KR20120086844A - Hybrid Model Simulation Method for Cyber-Physical System Environments - Google Patents

Hybrid Model Simulation Method for Cyber-Physical System Environments Download PDF

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KR20120086844A
KR20120086844A KR1020110008133A KR20110008133A KR20120086844A KR 20120086844 A KR20120086844 A KR 20120086844A KR 1020110008133 A KR1020110008133 A KR 1020110008133A KR 20110008133 A KR20110008133 A KR 20110008133A KR 20120086844 A KR20120086844 A KR 20120086844A
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South Korea
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model
fuzzy
hybrid
atomic
cyber
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KR1020110008133A
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Korean (ko)
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문수영
박혁
김지연
김형종
조대호
김진명
전인걸
김원태
박승민
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서울여자대학교 산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/048—Fuzzy inferencing

Abstract

The present invention relates to a method of modeling and simulating a hybrid system for a cyber-physical system environment, wherein each of the cyber-physical systems has characteristics of computer-environment interaction, real-world data, high reliability, uncertainty, hybrid, etc. By incorporating inference modules using the inference method of artificial intelligence in the detailed model of, we can model complex real-world systems including human intelligence. In addition to an inference module that can predict its next state, it provides a simulation framework based on discrete event system specification formalism to simulate these models, enabling model-level simulation in a more real-world-like environment, Can raise the level.

Figure P1020110008133

Description

Hybrid Model Simulation Method for Cyber-Physical System Environments

The present invention relates to a method for modeling and simulating a hybrid system for a cyber-physical system environment, and more specifically, to modeling some real-world systems with human intelligence among various detailed models of the cyber-physical system using inference modules, A simulation framework based on discrete event system specification formalism capable of simulating the model.

Cyber-physical system refers to a system that guarantees software reliability, real-time and intelligence in order to prevent unexpected occurrences and errors due to the complexity of real-world systems and computing systems. Based on the network, the cyber-physical system can be configured in a strong combination of physical systems or physical processes and the computing systems that control them. Cyber-physical systems are characterized by computer interaction, real-world large data, high reliability, uncertainty, and hybrids. The state of the system consists of variable values that affect the output of the system. The variable values are classified into discrete event systems or continuous systems according to whether the variable values are discrete or continuous, but a system in which the two are mixed is a hybrid system.

In order to ensure the stability and reliability of the system, a technique for precisely modeling the system is required from the design stage, and performance evaluation and verification through simulation may be essential. Various frameworks have been studied to model and simulate precise hybrid systems, and the simulation framework based on discrete event system specification formalism provides an environment for simulating the hybrid system model.

However, the discrete event system specification-based simulation framework does not support a method for simulating a continuous state change of the hybrid model if it is not given as a differential equation. In addition, in the case of the cyber-physical system, there is a problem that a large number of unpredictable variables exist so that differential equations may not be defined or may be represented by a plurality of differential equations near infinity.

Therefore, there is a need for a method for modeling and simulating a hybrid system by solving the above problems.

In the case of a model in which a continuous state change of a cyber-physical system is not represented by a differential equation, the present invention includes a fuzzy rule-based reasoning module in the model, which is based on a more real-world-like cyber-physical system model and discrete event system specification formalism. It aims to provide a simulation framework.

The characteristics of the continuous state change of a typical system can be represented by a differential equation, and the differential equation shows the relationship between the time and the rate of change of the state variable. However, under the cyber-physical system environment, an unknown variable or an unpredictable variable may have an unexpected effect on a specific state of the system. Therefore, there is a limit in expressing one differential equation. Therefore, in the present invention, the system model, which is difficult to be expressed by the one differential equation, is defined and classified as an unknown model.

The Unknown model is included in one category of continuous systems of the cyber-physical system. In the case of the differential equation model, which is a classification of another continuous system, it is simulated through a discretization method such as time slicing or state quantization, whereas for the unknown model, the dynamic characteristics of the system are described by fuzzy rules. In the simulation step, the next state of the model is calculated by inference based on the fuzzy rule.

