PID (Proportion Integration Differentiation) parameter calibration method and device of mass flow controller

Abstract

The invention provides a PID parameter calibration method and device of a mass flow controller, the mass flow controller and a computer readable storage medium, and the PID parameter calibration method comprises the steps: obtaining a current PID parameter, carrying out the flow control through the current PID parameter, and obtaining a current characteristic parameter of a current flow set point, the current characteristic parameters comprise current response time and a current overshoot ratio; under the condition that the current response time is larger than a preset time threshold value and / or the current overshoot proportion is not located in a preset proportion range, the current PID parameter is adjusted based on a preset fuzzy control algorithm, and the steps of obtaining the current PID parameter, conducting flow control through the current PID parameter and obtaining the current characteristic parameter of the current flow set point are returned; and when the current response time is less than or equal to a preset time threshold and the current overshoot proportion is within a preset proportion range, recording and storing the current PID parameter.

Description

Technical Field

[0001] The present disclosure relates to the technical field of mass flow control, and in particular to a PID parameter calibration method and device for a mass flow controller, a mass flow controller, and a computer-readable storage medium. Background Art

[0002] Mass Flow Controller (MFC) can precisely measure and control gas or liquid, and is widely used in semiconductor, fuel cell, solar energy, vacuum coating, analytical instrument and other industries. Due to the wide variety of mass flow meters, mechanical diversity, and high real-time control requirements, it is difficult for designers to find an accurate flow control model. At present, MFC usually uses PID (Proportion, Integral, Differential) control algorithm for flow control. Summary of the invention

[0003] The present invention provides a PID parameter calibration method and device for a mass flow controller, a mass flow controller, and a computer-readable storage medium.

[0004] In a first aspect, the present disclosure provides a method for calibrating PID parameters of a mass flow controller, the method comprising:

[0005] Obtaining current PID parameters and performing flow control using the current PID parameters, and obtaining current characteristic parameters of the current flow set point, wherein the current characteristic parameters include a current response time and a current overshoot ratio;

[0006] When the current response time is greater than a preset time threshold, and / or the current overshoot ratio is not within a preset ratio range, the current PID parameters are adjusted based on a preset fuzzy control algorithm, and the current PID parameters are returned to the step of obtaining the current PID parameters and performing flow control by using the current PID parameters to obtain the current characteristic parameters of the current flow set point;

[0007] When the current response time is less than or equal to the preset time threshold and the current overshoot ratio is within a preset ratio range, the current PID parameters are recorded and saved.

[0008] In some embodiments, the current PID parameters include a current proportional coefficient, a current integral coefficient, and a current differential coefficient. The step of adjusting the current PID parameters based on a preset fuzzy control algorithm includes:

[0009] Taking the input parameters corresponding to each coefficient as the input quantity and the increment of each coefficient as the output quantity, based on the preset fuzzy control algorithm, the current target increment corresponding to each coefficient is determined according to the input parameters corresponding to each coefficient; wherein the input parameters corresponding to the current proportional coefficient and the current integral coefficient respectively include the current response time and the current overshoot ratio, and the input parameters corresponding to the current differential coefficient include the current overshoot ratio;

[0010] The current proportional coefficient, the current integral coefficient, and the current differential coefficient are adjusted respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient, and the current target increments corresponding to the coefficients.

[0011] In some embodiments, the steps of adjusting the current proportional coefficient, the current integral coefficient, and the current differential coefficient respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient, and the current target increments corresponding to the coefficients include:

[0012] updating the current proportionality coefficient according to the sum of the current proportionality coefficient and the current target increment corresponding to the current proportionality coefficient; and

[0013] updating the current integral coefficient according to the sum of the current integral coefficient and the current target increment corresponding to the current integral coefficient; and

[0014] The current differential coefficient is updated according to the sum of the current differential coefficient and the current target increment corresponding to the current differential coefficient.

[0015] In some embodiments, based on a preset fuzzy control algorithm, the step of determining the current target increment corresponding to each coefficient according to the input parameters corresponding to each coefficient based on the preset fuzzy control algorithm includes:

[0016] For each coefficient of the current proportional coefficient, the current integral coefficient and the current differential coefficient, a fuzzy operation is performed on the input parameter corresponding to the coefficient to obtain the membership degree corresponding to the input parameter;

[0017] Obtaining a fuzzy relationship between an input parameter corresponding to the coefficient and an output quantity corresponding to the coefficient from a fuzzy rule base, and performing a fuzzy reasoning operation on the output quantity corresponding to the coefficient based on the fuzzy relationship to obtain a fuzzy matrix corresponding to the fuzzy relationship;

[0018] According to the membership degree corresponding to the input parameter corresponding to the coefficient and the fuzzy matrix, obtaining the membership degree corresponding to the output quantity corresponding to the coefficient;

[0019] Based on the membership degree corresponding to the output amount corresponding to the coefficient, a clarification operation is performed on the output amount corresponding to the coefficient to obtain the current target increment corresponding to the coefficient.

[0020] In some embodiments, the step of performing a fuzzy operation on the input parameter corresponding to the coefficient to obtain the degree of membership corresponding to the input parameter includes:

[0021] Based on the domain mapping function, the input parameter is converted into a value on the corresponding standard domain;

[0022] Based on the membership function corresponding to the input parameter, the membership corresponding to the input parameter is obtained according to the value on the standard domain corresponding to the input parameter.

[0023] In some embodiments, the step of performing a clarification operation on the output amount corresponding to the coefficient based on the degree of membership corresponding to the output amount corresponding to the coefficient to obtain the current target increment corresponding to the coefficient includes:

[0024] Defuzzification is performed on the output quantity corresponding to the coefficient according to the degree of membership corresponding to the output quantity corresponding to the coefficient and the standard domain corresponding to the output quantity corresponding to the coefficient, so as to obtain the output value of the output quantity corresponding to the coefficient in the corresponding standard domain;

[0025] The output value of the output quantity corresponding to the coefficient in the corresponding standard domain is subjected to domain inverse transformation processing to obtain the current target increment corresponding to the coefficient.

[0026] In some embodiments, before the steps of obtaining current PID parameters, performing flow control by using the current PID parameters, and obtaining current characteristic parameters of the current flow set point, the method further comprises:

[0027] The measuring range of the mass flow controller is divided into a plurality of flow intervals, wherein different flow intervals correspond to different PID parameters, and the flow rate of the flow set point in any flow interval is controlled based on the corresponding PID parameters.

