CN112904709A - Air conditioner control method and air conditioner - Google Patents
Air conditioner control method and air conditioner Download PDFInfo
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- CN112904709A CN112904709A CN202110062464.5A CN202110062464A CN112904709A CN 112904709 A CN112904709 A CN 112904709A CN 202110062464 A CN202110062464 A CN 202110062464A CN 112904709 A CN112904709 A CN 112904709A
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- G05B11/00—Automatic controllers
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- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P.I., P.I.D.
Abstract
The invention provides an air conditioner control method, which comprises the following steps: determining a pressure error value and a pressure error change value based on the actual pressure value and the target pressure value; defining a fuzzy subset, building a discrete domain, and calculating the membership degree of a pressure error value and a pressure error change value to the fuzzy subset; adaptively generating a fuzzy rule table according to the characteristics of the system and the environmental conditions; resolving the blur according to a gravity center method, and determining a proportional parameter increment and an integral parameter increment; and obtaining a control signal of the outdoor fan based on a calculation formula of the PID controller, the proportional parameter increment and the integral parameter increment, and then regulating the speed of the outdoor fan. The air conditioner control method provided by the invention omits the link of manual trial and error in the process of establishing the fuzzy rule table, is beneficial to saving resources and cost, and effectively shortens the operation period of the fuzzy rule table, so that the operation process of the air conditioner is more stable and reliable. The invention also provides an air conditioner adopting the method, which has the advantages of timeliness and stability.
Description
Technical Field
The invention belongs to the technical field of air conditioners, and particularly relates to an air conditioner control method and an air conditioner.
Background
The conventional PID controller is generally adopted to control the air pressure regulation in the conventional air conditioner, has a simple structure and certain robustness, is easy to realize in a control process, and can meet the requirements of most industrial controls. However, in the face of different environmental requirements, a special air conditioner needs to set different target pressures under different working conditions, and in such a case, the traditional PID controller has the defects of control lag, large error, time-varying parameters and model uncertainty to different degrees, and the control process of the PID controller is time-consuming and labor-consuming, and has the problem of easily causing system oscillation.
Disclosure of Invention
Aiming at the technical problems, the invention provides an air conditioner control method and an air conditioner, which can generate a fuzzy rule table in a self-adaptive manner and have timeliness and air conditioner operation stability.
In order to achieve the purpose, the invention adopts the following technical scheme:
an air conditioner control method includes the following steps:
determining a pressure error value and a pressure error change value based on the actual pressure value and the target pressure value;
respectively defining fuzzy subsets of pressure errors and pressure error changes, building discrete domains of the pressure errors and the pressure error changes, and respectively calculating membership degrees of the pressure error values and the pressure error change values to the fuzzy subsets respectively;
generating a fuzzy rule table according to the system characteristics and the environmental conditions;
resolving the blur according to a gravity center method, and determining a proportional parameter increment and an integral parameter increment;
and obtaining a control signal of the outdoor fan based on a calculation formula of the PID controller, the proportional parameter increment and the integral parameter increment, and then regulating the speed of the outdoor fan.
The air conditioner control method provided by the invention can generate the fuzzy rule table in a self-adaptive manner according to the characteristics of the air conditioner system and the environment conditions to set the PID parameters, so that the link of manual trial and error in the process of establishing the fuzzy rule table by the conventional expert experience method is omitted, the resource and the cost are saved, the operation period of the fuzzy rule table is effectively shortened, and the running process of the air conditioner has the advantages of timeliness and stability.
According to some embodiments of the application, the generating of the fuzzy rule table comprises the steps of:
based on the calculation result of the variable membership degree, the variable is solidified into the coordinate axis of the discrete domain;
verifying whether the relation between the pressure error change value in the current period and the pressure error value in the previous period meets a preset condition, if so, determining a domain element and keeping the domain element in a fuzzy rule table; otherwise, the discourse field elements are upgraded and left for next verification.
