Control method for air supply system of clean room

Abstract

The invention discloses a control method for an air supply system of a clean room, and relates to the technical field of automatic control and environment monitoring, and the method comprises the following steps: monitoring the pressure difference between the clean room and an external area in real time, and taking the pressure difference as a system feedback signal; in combination with the advantages of the fuzzy control technology, the air supply amount is dynamically adjusted through the fuzzy PID algorithm so as to keep the indoor pressure within the target range, the fuzzy control can flexibly adjust the control rule according to the change of the system state, the PID control can further refine the adjusting process, and therefore more accurate control is achieved; the control method not only can effectively maintain the positive pressure environment of the clean room, but also can quickly respond to the environment change, and improves the stability and the anti-interference capability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control and environmental monitoring, and in particular to a control method for a clean room air supply system. Background Art

[0002] As a high-standard controlled environment, clean rooms are widely used in many industries, such as biomedicine, semiconductor manufacturing, aerospace, etc. These industries have strict requirements on the air quality and pressure control of the environment. In a clean room, one of the most important control indicators is air quality, which is directly related to whether the production environment of the product meets the standards. Pressure control is also crucial. Clean rooms need to maintain a certain positive pressure state to prevent the intrusion of external pollutants and maintain cleanliness. To ensure the realization of these control requirements, clean rooms usually need to rely on accurate and stable air supply systems for air circulation and purification.

[0003] Most of the existing clean room air supply control systems use traditional PID control algorithms. PID control (proportional-integral-differential control) is widely used in various control systems due to its relative simplicity and easy implementation. However, when faced with the complexity of clean room air supply systems, traditional PID control often has certain limitations. First, the clean room air supply control system has obvious nonlinear characteristics, especially under different operating conditions, the changes in air flow and pressure are no longer linear. Secondly, the clean room air supply system often has a large time lag, and the impact of the control signal on the system state takes a certain amount of time to appear. Finally, since the air quality requirements of the clean room are very precise, the traditional PD control algorithm may have a slow adjustment response and it is difficult to quickly adapt to environmental changes. Summary of the invention

[0004] In view of the limitations of the prior art, which may be slow in adjustment response and difficult to quickly adapt to environmental changes, the present invention proposes a clean room air supply control method, system and device, which applies fuzzy control technology to the PID control algorithm, can effectively cope with the complexity and uncertainty in the clean room air supply system, thereby solving the problems existing in the prior art.

[0005] A clean room air supply system control method comprises the following steps:

[0006] The pressure difference between the clean room and its external area monitored in real time is collected, and the deviation e and the deviation change rate ec of the indoor pressure setting value and the actual feedback value are obtained according to the pressure difference;

[0007] A fuzzy PID controller is constructed by introducing the fuzzy control method into the PID controller; the deviation e and the deviation change rate ec are quantified and converted into language variables E and EC and then input into the fuzzy PID controller; by setting the domain of the language variables E and EC, fuzzy sets are established on each domain, and the input language variables E and EC are converted into fuzzy quantities through the fuzzy sets; fuzzy reasoning is performed on the fuzzy quantities according to the fuzzy sets and the set fuzzy rules, and the proportional parameter Kp and integral parameter K of the PID controller are obtained. I and the differential parameter K D The fuzzy value of each parameter is clarified by the centroid method to obtain the proportional coefficient adjustment value ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D ; According to the proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D Adjust the PID controller parameters;

[0008] The air supply volume of the clean room is adjusted according to the PID controller with adjusted parameters.

[0009] Furthermore, the domain of the language variable E is {-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2}; the domain of the language variable EC is: {-0.9, -0.6, -0.3, 0, 0.3, 0.6, 0.9}.

[0010] Furthermore, the triangular membership function is used as the membership function of the fuzzy set, and three parameters are defined. a、 b.c , determine the input variables x The degree of membership in different ranges is expressed as:

[0011]

[0012] Among them, the parameters a and c Determine the two angles of the triangle, parameters b Determine a peak of the triangle.

[0013] Furthermore, the proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D The PID controller parameters are corrected, and the correction process is expressed as:

[0014]

[0015] in,K p ' , K I ' and K D ' is the initial value of the PID controller parameter; Δ K p , Δ K I , Δ K D is the correction value of PID controller parameters; K p 、K I and K D It is the corrected value of PID controller.