[0028] In some embodiments, when the current response time is greater than a preset time threshold, and / or the current overshoot ratio is not within a preset ratio range, before adjusting the current PID parameter based on a preset fuzzy control algorithm, the method further includes: determining whether the current accumulated adjustment times of the current PID parameter is less than a preset calibration times;

[0029] When the current accumulated adjustment times of the current PID parameter is less than the preset calibration times, a step of adjusting the current PID parameter based on a preset fuzzy control algorithm is performed.

[0030] In a second aspect, the present disclosure provides a PID parameter calibration device for a mass flow controller, the device comprising:

[0031] An acquisition unit, which is configured to acquire current PID parameters and perform flow control by using the current PID parameters, and acquire current characteristic parameters of a current flow set point, wherein the current characteristic parameters include a current response time and a current overshoot ratio;

[0032] an adjusting unit, configured to adjust the current PID parameters based on a preset fuzzy control algorithm when the current response time is greater than a preset time threshold and / or the current overshoot ratio is not within a preset ratio range, and trigger the acquiring unit to reacquire the current PID parameters and perform flow control by using the current PID parameters, and acquire the current characteristic parameters of the current actual flow of the current flow set point;

[0033] The recording unit is configured to record and save the current PID parameters when the current response time is less than or equal to a preset time threshold and the current overshoot ratio is within a preset ratio range.

[0034] In a third aspect, the present disclosure provides a mass flow controller, which includes a flow sensor, a valve and a controller, wherein the controller controls the valve based on PID parameters, and the PID parameters are calibrated by the PID parameter calibration method described in the first aspect above.

[0035] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the PID parameter calibration method described in the first aspect is implemented.

[0036] According to the technical solutions of the PID parameter calibration method and device of the mass flow controller, the mass flow controller, and the computer-readable storage medium provided in the embodiments of the present disclosure, the response characteristics of the flow rate at the current flow setting point of the mass flow controller MFC are detected as the basis for calibrating the PID parameters, and the PID parameters are iteratively calibrated through fuzzy control, which is conducive to achieving high-precision calibration of the PID parameters, improving the accuracy of the PID parameter calibration of the MFC, and ensuring the response characteristics of the flow rate at the current flow setting point of the MFC; and the PID parameter calibration method of the embodiments of the present disclosure does not require manual calibration, which can effectively avoid the artificial calibration errors caused by manual calibration, and effectively improve the efficiency of PID parameter calibration.

[0037] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing detailed example embodiments with reference to the accompanying drawings, in which:

[0039] Figure 1 A flow chart of a PID parameter calibration method for a mass flow controller provided in an embodiment of the present disclosure;

[0040] Figure 2 It is a schematic diagram of actual flow rate change of an MFC;

[0041] Figure 3 It is a schematic diagram of actual flow rate change of another MFC;

[0042] Figure 4 is a flow chart of a method for determining a current target increment of a current PID parameter;

[0043] Figure 5 It is an execution block diagram of a fuzzy control algorithm;

[0044] Figure 6 is a schematic diagram of a membership function of a response time T;

[0045] Figure 7 is a schematic diagram of a membership function of an overshoot ratio K;

[0046] Figure 8 A schematic diagram of a membership function of an increment of a proportional coefficient;

[0047] Fig. 9 A schematic diagram of a membership function of an integral coefficient increment;

[0048] Fig.10 A schematic diagram of a membership function of an increment of a differential coefficient;

[0049] Fig.11 A structural block diagram of a PID parameter calibration device for a mass flow controller provided in an embodiment of the present disclosure;

[0050] Fig.12 A structural block diagram of a mass flow controller provided by an embodiment of the present disclosure;

[0051] Fig.13 A block diagram of a composition of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0053] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0054] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0055] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "including" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0056] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0057] In the related art, PID parameters are usually calibrated manually, and the calibration accuracy in the manual calibration method has human errors, because the PID parameters need to be corrected based on previous human experience, which will cause the quality of the calibration results to be limited by human factors, thereby affecting the control accuracy and production efficiency of the MFC.

[0058] In order to effectively solve the technical problems existing in the above-mentioned related technologies, the embodiments of the present disclosure provide a PID parameter calibration method and device for a mass flow controller, a mass flow controller, and a computer-readable storage medium.

[0059] Figure 1A flow chart of a PID parameter calibration method for a mass flow controller provided in an embodiment of the present disclosure.

[0060] The present disclosure provides a method for calibrating PID parameters of a mass flow controller (MFC), such as Figure 1 As shown, the PID parameter calibration method of the mass flow controller includes:

[0061] Step S11, obtaining current PID parameters and performing flow control by using the current PID parameters, and obtaining current characteristic parameters of the current flow setting point, the current characteristic parameters including the current response time and the current overshoot ratio.

[0062] In the disclosed embodiment, a mass flow controller (MFC) is used to precisely measure and control the gas mass flow rate. Exemplarily, the mass flow controller may include a flow sensor, a valve and a controller. The valve may be a piezoelectric valve. The controller may be used to control the opening and closing size of the valve to control the actual output flow rate of the valve. The flow sensor may be used to detect the actual output flow rate. Among them, the controller may use a PID (Proportion, Integral, Differential) control algorithm to control the actual output flow rate. The PID control algorithm refers to a control algorithm that combines the three links of proportion, integration and differentiation. It has the advantages of high response speed and small oscillation, so as to further improve the response speed of the mass flow controller to the change of the gas mass flow rate and quickly make corresponding adjustment actions.

[0063] Since the MFC valve changes from a closed state to an open state in a process from static to dynamic, the control is very difficult. When the output flow is not zero, the MFC valve is always in an active state, and the control difficulty of the MFC is relatively easy. Therefore, in order to reduce the control difficulty, the valve can be adjusted and controlled in sections from a closed state to different open states. In this way, the PID parameters in different intervals need to be calibrated separately, so as to achieve the effect of segmented control of the MFC flow.