The generation mode of the fuzzy rule table provided by the invention simplifies unnecessary calculation steps, is beneficial to shortening the operation period and improving the system operation efficiency.
According to some embodiments of the application, the preset condition is: the proportion of the error change value in the current period is more than 40% of the pressure error value in the last period. The design further simplifies the calculation steps of the fuzzy rule table and is beneficial to improving the operation efficiency.
According to some embodiments of the present application, the pressure error value and the fuzzy subset of pressure error variation values are each defined as { NL, NM, NS, ZE, PS, PM, PL }. Wherein N is the lower deviation, P is the upper deviation, L is larger, M is medium, S is smaller, and ZE is the smallest discrete discourse domain element. According to the invention, a 7-order discourse domain is adopted according to the characteristics of the air conditioning system, so that the excessive calculation amount and the excessive software resource occupation caused by excessive fuzzy subsets are prevented; meanwhile, the problem that the fuzzy processing precision is reduced due to too few fuzzy subsets to cause oscillation is avoided, and the stability and the reliability of system operation are ensured.
According to some embodiments of the present application, for coordinate (x, y) in the fuzzy rule table of the scale element, when x ≦ y, the raising order of the domain element is ZE → PS → PM → PL; when x > y, the universe element is upscaled in a manner of ZE → NS → NM → NL. The arrangement of the step-up mode can effectively avoid multiple step-up calculations of high-order discourse field elements in the comparative example link so as to ensure the timeliness of the system.
According to some embodiments of the present application, for the coordinates (x, y) in the fuzzy rule table of the integral element, when x > y, the raising order of the domain element is ZE → PS → PM → PL; when x ≦ y, the universe element is upscaled in a manner of ZE → NS → NM → NL. The arrangement of the order-increasing mode can also effectively avoid carrying out multiple order-increasing calculations on high-order discourse field elements in the integration link so as to ensure the timeliness of the system.
According to some embodiments of the present application, the system will stop the upscaling of the integral adjustment parameter when the absolute value | Ec | of the pressure error change value Ec > PM. The design is adaptive to the function of the integral link, the operation process of the integral link is simplified, and the timeliness of the system is further improved.
According to some embodiments of the present application, a new fuzzy rule table is generated each time the system is started; and in the operation process of the system, the generated fuzzy rule table is applied. The design can ensure the adaptability to the environment in the operation process of the air conditioning system, ensure the stability and the reliability of the operation of the air conditioner, shorten the operation period of the air conditioner and contribute to improving the operation efficiency of the system.
According to some embodiments of the present application, during the operation of the system, the applying of the generated fuzzy rule table further comprises the following steps: judging whether the quantization factor domain is calculated, if so, directly resolving the ambiguity according to a gravity center method; otherwise, returning to the step of executing variable curing, and adjusting the generated fuzzy rule table. Further shortening the operation period and improving the operation efficiency of the air conditioner.
An air conditioner adopts the air conditioner control method.
The air conditioner provided by the invention can generate the fuzzy rule table of the fuzzy controller in a self-adaptive manner according to the characteristics of the air conditioning system and the environmental conditions, completely saves the manual link of generating the fuzzy rule table, is beneficial to saving resources and cost, improves the timeliness of the system, and can obtain more stable system output at the same time, so that the air conditioner has the advantages of stability and reliability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a conventional PID control;
fig. 2 is a flow chart of PID fuzzy control.
FIG. 3 is a schematic diagram of a process of a fuzzy controller;
FIG. 4 is a general flowchart of the air conditioning control method of the present invention;
FIG. 5 is a schematic representation of discrete domain coordinate axes of the present invention;
FIG. 6 is a sub-flow diagram of the present invention for generating a fuzzy rule table;
FIG. 7 is a diagram of the operation of an outdoor fan when a manually formulated fuzzy rule table is applied;
FIG. 8 is a diagram of the operation of an outdoor fan when the self-generated fuzzy rule table of the present invention is applied.
