JPH0460333A - Control system for air conditioner - Google Patents

Abstract

PURPOSE:To realize a fuzzy control most suitable for each of operating state by a method wherein a degree of establishment of a membership function for a former part is applied to a control rule, a weighing of a membership function for a latter part is carried out, a control output is attained in response to a value of weighing and at the same time the membership function at the former part and/or latter part is changed. CONSTITUTION:A difference between a room temperature T from a temperature sensor 3 and a room temperature S set at a room temperature setting device 4 is received from an output part 5 and then a micro-computer 6 perform a fuzzy inference. That is, a degree of establishment of the membership function at the former part is calculated. Weighing of the membership function at the latter part is carried out in response to a fuzzy inference rule from a degree of establishment of the membership function at the former part. Characteristic of the fuzzy inference is changed by a cooling operation and a heating operation. Accordingly, at the time of cooling operation and the time of heating operation, a fuzzy inference most suitable for each of the operations can be carried out.

Description

Detailed Description of the Invention (a) Industrial Application Field The present invention relates to a control system for an air conditioner that uses fuzzy inference to control the operating capacity of a compressor. This is related to fuzzy inference.

(b) Conventional technology In general, as a conventional fuzzy control device, Japanese Patent Application Laid-Open No. 1-14
There was something that was completely described in Publication No. 2902. What is described in this publication is 'A fuzzy control method for an automobile that is equipped with a fuzzy control section that performs fuzzy control of a controlled object such as a propulsion system of an automobile by a processor installed in the automobile. and a readout control unit of a storage control device, the storage device is configured to calculate the detected amount and the temporal change in the controlled target portion in advance according to a predetermined rule, and store the results as a table;
A read control section of the storage device reads the table at a predetermined time and controls the controlled section.

It was something.

By configuring the fuzzy controller in this way,
When a processor is given a detected value, it can perform fuzzy control simply by reading out a value corresponding to the detected value from a table. That is, the processor does not need to perform fuzzy inference, which takes processing time, on each detected value, and has the advantage that the processing time required for fuzzy control in the processor can be shortened.

(c) Problems to be Solved by the Invention In the conventional technology configured as described above, it is possible to shorten the processing time of the processor by using a table. It was a pre-made item. Therefore, there is a problem in that the same fuzzy inference is always applied even when the controlled object part takes on a plurality of operating states. For example, when the object to be controlled is an air conditioner, the way the user perceives the room temperature is generally different when the air conditioner is in cooling mode and when it is in heating mode. Control suitable for each operating condition could not be performed sufficiently.

In order to solve these problems, the present invention provides an air conditioner that changes the characteristics of fuzzy inference according to the operating state of the controlled object and performs optimal fuzzy control according to each operating state. This provides a control method.

(d) Means for Solving the Problems The present invention is a fuzzy control used for controlling the operation of an air conditioner. The membership function of the consequent part is weighted by applying the degree of establishment of the membership function of the consequent part to the control rule, and the control output is obtained according to the weighting of the membership function of the consequent part. In addition, the membership function of the antecedent part and/or the consequent part is changed between when the air conditioner is in cooling operation and when it is in heating operation.

When the air conditioner is in cooling operation, some of the membership functions of the antecedent part and/or the consequent part are shifted in the direction where the control output can be negative, or
The shapes of the membership functions of the antecedent part and/or the consequent part during heating operation are changed so that the control output can go negative.

In addition, the fuzzy control calculates the degree of validity of the membership function of the antecedent part based on the room temperature, and applies the degree of validity of the membership function of the antecedent part to the control rule to calculate the membership function of the consequent part. Weighting is performed, and a control output is obtained according to the weighting of the membership function of the consequent part, and a part of the control rule is changed depending on whether the air conditioner is in a cooling operation or a heating operation. That's what happens.

(e) Effect: By using the air conditioner control system configured as described above, the characteristics of the fuzzy inference can be changed in accordance with the operating state of the air conditioner.