[0064] The range of the mass flow controller is divided into a plurality of flow intervals, wherein different flow intervals correspond to different PID parameters, and the flow rate of the flow set point in any flow interval is obtained by control based on the corresponding PID parameters. For example, according to experience, the general PID parameters of MFC correspond to four segmented intervals, namely the full process flow interval [0%, 6%], (6%, 13%], (13%, 26%], (26%, 50%], (50%, 100%], wherein different flow intervals correspond to different PID parameters, and the corresponding segmented controlled flow rates are 0% to 5%, 0% to 10%, 0% to 25%, 0% to 50%, 0% to 75%, 0% to 100%. For example, when the flow set point of MFC is in the interval of 50% to 100%, the PID parameters corresponding to the interval are used for interval flow control, so that the output flow of the mass flow controller reaches the corresponding flow set point, and the set value of the PID parameter is controlled according to the proportional range such as 0 to 75% and 0 to 100% during calibration.

[0065] After obtaining the current PID parameters corresponding to the flow interval where the current flow set point is located, and performing flow control on the mass flow controller MFC based on the set current PID parameters, the current characteristic parameters of the current flow set point can be obtained by detecting the actual output flow of the MFC. The current characteristic parameters include the current response time T and the current overshoot ratio K.

[0066] Figure 2 is a schematic diagram of actual flow change of an MFC, such as Figure 2 As shown in the figure, the horizontal axis represents time Time, and the vertical axis represents the actual flow Flow. The response time T refers to the time taken for the actual flow of the MFC to change from the initial flow set point SetPoint1 to the target flow set point (current flow set point) SetPoint2 for the first time. When the response time T exceeds the preset time threshold (maximum response time T Max ), the flow response performance of the MFC needs to be adjusted.

[0067] Figure 3 is a schematic diagram of actual flow change of another MFC, such as Figure 3As shown, the horizontal axis represents time Time, and the vertical axis represents the actual flow rate Flow, wherein the overshoot ratio refers to the ratio of the excess flow value to the target flow rate set point SetPoint2 when the maximum value of the actual flow rate exceeds the set target flow rate set point (current flow rate set point), that is, K=ΔF / SetPoint2, ΔF is the difference between the maximum value of the actual flow rate and the target flow rate set point. When the overshoot ratio K is not within the preset ratio range (the range allowed by the K value), it is necessary to adjust the flow rate response performance of the MFC; illustratively, the allowable range of the K value can be 0-1%.

[0068] like Figure 2 and Figure 3 As shown in Figure 2 As shown, where the overshoot ratio K = 0, the response time T <T Max , it means that the response time T and overshoot ratio K of the actual flow of MFC are in line with the expected indicators, the response characteristics of the actual flow of MFC are good, and the PID parameters corresponding to the flow interval of the current flow set point are appropriate, so there is no need to adjust the parameters. If the actual flow of MFC changes like Figure 3 As shown, the overshoot ratio K>1%, the response time T>T Max , it means that the response time T and overshoot ratio K of the actual flow of MFC do not meet the expected indicators, the response characteristics of the actual flow of MFC are poor, and the PID parameters corresponding to the flow range where the current flow set point is located are not appropriate, so parameter adjustment is required.

[0069] Step S12: When the current response time is greater than a preset time threshold and / or the current overshoot ratio is not within a preset ratio range, the current PID parameters are adjusted based on a preset fuzzy control algorithm, and the process of returning to the step of obtaining the current PID parameters and controlling the flow rate through the current PID parameters, and obtaining the current characteristic parameters of the current flow set point, is performed.

[0070] When the current response time T is greater than the preset time threshold T MAX It means that the response time T of the actual flow rate of MFC does not meet the expected index, and the response characteristics of the actual flow rate of MFC do not meet the expectations. Therefore, it is necessary to adjust the PID parameters corresponding to the flow interval where the current flow set point is located.

[0071] When the current overshoot ratio K is not within the preset ratio range, it means that the overshoot ratio K of the actual flow of MFC does not meet the expected index, and the response characteristics of the actual flow of MFC do not meet expectations. Therefore, it is necessary to adjust the PID parameters corresponding to the flow interval where the current flow set point is located.

[0072] In the embodiment of the present disclosure, fuzzy control adjustment is performed on the current PID parameters based on a preset fuzzy control algorithm, so as to control the flow of the MFC based on the adjusted current PID parameters.

[0073] After adjusting the current PID parameters corresponding to the flow interval where the current flow set point is located based on the preset fuzzy control algorithm, return to execute step S11 to continue to control the flow through the current PID parameters corresponding to the flow interval where the adjusted current flow set point is located, and continue to detect the response characteristics of the actual flow of the MFC, so as to continue to iterate and optimize the PID parameters corresponding to the flow interval where the current flow set point is located, and achieve the effect of dynamically and adaptively adjusting the current PID parameters corresponding to the flow interval where the current flow set point is located, thereby achieving dynamic adjustment of the response characteristics of the actual flow of the MFC, until the response characteristics of the actual flow of the MFC meet the expected effect.

[0074] Step S13: When the current response time is less than or equal to the preset time threshold and the current overshoot ratio is within a preset ratio range, record and save the current PID parameters.

[0075] When the current response time T is less than or equal to the preset time threshold T MAX , and when the current overshoot ratio K is within the preset ratio range, it means that the response time T and overshoot ratio K of the actual flow of MFC meet the expected indicators, the response characteristics of the actual flow of MFC are good, and the PID parameters corresponding to the flow interval where the current flow set point is located are appropriate. Therefore, there is no need to adjust the parameters. The current PID parameters corresponding to the flow interval where the current flow set point is located are recorded and saved, and then the flow control of MFC can continue to be based on the current PID parameters corresponding to the flow interval where the next flow set point is located.

[0076] According to the technical solution of the PID parameter calibration method of the mass flow controller provided by the embodiment of the present disclosure, the response characteristics of the flow rate at the current flow setting point of the MFC are detected as the basis for calibrating the PID parameters, and the PID parameters are iteratively calibrated through fuzzy control, which is conducive to achieving high-precision calibration of the PID parameters, improving the accuracy of the PID parameter calibration of the MFC, and ensuring the response characteristics of the flow rate at the current flow setting point of the MFC; and the PID parameter calibration method of the embodiment of the present disclosure does not require manual calibration, which can effectively avoid the artificial calibration error caused by manual calibration, and effectively improve the efficiency of PID parameter calibration.