[0016] Furthermore, the centroid method is used to clarify the fuzzy values ​​of each parameter, which is expressed as:

[0017]

[0018] in, u i is an element in the fuzzy subset, f ( u i )for u i The corresponding membership function is, x is the processed output variable.

[0019] The present invention also includes a clean room air supply control system, comprising:

[0020] The acquisition module is used to acquire the pressure difference between the clean room and its external area under real-time monitoring, and obtain the deviation e and the deviation change rate ec of the indoor pressure setting value and the actual feedback value according to the pressure difference;

[0021] The parameter adjustment module is used to construct a fuzzy PID controller by introducing the fuzzy control method into the PID controller; the deviation e and the deviation change rate ec are quantified and converted into language variables E and EC and then input into the fuzzy PID controller; by setting the domain of the language variables E and EC, fuzzy sets are established on each domain, and the input language variables E and EC are converted into fuzzy quantities through the fuzzy sets; fuzzy inference is performed on the fuzzy quantities according to the fuzzy sets and the set fuzzy rules to obtain the proportional parameter Kp and integral parameter K of the PID controller I and the differential parameter K D The fuzzy value of each parameter is clarified by the centroid method to obtain the proportional coefficient adjustment value ΔKp , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D , according to the proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D Adjust the PID controller parameters;

[0022] The regulating module is used to regulate the air supply volume of the clean room according to the PID controller after parameter adjustment.

[0023] The present invention provides a clean room air supply system control method, which has the following beneficial effects:

[0024] The present invention combines the traditional PID control algorithm with the fuzzy control technology, and proposes to apply the fuzzy PID control algorithm to the clean room air supply system; by real-time monitoring of the pressure difference between the clean room and the external area as a system feedback signal, combined with the advantages of the fuzzy control technology, the air supply volume is dynamically adjusted through the fuzzy PID algorithm to keep the indoor pressure within the target range. The fuzzy control can flexibly adjust the control rules according to the changes in the system state, while the PID control can further refine the adjustment process, thereby achieving more precise control; this control method can not only effectively maintain the positive pressure environment of the clean room, but also quickly respond to environmental changes, improve the stability and anti-interference ability of the system, ensure that the air quality in the clean room always meets the requirements, and avoid the invasion of external pollutants. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a structural diagram of the clean room air volume control based on the fuzzy PID control algorithm in an embodiment of the present invention;

[0026] Figure 2 This is a flow chart of the fuzzy PID control algorithm in an embodiment of the present invention;

[0027] Figure 3 Graph showing the triangle membership function in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0029] The present invention proposes a clean room air supply control method based on fuzzy PID algorithm, such as Figure 2 As shown, the specific steps include:

[0030] S1. Use pressure sensors to monitor the pressure difference between the clean room and the outside area in real time. The pressure difference is an important indicator of whether the air in the clean room is in a positive pressure state. If the pressure difference is too small, it means air leakage or insufficient air supply, and the system needs to increase the indoor pressure by adjusting the air volume. If the pressure difference is too large, the air volume needs to be adjusted to reduce the pressure difference to prevent excessive expansion of the indoor air.

[0031] Among them, the construction of the clean room system model includes the following steps:

[0032] 1. Obtain the changing relationship between the actual pressure and the control pressure in the clean room.

[0033] 2. Determine the corresponding transfer function form based on the changing relationship.

[0034] 3. Based on the response curve obtained from the step response experiment, determine the model parameters corresponding to the transfer function form.

[0035] 4. Determine the corresponding clean room air supply control system model based on the transfer function form and model parameters.

[0036] Establishment of the mathematical model of the clean room air supply control system: The structure of the transfer function is a first-order lag plus delay model. In the clean room air supply control, when the input (air supply volume) changes, the pressure signal G (S) will change according to the transmission coefficient k Make corresponding changes, and the inertia time constant T and lag time of the system τ This affects the dynamic characteristics and time response of the system. Therefore, the transfer function is expressed by the following formula:

[0037] (1)

[0038] In formula (1): G ( S ) is the actual pressure signal; k is the transmission coefficient; T is the system inertia time constant; τ is the system lag time, s is the Laplace transform factor.