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It should be understood, however, that elements, structures and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In some specific places, the application environment of the air conditioner is complex, so that different target pressures of the compressor need to be set under different working conditions, and the energy efficiency ratio of the air conditioner is ensured to meet the environmental requirement. There are many algorithms for adjusting the pressure of the compressor in the air conditioner in the related art, and the most classical adjustment method is a PID control method.
Fig. 1 is a flow chart of a conventional PID control, which is a main technical means in the current industrial control, as shown in fig. 1, and which is generally based on actual pressure data of an air conditioner, determines a proportional parameter Kp, an integral parameter Ki, and a differential parameter Kd through three links of proportional regulation, integral regulation, and differential regulation, and uses formulas
And adjusting the rotating speed of the air conditioner outdoor unit until the air conditioner pressure reaches the target pressure.
In the calculation process of the target pressure, a proportional link Kp is a control basis, the larger the value of Kp is, the faster the response speed of the system is, but the excessive Kp can cause the overshoot of the system; the integral link Ki can eliminate steady-state errors, but can cause integral saturation; the differential element Kd has the effect of improving the dynamic performance of the system, but prolongs the adjustment time of the system. Therefore, in order to avoid the side effect of the PID controller, the worker needs to continuously perform adjustment test on the PID parameter. The method not only wastes time, but also occupies expensive laboratory resources, and simultaneously, when the error E becomes smaller, the system can oscillate due to the relatively larger parameter value of the PID, and the method cannot adapt to different environmental requirements.
In the face of different environmental requirements, a PID fuzzy control algorithm is usually adopted in the related technology, namely PID parameters are optimized in real time through fuzzy logic, so that the PID parameters most suitable for air conditioner operation can be set according to different working conditions. As shown in fig. 2, since the main function of the differential element is to overcome the hysteresis of the controlled object, and is mainly used for the temperature control system, the differential element Kd is not usually used in the PID fuzzy control algorithm. In the PID fuzzy control process, an error E between target pressure and real-time pressure of an air conditioning system and a pressure error change value Ec are used as input parameters, a Kp value and a Ki value in the PID system are calculated in real time by using a fuzzy algorithm stored in a fuzzy controller, the Kp value and the Ki value are substituted into the PID controller to calculate the required air speed of an outdoor fan, and finally the system is adjusted to reach the target pressure.
The maximum advantage of the fuzzy PID controller is the self-adaption of PID parameters, namely, real-time PID parameters are obtained through fuzzification, fuzzy processing and clarification of the parameters, so that an ideal regulating curve is obtained. Specifically, as shown in fig. 3, the processing procedure of the fuzzy controller includes the following three steps:
the method comprises the following steps: determining the range of the system pressure error E and the error change value Ec, dividing the range, and fuzzifying the E and the Ec by utilizing a membership function;
step two: establishing a fuzzy rule table of a proportional regulation parameter Kp and an integral regulation parameter Ki according to the regulation requirement of the air conditioner, and carrying out fuzzy processing on E and Ec;
step three: the values of Kp and Ki are determined by the gravity center method and substituted into a PID controller to regulate the system pressure.
The core established by the fuzzy controller is the drawing up of a fuzzy rule table. At present, the method for establishing the fuzzy rule table only comprises an expert experience method and a weight calculation method.
Specifically, the expert experience method plans the approximate distribution of discrete domain elements according to traditional experience and system characteristics, then obtains a feedback result of an operation curve of an air conditioner external fan through a laboratory, and further continuously adjusts the fuzzy rule table until a reasonable fuzzy rule table is obtained. The fuzzy rule representation established according to expert experience is as follows:
table 1 existing fuzzy rule table example
The advantage of the expert experience method is that a more stable system curve can be obtained without manually adjusting PID parameters. However, the method has the disadvantages that the fuzzy rule table still needs to be manually set, more experimental resources are occupied, the determined fuzzy rule table cannot adapt to the complex and changeable environment and climate of the industrial air conditioner, if the design of the fuzzy rule table is not reasonable, the data is easy to oscillate, and the adjusting effect is not as good as that of the traditional PID controller.
