KR20000024516A - Fuzzy-VAV DDC control method to improve indoor environment in IB( Intelligent Building) - Google Patents

Abstract

PURPOSE: An indoor temperature controlling method is provided to control the indoor temperature in an intelligent building equipped with a VAV(Variable Air Volume conditioning) by judging the indoor temperature based on the detected value of a thermistor and outputting a control signal according to the judged result. CONSTITUTION: A method comprises the steps of: purge-varying the errors of the detected temperature of a temperature detecting sensor input to a central control unit and the predetermined target temperature, the change of the errors and input and output variables thereof; purge-assuming by using the values input from a VAV and the central control unit of hot water and cooling and heating plate, purge assuming rules and various purge functions; and controlling the VAV by non-purging the result of purge-assuming output from the central control unit. Herein, the response to the set up temperature is performed promptly and the temperature is kept in uniform state. Therefore, the management of energy and the pleasant indoor condition are satisfied by using a purge-controller.

Description

Fuzzy-VAV DDC control method to improve indoor environment in IB (Intelligent Building)}

In general, the VAV controller compares the set temperature with the current room temperature, and aims to control the valve of the air conditioner so as to supply the air volume that reaches the set temperature quickly but does not bother the noise.

As a conventional VAV controller, a simple comparison type controller or a PI controller is common. However, it is difficult to expect superior characteristics of simple comparison controller. So, as an alternative to this, the PI controller as shown in FIG. 1 was used to provide improved control characteristics.

Here, the general form of the PI controller is as follows.

At this time, Denote proportional gain and integral gain, respectively.

In the PI controller that calculates the manipulated value by multiplying the error and the derivative of the error by the appropriate gain, the parameter to be adjusted is basically There are two types, and it is common to control the VAV at a predetermined time interval or a time interval dependent on the execution of the program according to the program in which the determined parameters are embedded.

In this way, the control response is slow and in order to control the non-linear indoor temperature, it takes a lot of time and effort to tune the PI controller gain value to obtain a good characteristic, and the gain value also changes when the control environment is changed. I have a problem that requires tuning again. However, in practical application, considering that there are many cases where the control object (indoor environment) is different for each controller, the PI controller is not an efficient controller. In addition, if the PI controller gain value is not appropriate, there is a lot of transients to maintain the set temperature, which causes problems in maintaining a pleasant environment or effective energy cost management.

Accordingly, the present invention has been drawn to solve the above-mentioned problems, and processing the detected temperature signal of the thermistor input to the controller to the error and the change of the error with the set target temperature in the controller using fuzzy variables and fuzzy inference rules The setpoint airflow value is calculated, and the next step is to compare the calculated airflow value with the current airflow and use the error and the change of the airflow as the input values to be controlled using the fuzzy airflow variable and the fuzzy airflow inference rule. By doing so, it aims to control the VAV valve angle according to the optimum control situation.

1 is a general VAV control block diagram

2 is a block diagram of a VAV control according to the present invention.

3 is the structure of the fuzzy controller

4 is a time response of a control object to a control input value.

5 is a simulation of VAV control by a general PI controller.

6 is a simulation of VAV control by the present invention (fuzzy control algorithm)

7 is a comparison test result between a general PI controller and a controller according to the present invention (fuzzy control algorithm).

8 shows a general PI controller over time and a VAV trajectory controlled by a controller according to the present invention (fuzzy control algorithm).

The present invention, in order to control the room temperature in the Intelligent Building (IB) equipped with Variable Air Volume conditioning (VAV), after the control unit judges based on the detection value of the thermistor and outputs a control signal based on the result to control the room temperature It is a VAV purge control algorithm that controls the VAV using fuzzy variables and fuzzy inference rules so that the control object can obtain good temperature characteristics of the room temperature which is nonlinear and difficult to model mathematically.

Hereinafter, with reference to the accompanying drawings will be described an embodiment of the present invention;

1 is a control block diagram of a general VAV system, FIG. 2 is a VAV control block diagram according to the present invention, FIG. 3 is a structure of a fuzzy controller, FIG. 4 is a time response of a control object to a control input value, and FIG. 5 is a general PI. VAV control simulation by the controller, FIG. 6 is a VAV control simulation by the present invention (purge control algorithm), FIG. 7 is a comparison experiment result of the general PI controller and the controller by the present invention (purge control algorithm), FIG. A general PI controller over time and a VAV trajectory controlled by a controller according to the present invention (purge control algorithm) are shown.

Looking at the configuration of the fuzzy controller can be shown as shown in FIG.

It is a step of fuzzy function that converts a specific value into an appropriate language variable suitable for human judgment using the error between the detected temperature of the temperature detection sensor and the preset target temperature, the change of the error and the air volume as the input variables. In the next process, the converted value is introduced to the human mind, and the output of the controller is judged. This fuzzy inference part takes into account the characteristics of the PI controller showing good characteristics in the industry and expresses it as a language variable.

In order to construct the fuzzy control rule part of the fuzzy controller, the input / output relationship of the P1 controller is expressed by Equation (2).

To find the output of the controller at each interrupt time, take the derivative of equation (2) with respect to time,

It can be expressed as. here, ego, to be. Also, enter For Wow The relationship of is shown in Figure 4.

For input in Figure 4 Wow Is expressed as a language variable by dividing n into specific sections.

here Is a linguistic variable representing the process state, Is a control variable. and Is the fuzzy value of the state variable Is the control value.

Inference is needed to derive new facts from a given fuzzy control rule and input values.

There are various fuzzy inferences, but the inference method applied to the present invention is Mandani's Minimum operation.

In the next process, since the fuzzy value is not a value that can be directly input to the system to be controlled, a non-fuzzy method of converting the inferred fuzzy value into a clear value that can be most appropriately expressed is required. The center of area method was used, which tended to show superior performance over the non-fuzzy method.

The center of gravity method finds the center of gravity of the synthesized output fuzzy set and uses the center of gravity coordinates as the control input.

n = discretization level of the whole set of control variables

Obtained as

In order to verify the characteristics of the fuzzy controller configured as described above, a computer simulation was performed, and as a result, FIGS. 5 and 6 were attached. In addition, in order to examine the characteristics of the actual temperature control, the experiment was performed in IB (Jongno Tower) equipped with VAV, and as a result, Figure 7 and Figure 8 are attached.

The present invention, which performs temperature control as described above, uses a fuzzy logic controller that has more similarities to the characteristics of human thinking or natural language than the existing logic system, and provides fast response to the set temperature. It is effective to maintain proper energy management and comfortable condition by maintaining temperature.

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Claims (2)

In the temperature control method provided in the VAV, hot water, and heating / heating central heating control device, an error and a change of the error between the detected temperature of the temperature detection line input to the central control device and a predetermined target temperature, and fuzzy for each input / output variable Parameterizing; Fuzzy inference using values inputted from the central control unit (VAV), hot water, heating / cooling ondol, fuzzy inference rule, and various fuzzy functions; And controlling the VAV by defusing the result of the fuzzy inference output from the central controller. In the VAV temperature control method, a command value air volume is obtained by a change of a predetermined target temperature and an error, and the command value VAV control is performed using the thus obtained command value air volume value with a change of an error and an error from the current air volume.