[0077] In some embodiments, the current PID parameters include a current proportional coefficient, a current integral coefficient and a current differential coefficient. The step of adjusting the current PID parameters based on a preset fuzzy control algorithm, i.e., step S12, may further include: taking the input parameters corresponding to each coefficient as the input quantity and the increment of each coefficient as the output quantity, based on the preset fuzzy control algorithm, determining the current target increment corresponding to each coefficient according to the input parameters corresponding to each coefficient; wherein the input parameters corresponding to the current proportional coefficient and the current integral coefficient respectively include the current response time and the current overshoot ratio, and the input parameters corresponding to the current differential coefficient include the current overshoot ratio; and adjusting the current proportional coefficient, the current integral coefficient and the current differential coefficient respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient and the current target increment corresponding to each coefficient.

[0078] It can be understood that the current target increment corresponding to each coefficient includes: the current target increment corresponding to the current proportional coefficient, the current target increment corresponding to the current integral coefficient, and the current target increment corresponding to the current differential coefficient. The increment can also be understood as a change amount, and its value can be positive or negative.

[0079] Specifically, for the current proportional coefficient, the current response time and the current overshoot ratio are used as input, and the increment of the current proportional coefficient is used as output. Based on the preset fuzzy control algorithm, the current target increment corresponding to the current proportional coefficient is determined according to the current response time and the current overshoot ratio.

[0080] Similarly, for the current integral coefficient, the current response time and the current overshoot ratio are used as input quantities, and the increment of the current integral coefficient is used as output quantity. Based on the preset fuzzy control algorithm, the current target increment corresponding to the current integral coefficient is determined according to the current response time and the current overshoot ratio.

[0081] Similarly, for the current differential coefficient, the current overshoot ratio is used as the input, and the increment of the current differential coefficient is used as the output. Based on the preset fuzzy control algorithm, the current target increment corresponding to the current differential coefficient is determined according to the current overshoot ratio.

[0082] In some embodiments, the steps of adjusting the current proportional coefficient, the current integral coefficient and the current differential coefficient respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient and the current target increments corresponding to each coefficient may further include: updating the current proportional coefficient P according to the sum of the current proportional coefficient P and the current target increment ΔP corresponding to the current proportional coefficient P; and, updating the current integral coefficient I according to the sum of the current integral coefficient I and the current target increment ΔI corresponding to the current integral coefficient I; and, updating the current differential coefficient D according to the sum of the current differential coefficient D and the current target increment ΔD corresponding to the current differential coefficient D.

[0083] In other words, the current proportional coefficient P after adjustment and update is equal to the sum of the current proportional coefficient P before adjustment and update and the current target increment ΔP corresponding to the current proportional coefficient P, the current integral coefficient I after adjustment and update is equal to the sum of the current integral coefficient I before adjustment and update and the current target increment ΔI corresponding to the current integral coefficient I, and the current differential coefficient D after adjustment and update is equal to the sum of the current differential coefficient D before adjustment and update and the current target increment ΔD corresponding to the current differential coefficient D.

[0084] Figure 4 is a flow chart of a method for determining the current target increment of the current PID parameter, Figure 5 is an execution block diagram of a fuzzy control algorithm. In some embodiments, Figure 4 and Figure 5 As shown, based on the preset fuzzy control algorithm, the step of determining the current target increment corresponding to each coefficient according to the input parameters corresponding to each coefficient may further include:

[0085] Step S41: for each coefficient of the current proportional coefficient, the current integral coefficient and the current differential coefficient, a fuzzy operation is performed on the input parameter corresponding to the coefficient to obtain the membership degree corresponding to the input parameter.

[0086] Taking the current proportionality coefficient as an example, the input parameters corresponding to the current proportionality coefficient include the current response time and the current overshoot ratio. In step S41, fuzzy operation is performed on the current response time and the current overshoot ratio to obtain the membership degrees corresponding to the current response time and the current overshoot ratio respectively.

[0087] Taking the current integral coefficient as an example, the input parameters corresponding to the current integral coefficient include the current response time and the current overshoot ratio. In step S41, fuzzy operation is performed on the current response time and the current overshoot ratio to obtain the membership degrees corresponding to the current response time and the current overshoot ratio respectively.

[0088] Taking the current differential coefficient as an example, the input parameter corresponding to the current differential coefficient includes the current overshoot ratio. Then, in step S41, a fuzzy operation is performed on the current overshoot ratio to obtain the membership degree corresponding to the current overshoot ratio.

[0089] like Figure 5 As shown in the figure, the fuzzy control algorithm can be divided into three parts: fuzzification operation, fuzzy rule base processing, and clarification operation. Among them, fuzzification operation refers to the mapping of the observed value in the actual domain of the input quantity to the standard domain, which includes two parts: scale transformation and fuzzification processing. Among them, scale transformation refers to the linear conversion of the observed value of the input quantity into the value in the standard domain, and fuzzification processing refers to the process of calculating the membership of the value obtained by scale transformation.

[0090] In the embodiment of the present disclosure, the input quantities involved in the fuzzy control algorithm include the current response time T and the current overshoot ratio K, and the output quantities include the increments ΔP, ΔI, and ΔD of the PID parameters. Before the fuzzy control algorithm starts, it is necessary to first determine the domain of each input quantity and output quantity. The domain refers to the discussion range of parameters in the fuzzy control process.

[0091] For example, the actual domain of the current response time T can be set to [0, 3], the actual domain of the current overshoot ratio K can be set to [0%, 5%], the standard domains corresponding to the current response time T and the current overshoot ratio K can be set to {-3, -2, -1, 0, 1, 2, 3}, and the actual domains of ΔP, ΔI, and ΔD can be set to [P min , P max ]、[I min , I max ]、[D min , D max ], the standard domains corresponding to ΔP, ΔI, and ΔD can all be set to {-3, -2, -1, 0, 1, 2, 3}.

[0092] In some embodiments, the step of performing fuzzy operation on the input parameter corresponding to the coefficient to obtain the membership degree corresponding to the input parameter may further include: based on the domain mapping function, converting the input parameter into a value on the corresponding standard domain; based on the membership function corresponding to the input parameter, obtaining the membership degree corresponding to the input parameter according to the value on the standard domain corresponding to the input parameter.

[0093] Furthermore, for the current proportional coefficient and the current integral coefficient, based on the domain mapping function, the current response time and the current overshoot ratio are respectively converted into values ​​on the corresponding standard domain; based on the membership function corresponding to the current response time, the membership corresponding to the current response time is obtained according to the value on the standard domain corresponding to the current response time; and, based on the membership function corresponding to the current overshoot ratio, the membership corresponding to the current response time is obtained according to the value on the standard domain corresponding to the current overshoot ratio.