For the weight calculation method, weights are set for the fuzzy set, the weights are accumulated continuously to obtain a sum signal, and the sum signal is fed back to the control operation unit to obtain a fuzzy rule table. The method has the advantages that the expert experience method is more concise and visual, but the method has the defects that the operation period is longer and more complicated, although the method is suitable for temperature regulation, the pressure regulation of an air conditioning system with high real-time requirement cannot be met, and the setting of the weight is difficult to meet the requirement of an industrial air conditioner on complex environment.
The invention provides an air conditioner control method by improving a fuzzy PID control algorithm, the control method can set the PID parameters by adaptively generating a fuzzy rule table, the parameter values of the fuzzy rule table can be automatically drawn up according to the environmental requirements in the operation of an air conditioner system, the link of manual trial and error is omitted, the operation period of the fuzzy rule table is effectively shortened, the resource and the cost are saved, and the stability and the reliability of the air conditioner pressure regulation are improved.
FIG. 4 is a general flowchart of the air conditioning control method of the present invention; FIG. 5 is a schematic representation of discrete domain coordinate axes of the present invention; FIG. 6 is a sub-flow diagram of the generation of a fuzzy rule table in accordance with the present invention.
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings.
The air conditioner control method provided by the application comprises the following steps:
s1, collecting an actual pressure value;
specifically, the actual pressure value is acquired by a pressure sensor of the air conditioner.
S2, determining input parameters of the fuzzy controller based on the actual pressure value and the target pressure value: a pressure error E and a pressure error variation Ec;
specifically, the actual pressure value p 'is compared with the set target pressure value p in real time to obtain a pressure error E and a pressure error change value Ec, where E is p-p', and Ec is dE/dt. And the pressure error E and the pressure error change value Ec are used as input variables of the fuzzy controller to carry out fuzzy processing.
S3, fuzzy subsets of pressure errors and pressure error changes are respectively defined, discrete domains of the pressure errors and the pressure error changes are built, and membership degrees of the pressure error values and the pressure error change values to the respective fuzzy subsets are respectively calculated;
specifically, the range of the system pressure error E and the pressure error variation Ec is divided into a plurality of intervals, i.e. fuzzy subsets; then calculating formula according to membership degree for parameter x in interval (y, z)
And calculating the membership degree of the pressure error E and the pressure error change value Ec to the fuzzy subset.
In fuzzy systems, discrete domains are mainly divided into the 5 th, 7 th and 9 th domains. For the air conditioning system, too many fuzzy subsets can cause too large calculation amount and occupy too many software resources; too few blur subsets reduce the processing accuracy of the blurring, and easily cause hunting. Thus, through a number of experimental adjustments, as shown in fig. 5, the present embodiment assumes a 7 th-order domain, and in the present embodiment, the fuzzy subsets of pressure error E and pressure error change Ec are each defined as { NL, NM, NS, ZE, PS, PM, PL }. Wherein N is a negative deviation (negative), P is a positive deviation (positive), L is a large (large), M is a medium (middle), S is a small (small), and ZE is a minimum discrete domain element.
S4, generating a fuzzy rule table in a self-adaptive mode according to the system characteristics and the environmental conditions;
specifically, the generation of the fuzzy rule table is the core content of the application, and for the PID regulation of the air-conditioning pressure system, a differential link Kd is generally not needed, so that in the embodiment, the fuzzy rule table is only established for the proportional link Kp and the integral link Ki, unnecessary calculation steps are simplified, the calculation period is favorably shortened, and the operation efficiency is improved.
Because the application scenes of the industrial air conditioner are complex and changeable, the system can clear the fuzzy rule table every time the fan operates for the first time, and the fuzzy rule table is generated again according to the current environment. The first operation of the fan comprises the first electrifying operation of the air conditioning system and the re-operation of the air conditioner after the air conditioner is stopped. The setting can effectively ensure the adaptability of the parameters of the PID controller to the environmental conditions, avoid the occurrence of system oscillation and enable the system to regulate the air-conditioning pressure more stably and reliably.