[0039] S2. Use fuzzy control technology to adjust PID parameters in real time. According to the monitored pressure difference, fuzzy control technology adjusts the proportional (Kp), integral (K I ) and differential (K D ) parameters for real-time adjustment. When the pressure difference increases, the system will increase the proportional coefficient K P , in order to quickly respond to the error; when the pressure error changes more slowly, the integral coefficient K can be appropriately increased I , reducing the long-term accumulated errors of the system.

[0040] The fuzzy PID algorithm can dynamically adjust the parameters of the PID controller by introducing a fuzzy reasoning mechanism to adapt to the nonlinear characteristics and time lag problems that change over time in the system. The fuzzy PID algorithm generates corresponding fuzzy control quantities based on the current state and error range of the system through a fuzzy rule base, and converts these control quantities into specific PID control parameters, thereby achieving precise regulation of the system, such as Figure 2 As shown. Fuzzy control mainly consists of four components: knowledge base, fuzzification, fuzzy reasoning and clarification. The specific design steps are as follows:

[0041] S2.1. Fuzzification

[0042] The process of converting precise input quantity into fuzzy quantity recognizable by fuzzy controller usually involves three steps: quantifying the physical field, establishing fuzzy sets, and determining membership functions.

[0043] 1. Quantification of physical domain: Convert the actual physical quantity or control variable into a numerical value that can be processed by the fuzzy controller. Assume that the precise value of the controller input is X, and the range is [X L , X H ], X∈[X L , X H ] is called the physical domain. In order to facilitate controller design and subsequent modification, the physical domain is usually converted into a discrete domain. The physical quantity n is quantified and mapped to m in the discrete domain. The relationship between n and m is expressed by formula (2):

[0044] (2)

[0045] In formula (3) k is the quantization factor, and its value is:

[0046] (3)

[0047] 2. Establish fuzzy sets: Since the input quantities of fuzzy controllers are all numerical values, it is necessary to establish fuzzy sets in each domain and convert the input quantities into fuzzy quantities through fuzzy sets. Fuzzy sets can be set to positive large, positive middle, positive small, zero, negative large, negative middle, and negative small. They can also be represented by the English abbreviations PB, PM, PS, ZO, NB, NM, and NS. The number of fuzzy sets is limited by factors such as the complexity of controller design and computing resources. If there are too many fuzzy sets, the complexity and computational burden of the control rules will increase, which may lead to a decrease in real-time performance or make it difficult to achieve. It is necessary to weigh the number of fuzzy sets and the performance requirements of the control system according to the specific situation.

[0048] 3. Select the membership function: The membership function is used to describe the degree of membership of the elements in the input or output fuzzy set to the set. Due to differences in the understanding and cognition of fuzzy concepts, the selection of membership functions is somewhat subjective and can usually be determined by combining system knowledge, expert experience, experimental adjustment and other methods. The membership function not only affects the applicability of fuzzy rules, but is also directly related to the performance of the entire control system. The present invention adopts a triangular membership function. Its mathematical expression is:

[0049] (4)

[0050] In formula (4): Parameter a and c Determine the two angles of the triangle, parameters b Determine a peak of the triangle.

[0051] S2.2 Knowledge Base

[0052] The knowledge base includes a database and a control rule base.

[0053] 1. The database is responsible for storing various data related to fuzzy reasoning, including the actual values ​​of input and output variables and the membership of fuzzy sets, etc. These data are called and updated during the fuzzy reasoning process to ensure that the fuzzy PID controller can be adjusted in real time according to the system status.

[0054] Second, the control rule base is the core of the knowledge base, which contains fuzzy control rules based on expert experience, system characteristics or experimental data. These rules describe the fuzzy relationship between input variables and output variables and provide guidance for the fuzzy reasoning process. Usually, these rules are presented in the form of "if-then", for example:

[0055] Rl: if E is PM and EC is ZO thenU is NS.

[0056] R2: if E is PS and EC is NB thenU is ZO.

[0057] R3: if E is PB and EC is NM thenU is PM.

[0058] S2.3 Fuzzy Reasoning

[0059] Fuzzy reasoning is a key step in fuzzy control, which involves fuzzification of variables and performs operations such as fuzzy implication and fuzzy synthesis based on input fuzzy sets and fuzzy rules to derive output fuzzy sets. This process simulates human fuzzy thinking and decision-making process. In the fuzzy control system of the present invention, the fuzzy reasoning method used is the Mamdani fuzzy reasoning method.