[0094] For the current differential coefficient, based on the domain mapping function, the current overshoot ratio is converted into a value on the corresponding standard domain; based on the membership function corresponding to the current overshoot ratio, the membership corresponding to the current response time is obtained according to the value on the standard domain corresponding to the current overshoot ratio.

[0095] In some embodiments, the domain mapping function can be configured as the following formula:

[0096]

[0097] Where x* represents the actual input quantity, such as the current response time T or the current overshoot ratio K, x min * and x max *represents the actual domain [x min * ,x max * ], the minimum and maximum values ​​in x min and x max They represent the standard domain [x min ,x max ] in the minimum and maximum values.

[0098] According to the above domain mapping function, linear scaling can be performed to obtain the corresponding values ​​x of the current response time T and the current overshoot ratio K in the corresponding standard domain. For example, according to actual production experience, the actual domain of the current response time T is selected as [0, 3], the unit of the value in the actual domain is second S, and the standard domain is set to {-3, -2, -1, 0, 1, 2, 3}, then x min *=0,x max * = 3, x min =-3,x max =3, if the input current response time T is x*=1, substituting it into the above domain mapping function can obtain the value x=-1 corresponding to the current response time T in the corresponding standard domain. Similarly, the value corresponding to the current overshoot ratio K in the corresponding standard domain can be obtained.

[0099] In some embodiments, the membership function corresponding to the current response time may be configured as the following formula:

[0100]

[0101] Among them, μ(x) represents the membership degree of each value in the standard domain, x i is the value of the i-th element in the standard domain.

[0102] Exemplarily, the value x=-1 corresponding to the current response time T in the corresponding standard domain is obtained. According to the above membership function, in the standard domain, when x i When =-1, μ(x)=1, when xi≠-1, μ(x)=0, and the membership degree corresponding to x is μ(x)={0,0,1,0,0,0,0}.

[0103] In some embodiments, the membership function corresponding to the current overshoot ratio may be the same as the membership function corresponding to the current response time. Similarly, the membership corresponding to the current overshoot ratio may be obtained according to the membership function corresponding to the current overshoot ratio.

[0104] Step S42: Obtain the fuzzy relationship between the input parameter and the output quantity corresponding to the coefficient from the fuzzy rule base, and perform fuzzy reasoning operation on the output quantity corresponding to the coefficient based on the fuzzy relationship to obtain a fuzzy matrix corresponding to the fuzzy relationship.

[0105] like Figure 5 As shown, the fuzzy rule base includes a database and a rule base. The database stores the membership function of the input and output quantities with respect to the fuzzy language, and the rule base records the rules between the fuzzy languages.

[0106] Database design: The observation quantities are T and K, where T is the response time of the actual flow and K is the overshoot ratio of the actual flow; the control quantities are ΔP, ΔI, and ΔD, which respectively represent the increments of the proportional coefficient, integral coefficient, and differential coefficient of PID control; the observation quantities T and K can be configured with five fuzzy sets {NB, NS, ZO, S, B}. Correspondingly, in the database, the membership function of the response time T can be established as follows: Figure 6 The membership function shown in Figure 2 is used to establish the membership function of the overshoot ratio K. Figure 7 The membership function shown.

[0107] The fuzzy set corresponding to the control variable ΔP can be configured as {NB, NM, NS, ZO, S, M, B, SB}, and the membership function can be configured as follows: Figure 8 The membership function shown in FIG. 1 is a fuzzy set corresponding to ΔI, and the fuzzy set corresponding to ΔI can be configured as {NB, NM, NS, ZO, S, M, B, SB}. The membership function can be configured as follows: Fig. 9 The membership function shown in FIG. 1 is a fuzzy set corresponding to ΔD, which can be configured as {NB, NS, ZO, S, B}, and the membership function can be configured as follows: Fig.10 The membership function shown.

[0108] Rule base design: There are three fuzzy rule bases, namely TK-ΔP, TK-ΔI, and K-ΔD. The TK-ΔP rule base indicates that the proportional link P value is affected by the response time T and the overshoot ratio K. As shown in Table 1, the proportional coefficient P value will increase with the increase of the response time T, and decrease with the increase of the overshoot ratio K; the TK-ΔI rule base indicates that the integral link I value is affected by the response time T and the overshoot ratio K. As shown in Table 2, the larger the response time T, the smaller the corresponding integral coefficient I value; the KD rule base indicates that the differential link D value is affected by the overshoot ratio K value. As shown in Table 3, the differential coefficient D value will increase with the increase of the overshoot ratio K.

[0109] Table 1

[0110]

[0111] Table 2

[0112]

[0113] Table 3

[0114]

[0115] According to the above fuzzy rule base, the fuzzy relationship between the input parameter and the output quantity corresponding to the coefficient (such as the current proportional coefficient, the current integral coefficient or the current differential coefficient) can be obtained. For example, in combination with Table 1, Table 2 and Table 3, the fuzzy relationship between the current response time T, the current overshoot ratio K and the increment ΔP of the current proportional coefficient P in the current PID parameter is obtained. The fuzzy relationship is "If T=ZO AND K=NB then ΔP=M".

[0116] After the fuzzy relationship is determined, in order to facilitate calculation, the fuzzy set of the input parameter and output quantity corresponding to the coefficient (such as the current proportional coefficient, the current integral coefficient or the current differential coefficient) in the fuzzy relationship can be represented by a matrix to construct the fuzzy matrix corresponding to the fuzzy relationship. Specifically, after the fuzzy relationship is determined, based on the fuzzy relationship, fuzzy reasoning operations are performed using the matrices corresponding to the fuzzy sets of the input parameter and output quantity corresponding to the coefficient in the fuzzy relationship, thereby obtaining the fuzzy matrix corresponding to the fuzzy relationship.

[0117] For example, the input parameters are response time T, overshoot ratio K, and the output is the increment ΔP of the current proportional coefficient P. Assume that the actual domain of the current response time T is [0, 1.5, 3.0] (s), the actual domain of the current overshoot ratio K is [0, 2.5%, 5%], and the actual domain of the increment ΔP of the current proportional coefficient P is [-0.1, 0.1]. The standard domains corresponding to T and K are {-1, 0, 1}, and the standard domain corresponding to ΔP is {-1, 1}. When there is a fuzzy relationship "If T=ZO AND K=NB thenΔP=M" in the rule base, T=ZO can be set as matrix A, K=NB can be set as matrix B, and ΔP=M can be set as matrix C. The fuzzy matrix corresponding to the fuzzy relationship can be configured as R=(AXB) T XC; where, assume A = [0.2, 1, 0.2]; B = [1, 0.5, 0]; C = [0.2, 0.8].