In the case of the differential equation model, which is a general continuous system model that can be expressed as a differential equation, the next state of the system can be tracked by an equation, while for the unknown model, it is impossible to completely predict how the next state will change at a given point in time. Because it can not be characterized by performing the simulation in a manner to predict the state of the model by the above-mentioned inference.

An atomic-fuzzy model is defined in which the fuzzy inference module is inserted into a discrete event system specification formalism based atomic model to represent the unknown model. The atomic-fuzzy model may be input to an external state transition function of the model when an input is generated from the outside and update the state of the model therefrom. When a predetermined amount of time passes in the current state of the model, the output of the model is generated and at the same time, the internal state transition function is called to update the ecology of the model. The inference module can infer the time of occurrence of the next event from the input of the model and the current state, and the inference uses a predefined fuzzy rule, which is defined differently according to the model and used to represent the intelligence of the system. It features.

According to the simulation method for the cyber-physical system environment according to the present invention as described above, the system at the model level by representing the model using a fuzzy rule for a continuous model that is not represented by a differential equation for the simulation of the cyber-physical system Extend expressiveness, which allows for more complex and precise representation of the system.

In addition, it is possible to predict the next state of the model through fuzzy rule-based inference during the simulation process, by providing a framework that can simulate a simple continuous model represented by a differential equation and a model described by fuzzy rules together. This has the advantage of increasing the model level of simulation.

1 illustrates the classification of a hybrid cyber-physical system model of the present invention.
2 is a view showing the difference in the dynamic characteristics of the ODE model and Unknown model of the present invention.
3 illustrates the concept of an atomic-fuzzy model of the present invention.
4 is a diagram showing the components and the input-output relationship of the atomic-fuzzy model of the present invention.
5 is a diagram illustrating a membership function for fuzzy rule-based system input and output variables of the UACV model.

Hereinafter, a hybrid simulation method for a cyber-physical system environment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a classification of a hybrid cyber-physical system model of the present invention.

As shown in FIG. 1, the hybrid cyber-physical system 11 may be composed of a continuous cyber-physical system 12 and a discrete cyber-physical system 18. Continuous simulation is a simulation of a system in which state variables change continuously over time, usually expressed as a differential equation that defines the relationship between the rate of change of time and state variables, but does not identify variables or characteristics that are unpredictable in a cyber-physical system environment. Unexpected systems may cause differential equations to change in the middle, or there may be continuous systems that cannot be defined by differential equation models.

Therefore, in the present invention, a system capable of defining the continuous system 12 of the cyber-physical system environment as one differential equation is represented by an Ordinary Differential Equation (ODE) model 13, and a system that cannot be defined by the differential equation is described. The unknown model 16 is used to classify the continuous cyber-physical system 12.

In the case of the ODE model 13, a continuous system having a differential equation model cannot be represented as an accurate differential equation model because it is represented as a finite state on a digital computer. Therefore, the simulation of differential equation model requires approximation of the state value through discretization, the Time Slicing (14) method which discretizes based on the time axis according to the discretization method, and the State Quantization (15) which progresses time based on the event. Classify as)

In case of the unknown model (16), it is a model of continuous system defined in consideration of the uncertainty of the cyber-physical system environment, and means a system in which the characteristics of the system are not known and an unpredictable variable exists. The dynamic characteristics of the system can be described and the simulation can be performed through inference-based simulation.

2 is a view showing the difference in the dynamic characteristics of the ODE model and Unknown model of the present invention.

In the case of the ODE model 21, the state of the system can be tracked through the calculation of the differential equation given to the model. On the other hand, in the case of the unknown model 22, since it is impossible to completely predict how the state changes at a given point in time, the simulation is performed by predicting the state of the model by inference.

The present invention provides an atomic-fuzzy model in which a fuzzy inference module is inserted into an atomic model based on a discrete event system specification formalism in order to represent an unknown model in order to enable such inference simulation.

