A control method of variable universe fuzzy PID for vacuum suction system

Abstract

The application discloses a control method of variable domain fuzzy PID for a vacuum suction system, and belongs to the field of industrial control. The method comprises the following steps: establishing a theoretical model of the vacuum suction system, simulating based on the theoretical model and constructing an initial PID controller, determining initial parameters of the initial PID controller, and setting related parameters for fuzzy processing; calculating a domain scaling factor according to a vacuum degree deviation and a vacuum degree deviation change rate at a current time, adjusting a basic domain of a signal by using the scaling factor, and performing fuzzy processing based on a fuzzy rule table to obtain a deviation value of PID parameters, adjusting the initial parameters of the initial PID controller by using the deviation value of the PID parameters, and finally adjusting a duty cycle of a vacuum pump by using an adjusted PID controller to control the vacuum degree of the vacuum suction system. The application can dynamically adjust PID parameters in the vacuum suction system, and realize stable control of the vacuum suction system in a complex environment by using fewer parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to industrial control technology, in particular to a variable universe fuzzy PID control method for a vacuum suction system. BACKGROUND

[0002] The conventional PID control algorithm structure is relatively simple, and is relatively easy to implement, and is widely used in the field of industrial control, and is mature in theory, but due to the difficulty in dealing with the nonlinear and uncertain problems in the system, when applied in the field of vacuum suction, it cannot well adapt to the change of the dynamic characteristics of the system, resulting in problems such as overshoot, oscillation and slow response in the vacuum degree control process.

[0003] The fuzzy PID control algorithm introduces fuzzy processing to enhance the adaptability and robustness of the conventional PID controller, can effectively deal with the nonlinearity and uncertainty of the system, and has strong adaptability and does not depend on accurate mathematical model, and can dynamically adjust the control strategy. Although fuzzy control can improve the robustness and adaptability of the system to a certain extent, when dealing with a large range of vacuum degree changes, the traditional fuzzy control rule and the universe range are often limited, and it is difficult to achieve high-precision control effect. In addition, how to flexibly adjust the control parameters under different operating conditions to meet the needs of variable working conditions is still an important technical challenge. SUMMARY

[0004] The purpose of the present application is to provide a variable universe fuzzy PID control method for a vacuum suction system, which adjusts the fuzzy universe in real time through a scaling factor to solve the nonlinear and time-varying problems of vacuum degree control in the vacuum suction system.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] The present application provides a variable universe fuzzy PID control method for a vacuum suction system, the steps are as follows:

[0007] Step 1: Establish a theoretical model of the vacuum suction system, simulate based on the above-mentioned theoretical model of the vacuum suction system and construct an initial PID controller, and determine the initial parameters of the initial PID controller, including: the initial value of the proportional coefficient K P0 , the initial value of the integral coefficient K I0 , the initial value of the differential coefficient K D0 ; Set the related parameters of fuzzy processing, including: the basic universe E of the vacuum degree deviation e, the basic universe Ec of the vacuum degree deviation rate ec, and the basic universe U of the PID parameter deviation ΔK P , ΔK I and ΔK D .

[0008] Step two: collect the vacuum pressure sensor voltage signal, convert the voltage and pressure ratio into the signal of the vacuum degree deviation e, and calculate the vacuum degree deviation change rate ec according to the vacuum degree deviation e.

[0009] Step three: calculate the expansion factor of E alpha1 and the expansion factor of U beta(u) according to e, and calculate the expansion factor of Ec alpha2 according to ec.

[0010] Step four: adjust the domain of E, Ec and U according to alpha1, alpha2 and beta(u) respectively, and establish a fuzzy rule base according to the PID setting rule to perform fuzzy processing on e and ec, and obtain the deviation value of the PID parameter Delta K P , Delta K I And Delta K D .

[0011] Step five: adjust the initial parameters of the initial PID controller according to the deviation value of the PID parameter Delta K P , Delta K I And Delta K D , and construct an adjusted PID controller.

[0012] Step six: input e and ec into the adjusted PID controller to obtain the duty cycle of the vacuum pump.

[0013] Compared with the prior art, the control method of the variable domain fuzzy PID for the vacuum suction system has the following advantages: during the operation of the vacuum suction system, the proportional term coefficient, the integral term coefficient and the differential term value of the PID controller at the current time can be adjusted in real time according to the vacuum degree deviation and the vacuum degree deviation change rate at the current time, the robustness of the PID control applied to the vacuum degree control process in the vacuum suction system is enhanced, and the nonlinear problems and uncertain problems in the system are effectively handled. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a schematic diagram of the vacuum suction system of the present application.