(f) Examples Examples of the present invention will now be described based on the drawings. FIG. 1 is a schematic diagram when a fuzzy control device is applied to temperature control of a conditioned room. In this figure, 1 is a temperature-controlled room, and 2 is an air conditioning unit that is installed in this room and is capable of cooling and heating operations, and discharges cooled air or heated air from its outlet. This system cools or heats the room to be conditioned. This air conditioning unit can change its cooling capacity or heating capacity by using an inverter device, which will be described later. 3 is a temperature sensor attached to be able to detect the room temperature of the conditioned room 1, and uses a detection element such as a thermistor whose internal resistance value changes depending on the temperature. Reference numeral 4 denotes a room temperature setting device, which outputs a signal corresponding to the set temperature. There are various types of room temperature setting devices, such as those that use a variable resistor to change the resistance value, those that use a voltage generator to change the output voltage, and those that output a code or signal corresponding to the set value. Can be used.

Reference numeral 5 denotes a detected value output unit, which calculates the corresponding room temperature T from the change in the internal resistance value of the temperature sensor 3, and calculates the difference between this room temperature T and the room temperature S set in the room temperature setting device 4, ``e=, T -3" (in addition, when performing heating operation, "e=-(T-
3)".Also, the difference e゛ found last time and the difference C found this time.
The difference e and Δe are calculated and the difference e and Δe are output to the microcomputer 6. The microcomputer 6 uses this difference e and 80 to output the It is calculated and output to the impark device 11.

This inverter device 11 includes a microcomputer 6
The operating capacity of the air conditioning unit 2 is variably controlled based on the signal given from the air conditioning unit 2. For example, air conditioning unit 2
When a compressor using an induction motor as a drive source is used, the operating capacity of the air conditioning unit 2 can be changed by changing the frequency of the AC power supplied to the induction motor. Alternatively, if a compressor using a DC motor as the drive source is used, the operating capacity of the air conditioning unit 2 can be changed by changing the voltage of the DC power supplied to the DC motor. Note that 7 to 10 are storage units, and in this embodiment, one memory element is divided into a plurality of areas, which are distributed to the storage units 7 to 10.

The operation of the microcomputer 6 will be explained below. The microcomputer 6 performs fuzzy inference using the above e and Δe. Figure 2 shows an example of a membership function (isosceles triangle), and this membership function should be set as a function given the values of A and C on the horizontal axis and the maximum degree of establishment B on the vertical axis. I can do it. In other words, (1) when e < A, the degree to which the membership function holds true is 0; (2) when A≦e<(A+C)/2, the degree to which the membership function holds true is (2BX(eA)
)/(C-A), (3)(A+C)/2≦e×C, the degree of membership function is (2BX(e-C)
))/(C-A), (4) In C50, the degree of establishment of the membership function is O. In this way, A shown in Figure 2
, B, and C, a function representing this membership function can be set. Therefore, if the value of e is obtained, the degree to which the membership function holds can be determined. Note that the membership function is not limited to an isosceles diagonal shape, and may be arbitrarily set based on the object to be controlled and the five senses of the user, such as an equilateral triangle, trapezoid, or bell shape, but a shape that is easy to convert into a function is preferable.

FIG. 3 is a characteristic diagram showing the membership function of the antecedent part with respect to the difference e between the room temperature and the set value in the cooling operation mode. Respective membership functions NB, NS, 20.
PS and PB are shown in Figure 2 (7) A.

Constants corresponding to B and C are set, and functions representing the respective membership functions are set from these constants. A list of constants representing these functions is shown in FIG. In addition, the membership function ZO represents a set in which there is no difference e, the membership function PS represents a set in which the difference e is felt to be slightly on the positive side, and the membership function PB represents a set in which the difference e is felt to be slightly on the positive side. The membership function NS represents the set for which the difference e is a little on the negative side, and the membership function NB represents the set for which the difference e is a little on the negative side.
It represents a set that is felt to be largely on the negative side.

FIG. 3 shows the membership function of the antecedent part in the heating operation mode, and like the membership function shown in FIG. 3, a list of constants is shown in FIG.

Therefore, the degree to which each of the membership functions of the antecedent part holds true for the difference e can be calculated from the above equation using the constant of the cooling operation mode or heating operation mode for each function of the membership function NB-FB. can.

Although both the shape and the arrangement of the membership functions of the antecedent part shown in FIGS. 3 and 4 are changed, only one of them may be changed.

Furthermore, the membership function of the antecedent part can be similarly set for the rate of change Δe of the difference C.