The establishment of the fuzzy rule table of the present application will be described in detail with reference to fig. 6.
The generation of the fuzzy rule table comprises the following steps:
s41, solidifying the variable into the coordinate axis of the discrete domain of discourse based on the calculation result of the variable membership degree;
in the set-up fuzzy subset coordinates, (x1, y1), (x1, y2), (x2, y1), (x2, y2) are quantization factor domains to which the current coordinates of the system belong if the error E E (x1, x2) between the target pressure and the real-time pressure of the air conditioning system and the error change value Ec E (y1, y 2).
In the fuzzy control system of the present embodiment, it is necessary to deblur the system pressure error E and the pressure error variation Ec by using the gravity center method, which is premised on the fact that the membership degree of the variable is first obtained and that the available data is ensured in the fuzzy rule table. However, in the algorithm strategy of the present embodiment, when the quantization factor field corresponding to the variable is not established, it is impossible to perform the deblurring process on the variable. Therefore, after calculating the membership of the variable, if the discrete domain elements in the quantization factor domain are not obtained, the variable needs to be solidified.
Specifically, the solidification is that the variable is directly indicated to the coordinate in the coordinate axis of the discrete domain according to the membership degree. The curing rule is as follows: and the parameter x is in the interval (y, z), if the membership degree X (y) of x to y is more than or equal to 0.5, x is equal to y, and otherwise, x is equal to z.
In the embodiment, discrete domain elements in the fuzzy rule table are calculated step by step from small to large, so that the coordinate solidification principle cannot cause system overshoot caused by overlarge parameters; also, when the curing results in too small a parameter, a higher calculation frequency in the fuzzy controller can compensate for the calculation error of the initial period. The principle of solidifying the parameters can meet the requirement of the air conditioning system on pressure control, and the system can be ensured to perform fuzzy processing on the parameters according to a gravity center method.
S42, calculating a fuzzy rule table;
the pressure system of the air conditioner has obvious characteristics, for a proportional link Kp: after the air conditioning compressor is operated, the system pressure begins to gradually increase. However, under most working conditions, the outer fan needs to be started until the pressure reaches a set threshold value, and at the moment, the rotating speed of the outer fan needs to be increased as soon as possible to reduce the pressure, so that the shutdown caused by the formation of high-pressure protection is avoided.
The response speed of the system mainly depends on a proportional link Kp, and the larger the Kp value is, the faster the response speed of the system is, so that the Kp value is required to be as large as possible in the initial adjustment stage; with the continuous increase of the wind speed of the outdoor fan, the error change value Ec is smaller and smaller, and at the moment, in order to avoid oscillation caused by system overshoot, the Kp value is gradually reduced; in the later stage of the system gradually tending to be stable, in order to ensure the static and dynamic characteristics of the system, the Kp value needs to be increased.
In the progressive calculation method, all corresponding coordinates should be calculated directly starting from the smallest discrete universe element (i.e., ZE), but this leads to two problems: firstly, in a pressure system of an air conditioner, when the parameter value requirement is large, accumulated errors caused by step-by-step calculation are difficult to be complexly calculated and eliminated in an air conditioner single chip microcomputer with small memory; secondly, the time is excessively occupied by multiple step-by-step calculation, and the timeliness requirement of the starting period of the air conditioning system cannot be met.
As shown in fig. 6, the present application addresses the above problem, and according to the characteristics of the air conditioning system and the change of the environmental conditions, the process of establishing the fuzzy rule table of the proportional link Kp in the present embodiment includes the following steps:
s421, verifying whether the relation between the proportion of the error change value of the current period and the system pressure error value of the previous period meets a preset condition, and if so, executing a step S422; otherwise, go to step S423;
s422, determining discourse domain elements and storing the discourse domain elements in a fuzzy rule table;
and S423, raising the order of the domain elements, and reserving for next verification.