3 is a diagram illustrating the concept of an atomic-fuzzy model of the present invention.

The atomic-fuzzy model 33 is designed to insert a fuzzy system 32, which is one of the inference engines, into the existing atomic model 31 for inference-based simulation. This atomic-fuzzy model 33 is recognized as a simulation entity of the discrete event system specification formalism based simulation environment and becomes a component.

As shown in FIG. 3, in the atomic-fuzzy model 33, the atomic model 31 changes states in units of quantum size, and the fuzzy system 32 generates a fuzzy rule dependent on the model while scheduling the next event occurrence. By using the input of the atomic model 31 and the current state to infer the occurrence time of the next event of the model, the result can be output to the atomic model 31.

4 is a diagram showing the components and the input-output relationship of the atomic-fuzzy model of the present invention.

As shown in FIG. 4, the left side of the internal components of the atomic-fuzzy model 40 corresponds to the components of the existing atomic model based on the discrete event system specification formalism. When an input from outside of the atom-fuge model 40 occurs, the input is passed to an external state transition function 41 of the atom-fuge model 40 from which the state 42 of the model is updated. The input and current state 42 are passed to the fuzzy system 44 to infer the time of the next event occurrence. Inference uses a predefined fuzzy rule, which is defined differently depending on the model and can be used to represent the intelligence of the system.

The result value through the fuzzy inference of the fuzzy system 44 is returned to the time forward (ta) function 43 and the output function 45 is generated when the time elapses by the value of the time forward function 43 in the updated state 42. It is called to generate the output of the model and at the same time the internal state transition function 46 is called to update the state of the model.

Through this atomic-fuzzy model 40, it is difficult to completely predict the next state corresponding to the unknown model among the continuous systems of the cyber-physical system, or may be represented as a model for model simulation of an intelligent system.

As an example for representing the purge system 44 according to the present invention, the unmanned aerial vehicle (UAV) model is an example of a cyber-physical system mainly used for military purposes. Variables affecting the operation of the UAV are a variety of climates, durability, altitude, command, presence of enemies, etc. The system is characterized by making intelligent judgments by itself from the input of the external environment. The variables that affect the behavior of the UAV are very diverse and difficult to predict, so the use of some specific differential equations is impractical and can be modeled using the atomic-fuzzy model presented above. In the example of the present invention, it is assumed that the UAV is used for the neck of the reconnaissance, and it is assumed that the event determines that the current altitude is changed by the threshold.

FIG. 5 illustrates a membership function for fuzzy rule-based system input / output variables of a UAV model. Among these, FIGS. 5A, 5B, and 5C correspond to membership functions for fuzzy input variables, and FIG. 5D shows membership functions for fuzzy output variables.

These membership functions represent values received from the internal and external environment of the UAV model. Details of each fuzzy input variable and output variable can be expressed as shown in the following table.

Weather Bad weather B (Bad) Weather average N (Normal) Good weather G (Good)

Durability Weak durability W (Weak) Durable usually N (Normal) Durable S (Strong)

Altitude Height floor G (Ground) Height medium M (Medium) Altitude high H (High)

Next event time Very imminent VS (Very Soon) Imminent S (Soon) later L (Later)

Some of the rules of the fuzzy rule system that can be obtained by using the membership function according to the above table and FIG. 5 are as follows.

R01: If the weather is B, the durability is W, and the altitude is G, then the time of occurrence of the next state event is L.

R03: If the weather is B, the durability is W, and the altitude is H, the time of occurrence of the next state event is VS.

R08: If the weather is B, the durability is S and the altitude is M, then the time of occurrence of the next state event is S.

R23: If the weather is G, the durability is N, and the altitude is M, then the time of occurrence of the next state event is S.

R25: When the weather is G, the durability is S, and the altitude is G, the time of the next state event occurs is VS.

R27: If the weather is G, the durability is S, and the altitude is H, then the time of occurrence of the next state event is L.