[0060] S2.4. Clarification

[0061] Clarification is the process of converting the fuzzy value obtained through fuzzy reasoning into a specific and accurate value applicable to the control system. Since the results of fuzzy reasoning are ambiguous and cannot be directly applied to the control system, clarification is required to obtain accurate control signals. There are various clarification methods, and different methods have their own advantages and applicable scenarios. The present invention adopts the centroid method, which takes the centroid of the area enclosed by the membership function curve and the horizontal coordinate as the representative point to obtain the clarified accurate value. This method is more reasonable in theory, makes full use of all the information in the fuzzy set, and makes corresponding adjustments according to different membership degrees. However, its computational complexity is high and it is more sensitive to the shape of the membership function. Its clarification formula is:

[0062] (5)

[0063] In formula (5) u i is an element in the fuzzy subset; f ( u i )for u i The corresponding membership function.

[0064] The present invention successfully solves the shortcomings of traditional PID control in dynamic environments by introducing a fuzzy PID control algorithm. In the application of clean room air supply systems, traditional PD control algorithms face the problems of difficulty in parameter adjustment and decreased control accuracy caused by environmental changes. In order to solve this problem, the adaptive characteristics of fuzzy control are utilized so that the PID control parameters can be dynamically adjusted according to the real-time operating status of the system, thereby effectively coping with various uncertainties and fluctuations in the environment; the fuzzy PID control algorithm combines the flexibility of fuzzy control with the accuracy of PID control, and can automatically adjust the control parameters to ensure that the system maintains optimal performance under different working conditions. By monitoring the operating status of the system in real time, the fuzzy control rule table and the initial values ​​of the PID parameters can be optimized according to environmental changes, avoiding the instability caused by manual adjustment.

[0065] Example: Fuzzy PID controller for clean room air supply control system

[0066] The fuzzy PID controller combines fuzzy logic control with traditional PID control, and can better cope with the nonlinearity and uncertainty of the system by fuzzifying the input signal. By adjusting the fuzzy control rules, it can flexibly cope with changes in indoor pressure and improve the robustness and accuracy of the control system. The following are the specific steps for designing a clean room air supply control system based on the fuzzy PID algorithm:

[0067] 1. Select the structure of the fuzzy controller

[0068] According to the number of input variables, fuzzy controllers can be divided into one-dimensional, two-dimensional and three-dimensional fuzzy controllers. The present invention designs a fuzzy PID controller with two input variables and three output variables, making full use of the advantages of fuzzy control. The structure of the fuzzy PID controller is as follows: Figure 1 shown.

[0069] The two input variables in the fuzzy PID controller are the error between the given pressure value and the feedback value, e, and the error change, ec. The current system state is determined based on the input variables, and the output is adjusted to correct the deviation. The three output variables are the proportional coefficient adjustment ΔK p , integral coefficient adjustment ΔK I And the differential coefficient adjustment ΔK D The fuzzy PID controller collects and processes input variables in real time, calculates the adjustment amount of the PID controller coefficient, and can finely adjust the coefficient of the PID controller in real time.

[0070] (6)

[0071] In formula (6) K p ' , K I ' and K D ' is the initial value of the PID controller parameter; Δ K p , Δ K I , Δ K D is the correction value of PID controller parameters; K p 、K I and K D It is the corrected value of PID controller.

[0072] 2. Selection of Input Domain and Quantization Factor

[0073] In the clean room air supply control system, in order to achieve accurate control of the indoor pressure, it is first necessary to compare the set value of the indoor pressure with the actual feedback value to obtain the two inputs of deviation e and deviation change rate ec, which are quantified and transformed into language variables that can be processed by the fuzzy controller; fuzzy reasoning will obtain three outputs, which are quantified and transformed into specific values, and then the PID controller parameters are adjusted. The system quantization parameters are shown in Table 1.

