Discrete-Event-Based Deterministic Execution Semantics With Timestamps for Industrial Cyber-Physical Systems

2020 ◽  
Vol 50 (3) ◽  
pp. 851-862 ◽  
Author(s):  
Wenbin Dai ◽  
Cheng Pang ◽  
Valeriy Vyatkin ◽  
James H. Christensen ◽  
Xinping Guan
2021 ◽  
Author(s):  
Zhaoyang Cuan ◽  
Dawei Ding ◽  
Heng Wang

Abstract This paper is concerned with the event-based control problem for nonlinear cyber-physical systems (CPSs) with state constraints. A novel security control strategy consisting of a self-triggered mechanism is developed to decrease the network communication loads to the most extent on the basis of ensuring system safety and stability. The maximum capability of the designed self-triggered mechanism to resist denial-of-service (DoS) attacks occurring in controller-actuator (C-A) and sensor-controller (S-C) channels synchronously is also analyzed. In particular, we prove that the security control strategy guarantees the system safety and stability without resulting in Zeno behavior. Finally, a numerical example is provided to demonstrate the prominent effectiveness and the advantages over the existing results.


2014 ◽  
Vol 602-605 ◽  
pp. 2242-2248
Author(s):  
Hui Lin Wang

Representation model of composition event is a very important issue in the study of composition event. Aiming to solve the problem of no well reflecting the spatial-temporal characteristic of CPS (Cyber-Physical Systems) in current composition even models, a representation model of composition event based on multi-tuple is proposed for CPS in this paper. The contribution of the paper lies that we use five tuples: object, time, space, event and property, to represent a composition event of CPS, which not only can more comprehensively reflect the spatial-temporal characteristic of CPS event, but also can reflect the some dynamic change features of external environment. The simulation results show that our proposed scheme has several advantages in reducing event average error rate compared with some general event model methods.


2011 ◽  
Vol 110-116 ◽  
pp. 4043-4049
Author(s):  
Soo Young Moon ◽  
Hyuk Park ◽  
Tae Ho Cho ◽  
Won Tae Kim

Most of existing frameworks for modeling and simulation of hybrid systems represent continuous behavior of systems using ordinary differential equations (ODEs). ODE models can be represented as discrete event system specification (DEVS) models through discretization and simulated in the DEVS simulation framework. However, in cyber-physical systems (CPS), it is difficult to represent the continuous behavior of a system using an ODE because it can have unknown, unpredictable variables. In that case, it is needed to predict the model’s next event time by inference to embed the model in a DEVS model. We propose the simulation framework in which a fuzzy inference module is added to each simulation model to determine its next event time. The proposed method enables simulation of hybrid system models which can or cannot be represented using an ODE.


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