For example, the preset conditions in this process in this embodiment are as follows: the proportion of the error change value Ec of the current period measured by the experiment is more than 40% of the system pressure error value Ep of the previous period, and the PID parameter setting of the previous period can be considered to meet the system regulation requirement when the preset condition is met.
It should be noted that, in the case that the proportion of the current period error variation value does not satisfy the preset condition, step S423 is executed to upgrade the domain element x to x ', when the system encounters the domain element x again, the verification object of the system is the upgraded element x ', and if the element x ' satisfies the preset condition, step S422 is executed to store the element in the fuzzy rule table; if the element x 'still does not satisfy the preset condition, step S423 is continuously executed to upgrade the element x' and is left for the next verification until the preset condition is satisfied.
In the embodiment, for the coordinates (x, y) in the fuzzy rule table of the proportional link Kp, when x is less than or equal to y, the ascending order mode of the domain element is ZE → PS → PM → PL; when x > y, the argument elements are upscaled in a manner of ZE → NS → NM → NL.
Therefore, the fuzzy rule table established by the proportional link Kp according to the embodiment is as follows:
table 2 fuzzy rule table for Kp generated in the present embodiment
Furthermore, the main function of the integral link Ki is to eliminate the steady-state error of the air-conditioning pressure system. When a system sets a new pressure target, in order to avoid large overshoot of the system due to integral saturation, the value of Ki is reduced as much as possible; and as the target pressure and the actual pressure of the system are close to each other, the value of Ki is increased continuously to ensure the adjustment precision of the system.
The fuzzy rule table of Ki in the present embodiment is slightly different from Kp in the way of raising the argument. The integral element Ki mainly acts on the late stage of system regulation, so when the absolute value | Ec | of the error Ec is greater than PM, the system stops raising the order of Ki.
In the present embodiment, for the coordinates (x, y) in the Ki fuzzy rule table, when x > y, the ascending manner of the domain element is ZE → PS → PM → PL; when x is less than or equal to y, the ascending mode of the domain element is ZE → NS → NM → NL.
Therefore, the fuzzy rule table established by the integration element Ki in the embodiment is as follows:
table 3 fuzzy rule table for Ki generated in the present embodiment
Further, in the generation process of the fuzzy rule table according to the present embodiment, the following calculation rule is also provided:
(1) in the discrete universe coordinate axis shown in FIG. 5, for straight lines [ (NL, PL), (PL, NL) ], when the fuzzy rule table calculates the coordinates on the straight line, it means that the current system is relatively stable, and 0 th universe element is used.
For the fuzzy rule table of Kp, the upper deviation increasing step is gradually carried out towards the second quadrant direction, the lower deviation increasing step is gradually carried out towards the fourth quadrant direction, and the increasing step is larger at the position farther from the straight line.
For the fuzzy rule table of Ki, the lower deviation increasing step is gradually carried out towards the second quadrant direction, the upper deviation increasing step is gradually carried out towards the fourth quadrant direction, and the increasing step is also larger at the position farther from the straight line.
Based on the operation rule of the fuzzy rule table, corresponding order increase is firstly carried out on the basic domain elements on the basic fuzzy rule tables of fig. 2 and fig. 3, so that the high-order domain elements can be prevented from being subjected to multiple order increase calculations, and the timeliness of the system is ensured.
(2) When the pressure error of the air conditioning system in the early stage is large, the embodiment is mainly adjusted by Kp; after the pressure error in the later period is gradually reduced, the embodiment mainly plays the role of Ki adjustment to enhance the stability of the system while using Kp to gradually adjust to avoid overshoot.
As shown in table 3, based on the PID control principle, the coordinate values of the basic fuzzy rule table are directly used in the high-order element region in the Ki fuzzy rule table in the present embodiment, so that the unstable condition of the system caused by the over-high level in the Ki rule table set when the pressure error in the early stage is large can be effectively avoided.