According to the above rules, if the weather is bad or if the durability is low, the next event will be expected sooner or later if the current altitude is not above ground, and if the weather is good and the durability is good, it will take off. In the case of the altitude above, it can be seen that the UAV system having the characteristic of predicting that much time is left until the next event occurrence time is represented through the fuzzy rule.

Although the present invention has been described in detail with reference to exemplary embodiments above, those skilled in the art to which the present invention pertains can make various modifications without departing from the scope of the present invention. Will understand. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims below and equivalents thereof.

<Description of Signs for Main Parts of Drawings>
11: Hybrid Cyber-Physical System 12: Continuous Cyber-Physical System
13: ODE model 14: time based discretization
15: State Based Discretization 16: Unknown Model
17 Inference Based Simulation 18 Discrete Cyber-Physical System
40: atom-fuzzy model 41: external state transition function
42: State 43: Time Forward Function
44: fuzzy system 45: output function
46: internal state transition function

Claims (10)

A hybrid system model in a cyber-physical system environment, comprising a component of a discrete event system specification formalism based atomic model and a fuzzy inference module The method of claim 1, wherein the components of the atomic model execute an external state transition function on a given input of the model to update the state of the model,
Pass the input and current state of the model to the fuzzy inference module to schedule the next event occurrence according to the value returned by the fuzzy inference module,
When the next event occurs, the output function of the model is generated.
Hybrid system model, wherein the state of the model is updated by an internal state transition function The hybrid system model of claim 1, wherein the fuzzy inference module includes a fuzzy IF-THEN rule for predicting a time of occurrence of a next event of a model from an input of a model and a current state of the model. In a hybrid system model under cyber-physical system environment,
Hybrid system modeling method characterized by defining and classifying the system model, which is difficult to be represented by one differential equation, as Unknown model The method of claim 4, wherein the continuous system model is
A continuous system model comprising an atomic-fuzzy model with a fuzzy inference module embedded in a discrete event system specification formalism based atomic model. The continuous system model of claim 5, wherein the atomic-fuzzy model is a system for simulating a model of a system having difficulty or intelligence that is difficult to completely predict a next state corresponding to an unknown model of a continuous system of a cyber-physical system. The method of claim 5, wherein the atomic-fuzzy model is
While scheduling the next event occurrence by changing the state in quantum size, the fuzzy system relies on the model to infer the time of the next event occurrence of the model from the input and current state of the atomic model, using the fuzzy rules. Continuous system model, characterized in that to output the corresponding atomic model In a hybrid model under cyber-physical system environment,
Discrete event system specification formalism-based hybrid model simulation framework that can simulate hybrid system models, including components of the atomic model and fuzzy inference modules The hybrid model simulation framework of claim 8, wherein the discrete-based event system specification formalism-based hybrid model simulation framework includes an atomic-fuzzy model incorporating a fuzzy inference module into the discrete event system specification formalism-based atomic model. Hybrid model simulation framework The hybrid model simulation framework of claim 8, wherein the discrete-based event system specification formalism-based hybrid model simulation framework is capable of simulating the ODE model and the atomic-fuzzy model together.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101275172B1 (en) * 2012-12-17 2013-06-18 국방과학연구소 Method of simulating discrete event
KR101299192B1 (en) * 2012-09-14 2013-08-21 아주대학교산학협력단 Apparatus for simulating based on modeling and simulation framework in distributed cognitive radio networks
CN113672207A (en) * 2021-09-02 2021-11-19 北京航空航天大学 X language hybrid model modeling system, method and storage medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101299192B1 (en) * 2012-09-14 2013-08-21 아주대학교산학협력단 Apparatus for simulating based on modeling and simulation framework in distributed cognitive radio networks
KR101275172B1 (en) * 2012-12-17 2013-06-18 국방과학연구소 Method of simulating discrete event
CN113672207A (en) * 2021-09-02 2021-11-19 北京航空航天大学 X language hybrid model modeling system, method and storage medium
CN113672207B (en) * 2021-09-02 2024-03-26 北京航空航天大学 X language hybrid model modeling system, method and storage medium

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