algorithms which satisfy the basic accuracy requirement for WSN meets new challenges. This paper proposes a novel intelligent node localization algorithm in WSN based on beacon nodes to improve the precision in location estimation. Firstly, system model of WSN node localization is constructed according to the WSN environment. Then traditional WSN node localization methods such as DV-HOP, GA, and PSO are studied. Localization algorithm of WSN is proposed by using dynamic mathematics modeling. And the result of simulation, which is compared to the traditional algorithm, indicated that this algorithm is better to improve the accuracy and coverage of WSN. The simulation results show that the performance of the proposed WSN location algorithm is better than the traditional localization algorithms.

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Decentralized event-triggering communication scheme for large-scale systems under network environments

Cited by 59 publications

References 29 publications

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Network-Based T–S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

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