The adjustment of the fuzzy rule table of the embodiment based on the above rules effectively shortens the operation period of the fuzzy rule table, so that the air conditioner has the advantages of timeliness and stability in the operation process.
With continued reference to fig. 4-6, the present embodiment determines the control signal directly from the generated fuzzy rule table during operation of the system. During the operation of the system, the application of the fuzzy rule table further comprises the following steps:
s40, judging whether the quantization factor field is calculated, if yes, directly executing the step S5; otherwise, the process returns to step S41.
Specifically, in the system operation process, in order to ensure the timeliness of the system, the embodiment sets that when all calculated discrete domain elements exist in the quantization factor domain, the deblurring process is directly performed, otherwise, the coordinates of the elements which are not calculated need to be solidified, the fuzzy classification table is further adjusted, and the deblurring process is performed based on the adjusted fuzzy rule table.
S5, resolving the blur according to the gravity center method;
the process of data clarification is a process of deblurring data, and the embodiment performs deblurring processing on the data based on a gravity center method. The expression formula of the center of gravity method is as follows:
wherein z isoIs increment delta Kp or delta Ki, z of proportional link Kp or integral link Ki processed by fuzzy controlleriAs a fuzzy subset, u, of the system pressure error E or error variation value Ecc(zi) Is ziDegree of membership.
S6, obtaining a control signal based on a calculation formula of a PID controller, a proportional parameter increment and an integral parameter increment;
specifically, in the present embodiment, the values Δ Kp and Δ Ki are obtained by the center of gravity method and then substituted into the calculation formula of the PID controller, so that the control signal of the outdoor fan can be intelligently output according to the pressure deviation.
And S7, adjusting the wind speed of the outdoor fan according to the control signal.
Specifically, the main control object of the present invention is the pressure of the compressor, and the definition of the control object can effectively avoid the influence of external factors such as altitude, temperature, etc. on the compressor. In the embodiment, the pressure adjusting signal of the compressor is output based on the fuzzy rule table generated in a self-adaptive manner, and the pressure of the compressor is adjusted according to the signal, so that the air speed of the outdoor fan is regulated and controlled to be adaptive to the environmental condition.
In order to further verify the reliability of the air conditioner control method, the embodiment reads the fuzzy rule table automatically generated by the system in a remote monitoring mode under the working condition that the environmental condition is 35 ℃ and the standard atmospheric pressure after the system runs stably according to the control method, as shown in the following
TABLE 4 fuzzy rule table about Kp obtained by remote monitoring
TABLE 5 fuzzy rule table about Ki obtained by remote monitoring
From the above table, it can be seen that the overall distribution range of the domain elements Kp and Ki is similar to that of the fixed fuzzy rule table, but the fuzzy rule table can be finely adjusted at a specific position according to the environmental conditions, so as to achieve better effect of adapting to different environments.
Further, fig. 7 is an operation diagram of the outdoor fan when the artificially formulated fuzzy rule table is applied, and fig. 8 is an operation diagram of the outdoor fan when the self-generated fuzzy rule table of the present invention is applied under the same working condition. The abscissa in fig. 7 and 8 is the operating time of the outdoor fan, and the ordinate is the operating frequency of the fan. The method and the device have the advantages that the operation states of the fuzzy rule table which is manually drawn and is suitable for the same working condition and the operation diagram of the outdoor fan which is suitable for the self-generated fuzzy rule table are intercepted, and the operation states of the fuzzy rule table and the operation diagram are compared, so that the advantages of the air conditioner control method provided by the invention are visually highlighted.
As shown in fig. 7, after the fan is started and the operating frequency reaches the set value, when the demand pressure set by the system becomes smaller, the regulation of the controller generates obvious system oscillation, and the stability and reliability of the operation are poor.