[0015] Figure 2 It is a block diagram of the variable domain fuzzy PID control method of the present application.

[0016] Figure 3 It is an experimental schematic diagram of the vacuum suction system of the present application.

[0017] Figure 4 It is a pipe equivalent flow conductance model of the vacuum suction system of the present application.

[0018] Figure 5 It is a membership function diagram of the present application.

[0019] Figure 6is a vacuum degree change curve chart of the present application. DETAILED DESCRIPTION

[0020] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described below with reference to specific embodiments:

[0022] In combination Figure 1 and Figure 2 , a variable domain fuzzy PID control method for a vacuum suction system according to the present application comprises the following steps:

[0023] Step 1: Establish a theoretical model of the vacuum suction system, simulate based on the above-mentioned theoretical model of the vacuum suction system and construct an initial PID controller, and determine the initial parameters of the initial PID controller, including: the initial value of the proportional coefficient K P0 , the initial value of the integral coefficient K I0 , and the initial value of the differential coefficient K D0 ; set the related parameters of fuzzy processing, including: the basic domain E of the vacuum degree deviation e, the basic domain Ec of the vacuum degree deviation change rate ec, and the basic domain U of the PID parameter deviation ΔK P , ΔK I and ΔK D .

[0024] The theoretical model of the vacuum suction system is established as follows:

[0025] The following assumptions are made for the theoretical model of the vacuum suction system:

[0026] Assumption 1: The internal medium gas of the vacuum suction system is an ideal gas, i.e. it satisfies the ideal gas equation;

[0027] Assumption 2: The pumping process is considered as an isothermal process;

[0028] Assumption 3: The fluctuation of the vacuum degree in the actual work of the vacuum pump is ignored.

[0029] The gas satisfies the continuity equation:

[0030] Q = Q0 - Q L = -V0dP / dt (1)

[0031] Wherein, Q is the net flow of gas; Q0 is the system pumping capacity, i.e. the pipeline gas flow; Q l is the leakage amount; P is the vacuum chamber pressure; V0 is the vacuum chamber volume; and t is the time.

[0032] According to the isothermal pumping pipeline model, we have:

[0033] Q0 = S P P0 = SP = C (P - P0) (2)

[0034] where P0 is the pressure near the suction inlet of the vacuum pump; S P is the rated pumping speed of the vacuum pump; S is the pumping speed at the inlet of the pipe; C is the conductance, and equation (2) can be rewritten as:

[0035] Q0 = S P CP / (S P +C) (3)

[0036] By combining equations (1) to (3), the pumping equation of the vacuum pumping system is obtained:

[0037] V0 dP / dt = Q L -S P CP / (S P +C) (4)

[0038] The conductance equation of the vacuum pumping system is related to the gas flow state and the geometric characteristics of the pipe. When the pressure in the vacuum pumping system changes, the flow state of the gas can be divided into turbulent flow, laminar flow, and molecular flow.

[0039] The flow properties of the gas under different flow states are also quite different. The flow state of the gas can be judged by the Reynolds number Re. It is generally considered that when Re is greater than 4000, it is turbulent flow, and when it is less than 2000, it is laminar flow. In the range of 2000 to 4000, the transition time is usually calculated by the turbulent flow model, and its expression is:

[0040]

[0041] where, is the average density of the gas in the pipe, D is the equivalent pipe diameter, ω is the average flow rate of the gas in the pipe, and μ is the dynamic viscosity of the gas.

[0042] By approximating ω by the pumping speed S P of the vacuum pump, we have:

[0043] Re = 4S P ρ / (πDμ) (6)

[0044] where ρ is the density of the gas in the pipe.

[0045] The ideal gas in an isothermal state satisfies:

[0046]

[0047] where P a is the local atmospheric absolute pressure, and ρ a is the corresponding air density under atmospheric pressure. The average pressure of the pipeline.

[0048] After the pipeline is equivalent to a circular pipeline, the differential pressure ΔP between the two ends can be represented by the following formula:

[0049]

[0050] Where L is the length of the pipeline; λ is the resistance coefficient, whose value is:

[0051]

[0052] By combining equations (6)-(8), the gas flow rate Q0 of the pipeline under different flow conditions is obtained:

[0053]

[0054] Where the equivalent cross-sectional area of the pipeline A = πD 2 / 4.

[0055] The values of parameters a and b are:

[0056]

[0057] By combining equations (3) and (10), the conductance equation is obtained:

[0058]

[0059] Similar to the circuit calculation, when the vacuum pipelines are connected in parallel, the total conductance C of the pipelines satisfies the following formula:

[0060] C = C1 + C2 + … + C n (13)

[0061] Where C1, C2 … C n are the flow channels of each branch pipeline, and n represents the serial number of the branch pipeline flow channel.