[0118] It should be noted that matrices A, B, and C can actually be calculated based on the corresponding membership functions. For the convenience of calculation, the matrix data here are simplified, which does not mean that the matrix data here are matrix data calculated based on the actual corresponding membership functions.

[0119] According to the calculation formula of the fuzzy matrix above,

[0120] Furthermore, the AXB matrix is ​​expanded into a column vector to obtain a vector D (3*3 arranged in rows): D=[0.2, 0.2, 0, 1, 0.5, 0, 0.2, 0.2, 0].

[0121] Furthermore, the fuzzy matrix corresponding to the fuzzy relationship is

[0122]

[0123] The above exemplifies the process of performing fuzzy inference operations on output quantities based on fuzzy relations to obtain a fuzzy matrix corresponding to the fuzzy relations. The embodiments of the present disclosure may include but are not limited to calculating the fuzzy matrix corresponding to the fuzzy relations in the above manner.

[0124] Step S43: Obtain the membership degree corresponding to the output quantity corresponding to the coefficient according to the membership degree corresponding to the input parameter corresponding to the coefficient and the fuzzy matrix.

[0125] For example, when the current response time T=1.2 and the current overshoot ratio K=2% are input, substituting into the above domain mapping function, we can get x T =-0.2,x K =-0.2; x T =-0.2,x K =-0.2 Substituting into the membership function corresponding to the above current response time, we can get the membership corresponding to input T and K respectively: μ(x T )={0,1,0},μ(x K )={0,1,0}.

[0126] Let A*=[0,1,0], B*=[0,1,0], A* represents the membership of response time T μ(x T ), B* represents the membership of the overshoot ratio K μ(x K ),but

[0127] Further, the A*XB* matrix is ​​expanded to a column vector D* as follows: D*=[0,0,0,0,1,0,0,0,0], and the membership of the proportional coefficient increment ΔP is obtained as follows: E*=D*XR=[0.2,0.5], that is, the membership of ΔP μ(E*)={0.2,0.5}, where R is the fuzzy matrix obtained according to the above fuzzy reasoning operation.

[0128] Similarly, the degree of membership of the integral coefficient increment ΔI and the degree of membership of the differential coefficient increment ΔD can be calculated, which will not be described in detail here.

[0129] Step S44: perform a clarification operation on the output corresponding to the coefficient based on the degree of membership corresponding to the output corresponding to the coefficient to obtain the current target increment corresponding to the coefficient.

[0130] like Figure 5 As shown, the clarification operation can also be called the defuzzification operation. The defuzzification operation refers to the transformation of the inferred fuzzy language into actual digital quantities, which is divided into two parts: defuzzification processing and domain inverse transformation processing.

[0131] In some embodiments, the step of performing a clarification operation on the output quantity corresponding to the coefficient based on the degree of membership corresponding to the output quantity corresponding to the coefficient to obtain the current target increment corresponding to the coefficient may further include: defuzzifying the output quantity corresponding to the coefficient according to the degree of membership corresponding to the output quantity corresponding to the coefficient and the standard domain corresponding to the output quantity corresponding to the coefficient to obtain the output value of the output quantity corresponding to the coefficient in the corresponding standard domain; performing a domain inverse transformation on the output value of the output quantity corresponding to the coefficient in the corresponding standard domain to obtain the current target increment corresponding to the coefficient.

[0132] In some embodiments, the defuzzification method includes the maximum membership method, the centroid method and the weighted average method. Further, in order to make the output variation (increment) smoother, the centroid method can be used for defuzzification.

[0133] In some embodiments, based on the defuzzification function, the output quantity is defuzzified according to the membership degree corresponding to the output quantity and the standard domain corresponding to the output quantity to obtain the output value of the output quantity in the corresponding standard domain.

[0134] In some embodiments, the defuzzification function may be configured as follows:

[0135]

[0136] Among them, y is the output value of the output obtained by defuzzification processing in the corresponding standard domain, y i are the values ​​in the standard domain corresponding to the output quantity, μ(y i ) is y i The corresponding membership degree.

[0137] Exemplarily, assuming that the standard domain corresponding to the increment ΔP is {-3, -2, -1, 0, 1, 2, 3}, the obtained membership of the increment ΔP is μ(y) = {0, 0.2, 0.8, 0.3, 0, 0, 0}, and the output value y of the output after defuzzification processing in the corresponding standard domain is obtained by substituting it into the above defuzzification function as follows:

[0138]

[0139] In some embodiments, based on the inverse universe transformation function, an output value of the output quantity in the corresponding standard universe is subjected to an inverse universe transformation process to obtain a current target increment.

[0140] In some embodiments, the inverse universe transformation function may be configured as the following formula:

[0141]

[0142] Among them, y is the output value of the output obtained after defuzzification in the corresponding standard domain,

[0143] y min and max They represent the standard domain [y min ,y max ], the minimum and maximum values ​​in y min * and y max * respectively indicates that the actual domain corresponding to the configuration y is [y min *,y max *], y* represents the output value corresponding to the output in the actual domain, that is, the current target increment.

[0144] According to the above domain inverse transformation function, a linear inverse transformation is performed to obtain the output value y* corresponding to the output in the actual domain, that is, the current target increment. For example, when y = -0.92, assuming that the standard domain corresponding to the increment ΔP is [-3, 3], and the actual domain is [-0.1, 0.1], that is, y max =3,y min =-3, y max * = 0.1, y min *=-0.1, and substituting it into the above domain inverse transformation function, the output value corresponding to the increment ΔP in the actual domain, that is, the current target increment y*, can be obtained as follows:

[0145]

[0146] Exemplarily, in the calculation process of the membership μ(E*) of the above exemplary increment ΔP, when the fuzzy rule has only one "If T=ZO AND K=NB then ΔP=M", the input response time T=1.2, the overshoot ratio K=2%, the membership μ(E*) of the increment ΔP is calculated to be {0.2,0.5}, assuming that the standard domain corresponding to the increment ΔP is {-3, -2, -1, 0, 1, 2, 3}, substituting it into the above defuzzification function, the output value y=0.42 of the increment ΔP in the corresponding standard domain can be obtained, and further, the output value y=0.42 of the increment ΔP in the corresponding standard domain is substituted into the above domain inverse transformation function, and the target increment ΔP can be obtained as y*(ΔP)=0.042.

