Event‐Triggered Fault Detection for Networked Control Systems Subject to Packet Dropout

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.

mentioning

confidence: 99%

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

Liu

2018

“…Under the external perturbation, i.e., (t) ≠ 0,we have the following theorem. (8) and event-triggered rule (10) is finite-gain  2 stable from to x with a gain less than…”

Section:  2 Gain Stability With External Disturbancementioning

confidence: 99%

“…With the development of system science, networked control systems (NCSs) have attracted researchers' interests from various fields [1][2][3][4][5][6][7][8][9][10][11][12]. Different from the traditional control system without the communication network (as Figure 1), the networked control system is a kind of system in which the feedback signal from the sensor to the controller and the control signal from the controller to the actuator are transferred through the shared communica-tion network forming a closed-loop, formed by introducing a shared communication network into the single plant control system.…”

Section: Introductionmentioning

confidence: 99%

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Controlling the multi‐plant networked system with external perturbations via adaptive model‐based event‐triggered strategy

Pan

Feng

et al. 2019

This paper concerns the multi‐plant networked control system with external perturbations by applying the adaptive model‐based event‐triggered control strategy. Compared with existing works, we introduce multiple plants topology in order to smooth the information exchanges among different plants. Gained insight into the adaptive model features in the view of event‐triggered thought, the event‐triggered rules, determining when the control inputs update, are obtained using the Lyapunov technique to reduce the communication cost. By designing some adaptive updating laws combined with the concept of event‐triggered, the unknown parameters in uncertain multi‐plant networked control systems are real‐time online estimated and adjusted with respect to the relevant nominal systems at event‐triggered instants. To avoid Zeno phenomena, a lower bound of event‐triggered execution interval is discussed. Furthermore, given the external perturbations, L2 gain stability theory is introduced to analyze the stability of multi‐plant networked control systems with bounded perturbations, and then the sufficient conditions related to the multiple plants topology structure are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of our theoretic results.

“…Reliability is very important for many industrial processes [1,2]. However, unexpected faults may cause unreliability.…”

Section: Introductionmentioning

confidence: 99%

Fault‐Tolerant Control Based on Augmented State Estimator and PDF

Dai

Chen

et al. 2018

A new fault‐tolerant control based on augmented state estimator and probability density function (PDF) is proposed for a stochastic distribution system (SDS) with time‐delay and additive fault. First, a system model based on a PDF with the additive fault is constructed by using square‐root rational B‐spline neural networks. Second, an augmented system is obtained by converting the additive fault as an auxiliary state variable. In this framework, a robust augmented state estimator is designed to estimate the original states and the additive fault simultaneously. Then, based on the obtained estimation of fault, a delay‐dependent fault‐tolerant control is designed to compensate the fault. Finally, the numerical simulations show the effectiveness of the proposed method.