As shown in fig. 8, under the regulation of the adaptively generated fuzzy rule table controller of the present invention, the time taken for the outdoor fan to reach the maximum wind speed (50 Hz in the figure) is shorter, and when the pressure requirement set by the system becomes smaller, the regulation of the controller does not have obvious oscillation, and the operation curve is smoother compared with the controller manually setting the PID parameters.
The comparison can fully prove that the control method has strong adaptability to the change of the system characteristics, and the operation of the outdoor fan under the control of the air conditioner control method provided by the invention has higher stability and reliability.
The invention also provides an air conditioner, which adopts the air conditioner control method described in the above embodiment, and the air conditioner can generate the fuzzy rule table of the fuzzy controller in a self-adaptive manner according to the characteristics of the air conditioning system and the conditions of the external environment after being started, so that the manual link for generating the fuzzy rule table is completely saved, the resource and the cost are saved, and the timeliness of the system is enhanced; meanwhile, the fuzzy rule table generated by the air conditioner in a self-adaptive mode can obtain more stable system output, so that the air conditioner has the advantages of stability and reliability.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.
Claims (10)
1. An air conditioner control method is characterized by comprising the following steps:
determining a pressure error value and a pressure error change value based on the actual pressure value and the target pressure value;
respectively defining fuzzy subsets of pressure errors and pressure error changes, building discrete domains of the pressure errors and the pressure error changes, and respectively calculating membership degrees of the pressure error values and the pressure error change values to the fuzzy subsets respectively;
generating a fuzzy rule table according to the system characteristics and the environmental conditions;
resolving the blur according to a gravity center method, and determining a proportional parameter increment and an integral parameter increment;
and obtaining a control signal of the outdoor fan based on a calculation formula of the PID controller, the proportional parameter increment and the integral parameter increment, and then regulating the speed of the outdoor fan.
2. The air conditioner control method according to claim 1, wherein the generation of the fuzzy rule table comprises the steps of:
based on the calculation result of the variable membership degree, the variable is solidified into the coordinate axis of the discrete domain;
verifying whether the relation between the pressure error change value in the current period and the pressure error value in the previous period meets a preset condition, if so, determining a domain element and keeping the domain element in the fuzzy rule table; otherwise, the discourse field elements are upgraded and left for next verification.
3. The air conditioner control method according to claim 2, wherein the preset condition is: the proportion of the error change value in the current period is more than 40% of the pressure error value in the last period.
4. The air conditioning control method according to claim 2, wherein the fuzzy subset of pressure error values, the fuzzy subset of pressure error variation values are each defined as { NL, NM, NS, ZE, PS, PM, PL }. Wherein N is the lower deviation, P is the upper deviation, L is larger, M is medium, S is smaller, and ZE is the smallest discrete discourse domain element.
5. The air conditioner control method according to claim 4, wherein, for the coordinate (x, y) in the fuzzy rule table of the scale element, when x ≦ y, the raising manner of the domain element is ZE → PS → PM → PL; when x > y, the universe element is upscaled in a manner of ZE → NS → NM → NL.
6. The air conditioner control method according to claim 4, wherein, for the coordinates (x, y) in the fuzzy rule table of the integral element, when x > y, the raising manner of the domain element is ZE → PS → PM → PL; when x ≦ y, the universe element is upscaled in a manner of ZE → NS → NM → NL.
7. The air conditioning control method according to claim 6, wherein when the absolute value | Ec | > PM of the pressure error change value Ec, the system stops the step-up of the integral adjustment parameter.
8. The air conditioner control method according to claim 1, wherein a new fuzzy rule table is generated every time a system is started; and in the system operation process, the generated fuzzy rule table is applied.
9. The air conditioner control method according to claim 8, wherein the applying of the generated fuzzy rule table during the system operation further comprises the steps of:
judging whether the quantization factor domain is calculated, if so, directly resolving the ambiguity according to a gravity center method; otherwise, returning to the step of executing variable curing, and adjusting the generated fuzzy rule table.
10. An air conditioner characterized by employing the air conditioning control method as set forth in any one of claims 1 to 9.
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