[0147] In some embodiments, the step of obtaining the current characteristic parameters of the current flow setting point includes: obtaining the current flow response curve of the current flow setting point; and obtaining the current response time and the current overshoot ratio of the current flow setting point according to the current flow response curve.

[0148] Among them, the response time refers to the time taken for the actual flow in the current flow response curve to change from the initial flow setting point to the target flow setting point (i.e., the current flow setting point) for the first time; the overshoot ratio refers to the ratio of the excess flow value to the target flow setting point when the highest value of the actual flow in the current flow response curve exceeds the set target flow setting point.

[0149] In some embodiments, the current flow response curve of the current flow set point can be obtained by detecting the actual flow rate output by the MFC. For example, the current flow response curve is as follows: Figure 2 or Figure 3 The curve shown.

[0150] According to the current flow response curve, the time taken for the actual flow of the MFC to change from the initial flow set point SetPoint1 to the target flow set point (ie, the current flow set point) SetPoint2 for the first time can be determined, thereby determining the current response time T.

[0151] According to the current flow response curve, it is possible to determine the excess flow value ΔF when the maximum value of the actual flow exceeds the set target flow set point (i.e., the current flow set point). ΔF is the difference between the maximum value of the actual flow and the target flow set point. By further calculating the ratio of the excess part to the target flow set point SetPoint2, the current overshoot ratio K=ΔF / SetPoint2 can be obtained.

[0152] In some embodiments, before the steps of obtaining current PID parameters, performing flow control through the current PID parameters, and obtaining current characteristic parameters of the current flow set point, the PID parameter calibration method further includes: dividing the range of the mass flow controller into multiple flow intervals, wherein different flow intervals correspond to different PID parameters, and the flow rate of the flow set point in any flow interval is obtained by control based on the corresponding PID parameters.

[0153] In some embodiments, when the current response time is greater than a preset time threshold, and / or the current overshoot ratio is not within a preset ratio range, before adjusting the current PID parameters corresponding to the flow interval where the current flow set point is located based on a preset fuzzy control algorithm, the PID parameter calibration method also includes: determining whether the current accumulated adjustment times of the current PID parameters are less than a preset calibration times n.

[0154] When the current cumulative adjustment times of the current PID parameters are less than the preset calibration times, the steps of adjusting the current PID parameters based on the preset fuzzy control algorithm are executed; when the current cumulative adjustment times of the current PID parameters are greater than or equal to the preset calibration times, it means that the current fuzzy rules cannot calibrate the PID parameters well, and it is necessary to manually check whether the hardware equipment is intact and whether there is any air leakage in the pipeline. If the environment is normal, the fuzzy rules need to be checked and improved to continue the PID parameter calibration.

[0155] In some embodiments, since PID parameter calibration and fuzzy control algorithm processing need to rely on the control effect of the actual flow rate, before performing PID parameter calibration, it is necessary to build the gas circuit in advance and build the MFC test gas circuit to determine the response characteristics of the actual flow rate controlled by PID through actual gas circulation tests.

[0156] In some embodiments, before PID parameter calibration, a parameter initialization step is also included, and the initialized parameters may include PID parameter optimization space, maximum response time index T max , and the preset ratio range of the overshoot ratio K. The PID parameter optimization space refers to the value range of the PID parameters and the value range of the increments ΔP, ΔI, and ΔD of the PID parameters.

[0157] It should be noted that the value range for PID parameters is set to protect the service life of MFC. If the set PID parameters exceed the PID optimization space of the product, it may cause the valve to open and close violently. Even if the desired control flow can be achieved, the service life of MFC will be greatly reduced. The value range for increments ΔP, ΔI, and ΔD is set to control the PID parameter optimization time. If the increment value range is too small, it will consume a lot of time. If the value is too large, the PID parameters that meet the performance indicators may not be calibrated. In addition, for different series of products, customers have different performance requirements. For example, in some products, the maximum allowable response time indicator T can be obtained. max The duration is 1000ms and the overshoot ratio is 1%.

[0158] Fig.11 A structural block diagram of a PID parameter calibration device for a mass flow controller provided in an embodiment of the present disclosure, such as Fig.11 As shown, the PID parameter calibration device 110 includes: an acquisition unit 111, an adjustment unit 112 and a recording unit 113.

[0159] Among them, the acquisition unit 111 is configured to acquire the current PID parameters and perform flow control through the current PID parameters, and acquire the current characteristic parameters of the current flow setting point, and the current characteristic parameters include the current response time and the current overshoot ratio; the adjustment unit 112 is configured to adjust the current PID parameters based on the preset fuzzy control algorithm when the current response time is greater than the preset time threshold and / or the current overshoot ratio is not within the preset ratio range, and trigger the acquisition unit 111 to re-acquire the current PID parameters and perform flow control through the current PID parameters, and acquire the current characteristic parameters of the current actual flow of the current flow setting point; the recording unit 113 is configured to record and save the current PID parameters when the current response time is less than or equal to the preset time threshold and the current overshoot ratio is within the preset ratio range.

[0160] The PID parameter calibration device for a mass flow controller provided in the embodiment of the present disclosure is used to implement the above-mentioned PID parameter calibration method. For specific related descriptions, please refer to the related descriptions in the PID parameter calibration method of the above-mentioned embodiment, which will not be repeated here.

[0161] Fig.12 A structural block diagram of a mass flow controller provided in an embodiment of the present disclosure, such as Fig.12As shown, the embodiment of the present disclosure also provides a mass flow controller 120, which includes a flow sensor 121, a valve 122 and a controller 123, wherein the controller 123 controls the valve 122 based on PID parameters to control the actual output flow of the valve 122, and the PID parameters are calibrated by the PID parameter calibration method of the above embodiment, and the flow sensor 121 is used to detect the actual output flow of the valve 122.

[0162] Fig.13 A block diagram of a composition of an electronic device provided in an embodiment of the present disclosure.

[0163] Reference Fig.13 An embodiment of the present disclosure provides an electronic device, which includes: at least one processor 131; at least one memory 132, and one or more I / O interfaces 133, connected between the processor 131 and the memory 132; wherein the memory 132 stores one or more computer programs that can be executed by the at least one processor 131, and the one or more computer programs are executed by the at least one processor 131 so that the at least one processor 131 can execute the above-mentioned PID parameter calibration method.