[0062] When the vacuum pipelines are connected in series, the total conductance C of the pipelines satisfies the following formula:

[0063] 1 / C = 1 / C1 + 1 / C2 + … + 1 / C n (14)

[0064] Finally, equation (4) is discretized to obtain the discrete form of the pumping equation of the vacuum suction system, i.e., the theoretical model of the vacuum suction system:

[0065] ΔP = Δt i ((S Li P a -(S Pi C i P i ) / (S Pi +C i)) / V0) (15)

[0066] where S L represents the leakage flow rate, Δt represents the step length of each time step, and the subscript i of the parameters in the equation represents the number of iteration steps.

[0067] Step two: collect the vacuum pressure sensor voltage signal, convert the proportional relationship between voltage and air pressure into the signal of the vacuum degree deviation e, and calculate the vacuum degree deviation change rate ec.

[0068] Step three: calculate the expansion factor α1 of E and the expansion factor β(u) of U according to e, and calculate the expansion factor α2 of Ec according to ec, as follows:

[0069]

[0070] In the formula, the control system precision parameters τ1 and τ2 are constants between 0 and 1; K is the sensitivity coefficient of the control system; k corresponds to the number of output variables (corresponding to ΔK P , ΔK I and ΔK D ); p j is the input variable weight coefficient, and the subscript j corresponds to the expansion factor of different output variables (corresponding to ΔK P , ΔK I and ΔK D ); β(0) is the initial value of the output domain expansion factor.

[0071] Step four: adjust the domains of E, Ec and U according to α1, α2 and β(u) respectively, and perform fuzzy processing on e and ec according to the PID tuning rules to obtain the deviation values ΔK P , ΔK I and ΔK D of the PID parameters, as follows:

[0072] The above domain adjustment calculation method is as follows:

[0073]

[0074] In the formula, E(e) is the domain of the adjusted vacuum degree deviation e, Ec(e) is the domain of the adjusted vacuum degree deviation change rate ec; β(u) is the U expansion factor, where u represents different output variables (corresponding to ΔK P , ΔK I and ΔK D ), and U(e) is the domain of the adjusted ΔK P , ΔK I and ΔK D .

[0075] The fuzzy rule base contains PID tuning rules for the deviation values ΔK P , ΔK I and ΔK D of the PID parameters, which are as follows:

[0076] It should be noted that ε is a constant in the rules described below; the specific values of the parameters ΔK P , ΔK I and ΔK D are obtained by combining specific data.

[0077] When the vacuum deviation |e|>0.7E, in order to improve the response speed of the system, ΔK P =εK P0 (ε=0.1-0.5) should be taken; at the same time, in order to reduce the overshoot of the system, ΔK I =-εK I0 (ε=0.6-0.8) should be taken.

[0078] When the vacuum deviation satisfies 0.3E>|e|≥0.7E, ΔK P =-εK P0 (ε=0-0.4) should be taken to prevent the system from having an excessive overshoot; at the same time, in order to ensure the rapid response capability of the system, ΔK I =-εK I0 (ε=0.4-0.6), ΔK D =-εK D0 (ε=0.5-0.8) should be taken.

[0079] When the vacuum deviation |e|≤0.3E, the system is close to the target vacuum, in order to make the vacuum control more stable, ΔK P =-εK P0 (ε=0.2-0.5), ΔK I =-εK I0 (ε=0.4-0.6) should be taken; at the same time, in order to prevent the system from having an oscillation phenomenon, the value of ΔK D should be selected according to the vacuum deviation change rate |ec|; when the vacuum deviation change rate |ec|≤0.5Ec, ΔK D =εK D0 (ε=0.5-1.0) should be taken; when the vacuum deviation change rate 0.7>|ec|>0.5Ec, ΔK D =εK D0 (ε=-0.3-0.3) should be taken; when the vacuum deviation change rate |ec|≥0.7Ec, ΔK D =-εK D0 (ε=0.3-0.5) should be taken.

[0080] The fuzzy processing adopts seven fuzzy subsets NB, NM, NS, ZO, PS, PM and PB to perform fuzzy calculation on the deviation value of the PID parameter.

[0081] The meanings of NB, NM, NS, ZO, PS, PM and PB are explained as follows. The annotations in the brackets below are all examples of the vacuum degree deviation e and its basic argument E, and the vacuum degree deviation e membership function adopts the equal-division triangle membership function.