[0074] Table 1 System quantization parameters

[0075]

[0076] 3. Determine the membership function and fuzzy rules

[0077] The selection and definition of the membership function has a direct and critical impact on the results and performance of fuzzy reasoning. Different membership function shapes and parameters will lead to different control outputs, so it is necessary to select a suitable membership function based on actual control needs. The clean room air supply control system has high requirements for the flexibility and rapid responsiveness of the control algorithm, and the mathematical expression and operation of the triangular membership function are relatively simple, which helps to improve the sensitivity and stability of the system. Therefore, the triangular membership function is selected, such as Figure 3 shown.

[0078] Formulating fuzzy control rules is the core of the fuzzy controller. The fuzzy subsets of the two input variables and the three output variables are divided into seven fuzzy sets, {NB, NM, NS, ZO, PS, PM, PB}. Therefore, each output variable has 49 fuzzy rules, and the fuzzy set contains a total of 147 fuzzy relations.

[0079] In order to accurately adjust the PID controller parameters, it is necessary to have a deep understanding of K p , K I and K D In order to influence the system performance, fuzzy control rules suitable for PID parameter adjustment are formulated according to empirical formulas, experimental data or expert knowledge, as shown in Table 2, Table 3 and Table 4.

[0080] Table 2 ΔKp control rules

[0081]

[0082] Table 3 ΔKI control rules

[0083]

[0084] Table 4 ΔK D Control rules table

[0085]

[0086] 4. Clarity

[0087] The results of fuzzy reasoning must be clarified before they can be converted into precise quantities that meet actual control requirements. During the operation of the clean room air supply control system, it is inevitable to encounter various dynamic factors such as load changes and environmental pressure changes. The output signal generated by the center of gravity method is smooth, which helps to reduce system overshoot and oscillation. Compared with other clarification methods, the center of gravity method not only considers the value corresponding to the maximum membership, but also integrates the membership information of all output variables, making the system output more accurate. Therefore, the use of the center of gravity method as a clarification method can not only improve the control accuracy of the clean room air supply control system, but also more accurately maintain a stable positive pressure state in the room.

[0088] The present invention solves the shortcomings of the traditional PID control algorithm in controlling the indoor pressure accuracy by introducing a fuzzy PID control algorithm, and uses fuzzy control technology to optimize the PID controller parameters, thereby accurately adjusting the fan speed in the clean room to ensure that the indoor pressure reaches the preset requirements. The method monitors the pressure difference between the clean room and the external area in real time, obtains the deviation and change rate of the indoor pressure set value and the actual feedback value, and uses the fuzzy control model to output the adjustment amount ΔKp, ΔKI, ΔKD to dynamically optimize the proportional (KP), integral (KI) and differential (KD) parameters of the PID controller. By adjusting these parameters, the fan speed is further adjusted to achieve air volume control to maintain the stability of the pressure inside the clean room. In order to adapt to the dynamic characteristics of the clean room pressure change, the control method adjusts the adjustment amplitude of the PID parameter according to the amplitude of the pressure change. When the pressure fluctuation is large, the adjustment amplitude of the PID parameter is increased; when the pressure change is small, the adjustment amplitude is reduced. In addition, through the feedback mechanism based on the pressure fluctuation rate, the domain of the fuzzy control language variable can be dynamically adjusted, and the adjustment range is expanded when the pressure fluctuation is large, and the adjustment range is narrowed when the fluctuation is small, so as to avoid unnecessary over-adjustment. The control method also includes the establishment of a time-delay model and the use of a Smith controller for time-delay compensation to more accurately adjust the fan speed and ensure that the pressure in the clean room is always kept near the set value. Even in the case of system response delay, a good control effect can be maintained. By combining fuzzy control and PID regulation, the present invention provides an efficient and intelligent control method for the accuracy and dynamic response capability of clean room air supply control, which can achieve high-precision pressure control in a changing working environment.

[0089] Based on the same inventive concept, the present invention also proposes a clean room air supply control system, comprising:

[0090] The acquisition module is used to collect the pressure difference between the clean room and its external area under real-time monitoring, and obtain the deviation e and the deviation change rate ec between the indoor pressure setting value and the actual feedback value based on the pressure difference.