[0164] The embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the PID parameter calibration method provided in the above embodiment is implemented.

[0165] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a non-transitory medium and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer-readable storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0166] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A PID parameter calibration method for a mass flow controller, characterized in that: include: Obtaining current PID parameters and performing flow control using the current PID parameters, and obtaining current characteristic parameters of the current flow set point, wherein the current characteristic parameters include a current response time and a current overshoot ratio; When the current response time is greater than a preset time threshold, and / or the current overshoot ratio is not within a preset ratio range, the current PID parameters are adjusted based on a preset fuzzy control algorithm, and the current PID parameters are returned to the step of obtaining the current PID parameters, performing flow control by using the current PID parameters, and obtaining the current characteristic parameters of the current flow set point; When the current response time is less than or equal to the preset time threshold and the current overshoot ratio is within a preset ratio range, the current PID parameters are recorded and saved.

2. The method according to claim 1, characterized in that The current PID parameters include a current proportional coefficient, a current integral coefficient, and a current differential coefficient. The step of adjusting the current PID parameters based on a preset fuzzy control algorithm includes: Taking the input parameters corresponding to each coefficient as the input quantity and the increment of each coefficient as the output quantity, based on the preset fuzzy control algorithm, the current target increment corresponding to each coefficient is determined according to the input parameters corresponding to each coefficient; wherein the input parameters corresponding to the current proportional coefficient and the current integral coefficient respectively include the current response time and the current overshoot ratio, and the input parameters corresponding to the current differential coefficient include the current overshoot ratio; The current proportional coefficient, the current integral coefficient, and the current differential coefficient are adjusted respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient, and the current target increments corresponding to the coefficients.

3. The method according to claim 2, characterized in that The steps of adjusting the current proportional coefficient, the current integral coefficient, and the current differential coefficient respectively according to the current proportional coefficient, the current integral coefficient, the current differential coefficient, and the current target increments corresponding to the coefficients include: updating the current proportionality coefficient according to the sum of the current proportionality coefficient and the current target increment corresponding to the current proportionality coefficient; and updating the current integral coefficient according to the sum of the current integral coefficient and the current target increment corresponding to the current integral coefficient; and The current differential coefficient is updated according to the sum of the current differential coefficient and the current target increment corresponding to the current differential coefficient.

4. The method according to claim 2, characterized in that: Based on a preset fuzzy control algorithm, the step of determining the current target increment corresponding to each coefficient according to the input parameters corresponding to each coefficient includes: For each coefficient of the current proportional coefficient, the current integral coefficient and the current differential coefficient, a fuzzy operation is performed on the input parameter corresponding to the coefficient to obtain the membership degree corresponding to the input parameter; Obtaining a fuzzy relationship between an input parameter corresponding to the coefficient and an output quantity corresponding to the coefficient from a fuzzy rule base, and performing a fuzzy reasoning operation on the output quantity corresponding to the coefficient based on the fuzzy relationship to obtain a fuzzy matrix corresponding to the fuzzy relationship; According to the membership degree corresponding to the input parameter corresponding to the coefficient and the fuzzy matrix, obtaining the membership degree corresponding to the output quantity corresponding to the coefficient; Based on the membership degree corresponding to the output amount corresponding to the coefficient, a clarification operation is performed on the output amount corresponding to the coefficient to obtain the current target increment corresponding to the coefficient.

5. The method according to claim 4, characterized in that The step of performing fuzzy operation on the input parameter corresponding to the coefficient to obtain the degree of membership corresponding to the input parameter comprises: Based on the domain mapping function, the input parameter is converted into a value on the corresponding standard domain; Based on the membership function corresponding to the input parameter, the membership corresponding to the input parameter is obtained according to the value on the standard domain corresponding to the input parameter.

6. The method according to claim 4, characterized in that The step of performing a clarification operation on the output amount corresponding to the coefficient based on the degree of membership corresponding to the output amount corresponding to the coefficient to obtain the current target increment corresponding to the coefficient includes: Defuzzification is performed on the output quantity corresponding to the coefficient according to the degree of membership corresponding to the output quantity corresponding to the coefficient and the standard domain corresponding to the output quantity corresponding to the coefficient, so as to obtain the output value of the output quantity corresponding to the coefficient in the corresponding standard domain; The output value of the output quantity corresponding to the coefficient in the corresponding standard domain is subjected to domain inverse transformation processing to obtain the current target increment corresponding to the coefficient.

7. The method according to claim 1, characterized in that Before the steps of obtaining current PID parameters, performing flow control by using the current PID parameters, and obtaining current characteristic parameters of the current flow set point, the method further includes: The measuring range of the mass flow controller is divided into a plurality of flow intervals, wherein different flow intervals correspond to different PID parameters, and the flow rate of the flow set point in any flow interval is controlled based on the corresponding PID parameters.

8. The method according to claim 1, characterized in that When the current response time is greater than a preset time threshold, and / or the current overshoot ratio is not within a preset ratio range, before adjusting the current PID parameter based on a preset fuzzy control algorithm, the method further includes: determining whether the current accumulated adjustment times of the current PID parameter is less than a preset calibration times; When the current accumulated adjustment times of the current PID parameter is less than the preset calibration times, a step of adjusting the current PID parameter based on a preset fuzzy control algorithm is performed.

9. A PID parameter calibration device for a mass flow controller, characterized in that: include: An acquisition unit, which is configured to acquire current PID parameters and perform flow control by using the current PID parameters, and acquire current characteristic parameters of a current flow set point, wherein the current characteristic parameters include a current response time and a current overshoot ratio; an adjusting unit, configured to adjust the current PID parameters based on a preset fuzzy control algorithm when the current response time is greater than a preset time threshold and / or the current overshoot ratio is not within a preset ratio range, and trigger the acquiring unit to reacquire the current PID parameters and perform flow control by using the current PID parameters, and acquire the current characteristic parameters of the current actual flow of the current flow set point; The recording unit is configured to record and save the current PID parameters when the current response time is less than or equal to a preset time threshold and the current overshoot ratio is within a preset ratio range.

10. A mass flow controller, characterized in that: It comprises a flow sensor, a valve and a controller, wherein the controller controls the valve based on PID parameters, and the PID parameters are calibrated by the PID parameter calibration method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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