[0082] NB is the abbreviation of “Negative Big”, representing “a larger negative value” NM is the abbreviation of “Negative Medium”, representing “a moderate negative value” NS is the abbreviation of “Negative Small”, representing “a smaller negative value” ZO is the abbreviation of “ZERO”, representing “a value close to zero” PS is the abbreviation of “Positive Small”, representing “a smaller positive value” PM is the abbreviation of “Positive Medium”, representing “a moderate positive value” PB is the abbreviation of “Positive Big”, representing “a larger positive value”

[0083] Step five: according to the deviation value ΔK P , ΔK I and ΔK D of the PID parameter, the initial parameters of the initial PID controller are adjusted to build an adjusted PID controller, and the adjustment method is as follows:

[0084]

[0085] In the formula, K P1 , K I1 and K D1 are the adjusted PID parameters; K P0 , K I0 and K D0 are the initial parameters of the initial PID controller.

[0086] According to the adjusted PID parameters, an adjusted PID controller is further built.

[0087] Step six: the vacuum degree deviation e and the vacuum degree deviation change rate ec are input into the adjusted PID controller to obtain the duty ratio of the vacuum pump.

[0088] Embodiment

[0089] The feasibility of the implementation scheme is illustrated below by using an experimental platform case.

[0090] Adopting such Figure 3 The vacuum suction system shown serves as the experimental platform, and the vacuum suction system is equivalent to, as follows: Figure 4 The flow conduction model is shown. The rated speed S of the vacuum pump in the vacuum suction system is... P The flow rate is 6 L / min, the conductance of the first and second solenoid valves is 0.0003 PL / min, and the conductance of the third solenoid valve is 0.001 PL / min. The gas medium is air at 20°C. Initially, the host computer sends a command to open the first and second solenoid valves. The system uses the vacuum pump to evacuate a first sealed container with an equivalent volume of 0.5 L. The rear end of the first sealed container is connected to a second sealed container with an equivalent volume of 0.1 L via the third solenoid valve, and a throttling valve is installed to allow vacuum fluctuations. At 15 seconds, the third solenoid valve connecting the two containers opens, disturbing the system. The target vacuum level for the entire process is set to 50 kPa.

[0091] When conducting the above experiments, the variable universe fuzzy PID control method for vacuum suction systems described in this invention involves the following specific steps:

[0092] Step 1: Based on the established theoretical model of the vacuum suction system, perform simulation. Substitute the vacuum suction system parameters from the above experiment into the simulation to construct an initial PID controller, and set the initial parameter K of the initial PID controller. P0 =10, K I0 =3,K D0 =0.3; Set fuzzy processing parameters: the fundamental universe of discourse E of vacuum deviation e is [-10, 10], the fundamental universe of discourse Ec of vacuum deviation change rate ec is [-50, 50], ΔK P ΔK I and ΔK D The basic domains are [6, 6], [3, 3] and [-0.3, 0.3].

[0093] Step 2: Acquire the voltage signal from the vacuum pressure sensor, convert the voltage-to-pressure ratio into a vacuum deviation signal (e), and calculate the vacuum deviation change rate (ec).

[0094] Step 3: Calculate the scaling factor α1 of E and the scaling factor β(u) of U based on e, and calculate the scaling factor α2 of Ec based on ec.

[0095] The accuracy coefficients of the control system are selected as τ1 = 0.6 and τ2 = 0.6; the scaling factor of U is selected as:

[0096]

[0097] In the formula, βP , β I and β D correspond to the domain scaling factor of output variable ΔK P , ΔK I and ΔK D , respectively.

[0098] Step four: adjust the domain of E, Ec and U according to α1, α2 and β(u), respectively, and formulate 49 fuzzy rules of ΔK P , ΔK I and ΔK D , as shown in Table 1, Table 2 and Table 3, respectively. The membership function adopts triangular membership function, and the defuzzification method adopts the center of gravity method, and finally the PID parameter deviation values ΔK P , ΔK I and ΔK D are calculated. In particular, as shown in Table 1, the membership functions of the vacuum degree deviation e and the vacuum degree deviation change rate ec are different, mainly in order to reduce the influence of the vacuum pump on the vacuum degree fluctuation. Figure 5

[0099] Table 1 ΔK P fuzzy rule

[0100]

[0101] Table 2 ΔK I fuzzy rule

[0102]

[0103] Table 3 ΔK D fuzzy rule

[0104]

[0105] Step five: adjust the initial parameters of the initial PID controller according to the deviation values ΔK P , ΔK I and ΔK D of the PID parameters, and build an adjusted PID controller.

[0106] Step six: input the vacuum degree deviation e and the vacuum degree deviation change rate ec into the adjusted PID controller to obtain the duty cycle of the vacuum pump.