[0091] The parameter adjustment module is used to construct a fuzzy PID controller by introducing the fuzzy control method into the PID controller; the deviation e and the deviation change rate ec are quantified and converted into language variables E and EC and then input into the fuzzy PID controller; by setting the domain of the language variables E and EC, fuzzy sets are established on each domain, and the input language variables E and EC are converted into fuzzy quantities through the fuzzy sets; fuzzy inference is performed on the fuzzy quantities according to the fuzzy sets and the set fuzzy rules to obtain the proportional parameter Kp and integral parameter K of the PID controller I and the differential parameter K D The fuzzy value of each parameter is clarified by the centroid method to obtain the proportional coefficient adjustment value ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D , according to the proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D Adjust the PID controller parameters.

[0092] The regulating module is used to regulate the air supply volume of the clean room according to the adjusted PID controller parameters.

[0093] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A clean room air supply system control method, characterized in that: The following steps are involved: Collect the pressure medium pressure difference between the clean room and its external area monitored in real time, and obtain the deviation e and the deviation change rate ec of the indoor pressure setting value and the actual feedback value according to the pressure difference; A fuzzy PID controller is constructed by introducing the fuzzy control method into the PID controller; the deviation e and the deviation change rate ec are quantified and converted into language variables E and EC and then input into the fuzzy PID controller; by setting the domain of the language variables E and EC, fuzzy sets are established on each domain, and the input language variables E and EC are converted into fuzzy quantities through the fuzzy sets; According to the fuzzy set and the set fuzzy rules, the fuzzy quantity is fuzzy inferred to obtain the proportional parameter Kp and integral parameter K I and the differential parameter K D The fuzzy value of each parameter is clarified by the centroid method to obtain the proportional coefficient adjustment value ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D ;according to Proportional coefficient adjustment ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D Adjust the PID controller parameters; The air supply volume of the clean room is adjusted according to the PID controller with adjusted parameters.

2. A clean room air supply system control method according to claim 1, characterized in that: The domain of the language variable E is {-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2}, and the quantization factor is 0.5; the domain of the language variable EC is: {-0.9, -0.6, -0.3, 0, 0.3, 0.6, 0.9}, and the quantization factor is 0.

3. The selection of the domain value takes into account the pressure control requirements and response characteristics of the system under different loads, and is optimized according to the actual application scenarios to ensure that the system can maintain stable operating performance under various working conditions.

3. A clean room air supply system control method according to claim 1, characterized in that: The linguistic variable ΔK P , ΔK I , ΔK D The basic domains are [-1 1], [-0.1 0.1], and [-2 2], and the fuzzy domains are [-3 3], [-0.3 0.3], and [-6 6], and the quantization factors are all 3.

4. A clean room air supply system control method according to claim 1, characterized in that: The proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D The PID controller parameters are corrected, and the correction process is expressed as: ； in, K p ' , K I ' and K D ' is the initial value of the PID controller parameter; Δ K p , Δ K I , Δ K D is the correction value of PID controller parameters; K p 、K I and K D It is the corrected value of PID controller.

5. A clean room air supply system control method according to claim 1, characterized in that: The centroid method is used to clarify the fuzzy values ​​of each parameter, which is expressed as: ； in, u i is an element in the fuzzy subset, f ( u i )for u i The corresponding membership function is, x is the processed output variable.

6. A clean room air supply system control method, characterized in that: include: The acquisition module is used to acquire the pressure difference between the clean room and its external area under real-time monitoring, and obtain the deviation e and the deviation change rate ec of the indoor pressure setting value and the actual feedback value according to the pressure difference; The parameter adjustment module is used to construct a fuzzy PID controller by introducing the fuzzy control method into the PID controller; the deviation e and the deviation change rate ec are quantified and converted into language variables E and EC and then input into the fuzzy PID controller; by setting the domain of the language variables E and EC, fuzzy sets are established on each domain, and the input language variables E and EC are converted into fuzzy quantities through the fuzzy sets; According to the fuzzy set and the set fuzzy rules, the fuzzy quantity is fuzzy inferred to obtain the proportional parameter Kp and integral parameter K I and the differential parameter K D The fuzzy value of each parameter is clarified by the centroid method to obtain the proportional coefficient adjustment value ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D , according to the proportional coefficient adjustment amount ΔK p , integral coefficient adjustment ΔK I and differential coefficient adjustment ΔK D Adjust the PID controller parameters; The regulating module is used to regulate the air supply volume of the clean room according to the PID controller after parameter adjustment.

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