In this case, the constant of the function representing the membership function may be changed between the cooling operation mode and the heating operation mode, or the same -
It is also possible to use the constant . Generally, when setting a large variation range of Δe, it is preferable to use different constants in the cooling operation mode and heating operation mode, and when the variation range of Δe is small, the same constant can be used, but this is not limiting.

Note that five types of membership functions from NB to PB are set for the membership function for 80, similar to the membership function for e.

Figure 6 is a relationship diagram of tuning that weights the membership function of the consequent (rules used in fuzzy inference)
, and the degree of membership function establishment for e and Δ
The membership function of the consequent part is weighted based on the degree to which the membership function holds true for e.

The membership function of the consequent part is 7 up to the function NB-FB.
set. The membership function zO represents a set that does not change the operating capacity of the air conditioning unit 2 shown in FIG. 1, the membership function PS represents a set that slightly increases the operating capacity of the air conditioning unit 2, and the membership function P
M represents a set that moderately increases the operating capacity of air conditioning unit 2, and membership function PB represents air conditioning unit 2.
The membership function NS represents a set that greatly increases the operating capacity of the air conditioning unit 2, the membership function NM represents a set that slightly decreases the operating capacity of the air conditioning unit 2, and the membership function NM represents a set that moderately decreases the operating capacity of the air conditioning unit 2. , membership function NB represents a set that greatly reduces the operating capacity of the air conditioning unit 2. Incidentally, the horizontal axis of these membership functions sets the increase/decrease in the operating capacity of the air conditioning unit 2.

The following 17 rules are set as fuzzy inference rules.

(1) If the membership function NB regarding e holds true and the membership function NB regarding Δe holds true, then the membership function NB of the consequent part holds true.

(2) If the membership function NS regarding e does not hold true, then the membership function NM of the consequent part holds true.

(17) If the membership function PB regarding e holds true and the membership function PB regarding Δe holds true, then the membership function PB of the consequent part holds true.

Figure 7 is a tuning relationship diagram showing other characteristics,
If the relationship shown in FIG. 6 is for cooling operation, the relationship shown in FIG. 7 is for heating operation. In this diagram, the difference from the relationship diagram shown in Figure 6 is that the rule (1) above is defined as "If the membership function NS with respect to e holds and ΔC
If the membership function NS of the consequent holds true, then the membership function NS of the consequent part holds.''
The point is that we have deleted the rules when the membership function PB regarding e holds true and the membership function zO regarding Δe holds, and when the membership function zO regarding e holds true and the membership function PB regarding Δe holds. . By changing the tuning relationship in this way, the weighting on the membership function NS of the consequent part is reduced, as is the weighting on PB,
Overall, the characteristics of fuzzy inference change in a direction that does not significantly increase the operating performance of the air conditioner.

FIG. 8 is a characteristic diagram of the membership function of the consequent part weighted by the tuning relationship diagram of FIG. 6 or 7. FIG. In this figure, the horizontal axis is the output, that is, the increase/decrease value of the operating capacity of the air conditioning unit 2. Similarly to the membership function of the antecedent part, the function representing the membership function NB-FB of the consequent part can be determined by defining three constants A, B, and C. Among these constants, A and C are set in advance from the membership function of the consequent part shown in FIG. Similar to the membership function of the antecedent part, these constants may also be stored in two types of constants, one for the cooling operation mode and the other for the heating operation mode, and may be selected and used according to the operating state of the air conditioning unit. Note that the constant B represents a weight value and is a value set according to the above rules (1) to (17).

In this figure, constants A=f2', B=y13'C=f4
The membership function represented by ' (the membership function indicated by the dotted line) is the membership function NS during heating operation.
is the function to be replaced, and the membership function NS
has been moved to the left.

FIG. 9 is an explanatory diagram showing the values of constants A and C of the membership function of the consequent part. Note that a value weighted according to the tuning relationship diagram is used as the constant B. The constant values of the membership function NS are different between the cooling operation mode and the heating operation mode. That is, the left side of the membership function NS column in Figure 9 (A=f2.C=F4)
is the constant used in the cooling operation mode, and the right side (A=f'
2', C=f4') is a constant used in the heating operation mode.

Next, the main operations of the microcomputer 6 will be explained using the flowchart shown in FIG. First step SO
Start fuzzy inference from . This fuzzy reasoning is performed at predetermined intervals (once every 30 seconds) as part of the control of the air conditioning unit.
This is done in proportion to the number of degrees. Therefore, the operating capacity of the air conditioning unit is changed at predetermined intervals. Next, in step S1, a determination is made as to whether it is cooling operation or not, that is, it is determined whether to use the data of the cooling operation mode or the data of the heating operation mode. The cooling operation mode and heating operation mode are distinguished not only by whether the air conditioning unit is set to cooling operation or heating operation, but also by incorporating seasonal changes into the switching factors.

It goes without saying that the number of modes may be two or more, including an intermediate mode. Next, step S2,
In step S3, data corresponding to the mode is read from the storage section. In step S2, the constants for cooling operation shown in FIG. 5, the data showing the tuning relationship on the cooling operation side shown in FIG. 6, and the constants for cooling operation shown in FIG. This is to set a function that represents the membership function. Further, in step S3, a constant for heating operation is similarly read from the storage section to set a function for heating operation.

Next, in step S4, the value of e1Δe is input, and in step S5, the degree of establishment of each membership function is calculated.0 For example, in the cooling operation mode, C=el, Δe=Δe1
Then, each function representing the membership function of the antecedent part (a function using data of the cooling operation mode)
When l and Δc1 are substituted, for C, as shown in Fig. 10, the degree to which the membership function zO holds true is E2, the degree to which the membership function PS holds true becomes El, and the degree to which the membership functions NB, NS, and PB hold true are It is 0. Regarding Δe, as shown in Fig. 12, the degree of establishment of the membership function zO is 8E2, and the membership function P
The degree of establishment of S becomes ΔE1, and the membership function NB
, NS, and PB have a degree of establishment of 0.

Next, in step S6, the membership function NB- of the consequent part
Perform FB weighting. From Figures 11 and 12, the membership functions that hold the antecedent part are the membership function Zo and the membership function PS for e, and the membership function zO and the membership function PS for Δe. . Therefore, the rule for the shaded area as shown in No. 13115!0 can be applied. This rule is the rule explained using FIG. That is,
In the consequent part, a membership function zO, a membership function PS, and a membership function PM are established. The value of each weighting is E2XΔE2. E1 x 8E2 (EIX
ΔE2<E2XΔE1 (Adopt the value of EIXΔE2)
, EIXΔE1.

In this example, the degree of establishment of the membership function of the antecedent part is represented by byte data of 0 to FF (hexadecimal), and the weighting of the membership function of the consequent part is expressed as θ to FFFF (
Although the data is handled as word data in hexadecimal (hexadecimal) format to facilitate calculations on the microcomputer 6, the degree of establishment and weighting may also be converted to values between 0 and 1. At this time, the constant y stored in the storage section is set to a value of θ to 1.

In this way, the weighting value of the membership function of the consequent part obtained is set as the constant B of this membership function, and the respective membership functions are synthesized using constants A and C shown in Fig. 9. A composite figure as shown in FIG. 14 is obtained.

Next, in step S7, the center of gravity of the composite figure shown in FIG. 14 is calculated. The center of gravity is the constant A of the membership function before composition.
, B (weighting is a value), and c. This calculated center of gravity is G. Next, in step S8, a value Δf1 on the horizontal axis corresponding to this center of gravity is obtained. This 8f1 is outputted to the inverter device 11 as the amount of change in the capacity of the air conditioning unit. When the frequency of the AC power source that supplies the operating capacity of the air conditioning unit 2 is changed, 8f1 corresponds to the frequency increase/decrease value.

Also, when heating operation is performed, constants and rules for heating operation are set in step S3, so e=el
, Δe=Δe1, even if the detected value is the same value as during cooling operation, the fourth
The degree to which the membership function PS of the antecedent shown in the figure holds true increases. Therefore, the final output (the value of increase/decrease in the operating capacity of the air conditioning unit) becomes larger on the positive side compared to the time of cooling operation.

In this way, the characteristics of the fuzzy inference are changed between cooling operation and heating operation, so even if the same detected value is input, the thickness of the output will differ between cooling operation and heating operation. Therefore, during cooling operation, fuzzy inference can be set to be suitable for cooling operation, and during heating operation, fuzzy inference can be set to be suitable for heating operation. It allows fuzzy inference.

(G) Effects of the Invention As described above, the present invention provides fuzzy control for use in controlling the operation of air conditioners. Weighting the membership function of the consequent by applying the degree of establishment of the membership function of to the control rule,
The control output is obtained according to the thickness of the weighting of the membership function of the consequent part, and the membership of the antecedent part and/or the consequent part is By changing the arrangement and shape of the functions or the control rules, the fuzzy inference characteristics can be changed between cooling and heating operations of the air conditioner. Therefore, fuzzy inferences suitable for both cooling and heating operations can be set, and optimal fuzzy inferences can be performed for both cooling and heating operations, thereby improving the comfort of the conditioned room.

[Brief explanation of the drawing]

Fig. 1 is a schematic diagram showing the outline of the present invention, Fig. 2 is a characteristic diagram showing an example of a membership function (isosceles triangle), and Fig. 3 is a characteristic diagram showing an example of a membership function (isosceles triangle).
The figure is a characteristic diagram of the membership function of the antecedent part in cooling operation mode, Figure 4 is a characteristic diagram of the membership function of the antecedent part in heating operation mode, and Figure 5 is a characteristic diagram of the membership function of the antecedent part in cooling operation mode and heating operation mode. A correspondence diagram of the constants of the function representing the membership function of the subject part, Figure 6 is a diagram related to weighting the membership function of the consequent part, and Figure 7 shows the membership of the consequent part used in heating operation mode. Related diagrams when weighting functions. Figure 8 is a characteristic diagram of the membership function of the consequent part. Figure 9 is a correspondence diagram of the constants of the function representing the membership function of the consequent part. Figure 10 is a micrograph. A flowchart showing the operation of fuzzy control by a computer, FIG. 11 is an explanatory diagram showing the degree of establishment of the antecedent part when C-C1,
FIG. 12 is an explanatory diagram showing the degree of establishment of the antecedent part when 80=Δe1, FIG. 13 is an explanatory diagram showing the membership function for weighting when e-el and Δe=Δc1, and FIG. The figure is a diagram of a figure obtained by synthesizing the membership functions of the consequent parts that have been fully weighted according to FIG. 13. 1... Conditioned room, 2... Air conditioning unit, 3.
...Temperature sensor, 4...Room temperature setter, 5...Output section, 6...Microcomputer -7 to 10...
Storage unit, 11... Inverter device.

Claims (4)

[Claims] (1) A refrigeration cycle is constructed using a compressor, a condenser, a pressure reducing device, and an evaporator whose operating capacity can be changed arbitrarily, and the compressor is operated based on the difference between the room temperature of the conditioned room and the set temperature. When controlling the operating capacity of the compressor of an air conditioner configured to air condition a room by changing its capacity, the room temperature of the room to be conditioned is detected, and this detected value is used as a control rule based on empirical rules. In a control method using fuzzy control based on The membership function of the consequent part is weighted by applying it to the control rule, and the control output is obtained according to the weighting of the membership function of the consequent part. A control method for an air conditioner characterized in that the membership function of the antecedent part and/or the consequent part is changed between the heating operation and the heating operation. (2) When the air conditioner is in cooling operation, some of the membership functions of the antecedent part and/or the consequent part are shifted in a direction in which the control output is negative. A control method for an air conditioner according to item 1. (3) When the air conditioner is in cooling operation, the form of the membership function of the antecedent part and/or consequent part is changed to the form of the membership function of the antecedent part and/or consequent part when the air conditioner is in heating operation. 2. The control method for an air conditioner according to claim 1, wherein the control output is changed in a direction in which the control output is decreased. (4) A refrigeration cycle is constructed using a compressor, a condenser, a pressure reducing device, and an evaporator whose operating capacity can be changed arbitrarily, and the compressor is operated based on the difference between the room temperature of the conditioned room and the set temperature. When controlling the operating capacity of the compressor of an air conditioner configured to air condition a room by changing its capacity, the room temperature of the room to be conditioned is detected, and this detected value is used as a control rule based on empirical rules. In a control method using fuzzy control based on The membership function of the consequent part is weighted by applying it to the control rule, and the control output is obtained according to the weighting of the membership function of the consequent part. A control method for an air conditioner, characterized in that a part of the control rule is changed between during heating operation and during heating operation.