[0107] The experimental results are shown in Table 4. Figure 6 ​It can be seen that in the initial response, the two fuzzy control methods make the duty ratio of the vacuum pump high, so that the system can quickly respond; when approaching the control target, the variable universe fuzzy PID control method can reduce the duty ratio of the vacuum pump and reduce the overshoot in time due to the addition of the domain scaling factor. In addition, the variable universe fuzzy PID control method is less affected by the vacuum fluctuation, and the control is more stable. When the system is disturbed, the variable universe fuzzy PID control method can quickly increase the duty ratio of the vacuum pump, so that the system quickly recovers to stability.

[0108] It should be noted that embodiments of the present application can be realized by hardware, software, or a combination of software and hardware. The hardware part can be realized by special logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above-mentioned devices and methods can be realized by computer executable instructions and / or included in processor control code, such as providing such code on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0109] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A control method of variable universe fuzzy PID for a vacuum suction system, characterized by, The steps are as follows: Step one: establish a theoretical model of the vacuum suction system, simulate based on the above theoretical model of the vacuum suction system and construct an initial PID controller, determine the initial parameters of the initial PID controller including: the initial value of the proportional coefficient K P0 , the initial value of the integral coefficient K I0 , and the initial value of the differential coefficient K D0 ; set the related parameters of fuzzy processing including: the basic domain E of the vacuum degree deviation e, the basic domain Ec of the vacuum degree deviation change rate ec, and the basic domain U of the PID parameter deviation ΔK P , ΔK I and ΔK D ; Step two: collect the vacuum pressure sensor voltage signal, convert the proportional relationship between voltage and air pressure into the signal of vacuum degree deviation e, and calculate the vacuum degree deviation change rate ec according to the vacuum degree deviation e; Step three: calculate the scaling factor α1 of E and the scaling factor β(u) of U according to e, and calculate the scaling factor α2 of Ec according to ec; Step four: adjust the domain of E, Ec and U according to α1, α2 and β(u) respectively, and establish fuzzy rule base for e and ec according to PID tuning rules to obtain the bias value ΔK of PID parameters P , ΔK I and ΔK D ; Step five: adjusting the initial parameters of the initial PID controller according to the deviation value ΔK of the PID parameters P , ΔK I and ΔK D to build an adjusted PID controller; Step six: input e and ec into the adjusted PID controller to obtain the duty cycle of the vacuum pump.

2. The control method of variable universe fuzzy PID for vacuum suction system according to claim 1, wherein, In step one, the theoretical model of the vacuum suction system is as follows: ΔP = Δt i ((S Li P a -(S Pi C i P i ) / (S Pi +C i )) / V0) where S L represents the leak flow rate, Δt represents the step length of each time step, the subscript i of the parameters in the equation represents the step number of the iterative calculation, ΔP represents the pressure difference of the pipeline, P a is the local atmospheric absolute pressure, P is the vacuum chamber pressure, S P is the rated pumping speed of the vacuum pump, C is the flow conductance, and V0 is the vacuum chamber volume.

3. The control method of variable universe fuzzy PID for vacuum suction system according to claim 2, wherein, In step three, the scaling factor α1 of E and the scaling factor β(u) of U are calculated according to e, and the scaling factor α2 of Ec is calculated according to ec, which are as follows: In the formula, the control system precision parameters τ1 and τ2 are constants between 0 and 1; K is a control system sensitivity coefficient; k corresponds to the number of output variables; p j is an input variable weight coefficient, and subscript j corresponds to a different output variable corresponding to a scaling factor; β(0) is an initial value of an output domain scaling factor.

4. The control method of variable universe fuzzy PID for vacuum suction system according to claim 3, wherein, In step four, according to α1, α2 and β(u), the domain of E, Ec and U is adjusted respectively, which are as follows: where E(e) is the domain of the adjusted vacuum deviation e, Ec(e) is the domain of the adjusted vacuum deviation rate ec; β(u) is the scaling factor of U, where u represents different output variables, U(e) is the domain of ΔK P , ΔK I , and ΔK D .

5. The control method of variable universe fuzzy PID for vacuum suction system according to claim 4, wherein, In step four, the fuzzy rule base is established according to the PID tuning rule to perform fuzzy processing on e and ec to obtain the bias value ΔK of the PID parameter P , ΔK I , and ΔK D , as follows: Let ε be a constant; When the vacuum degree deviation |e|>0.7E, in order to improve the vacuum response speed of the system, ΔK P =εK P0 , ε=0.1-0.5; at the same time, in order to reduce the overshoot of the system, ΔK I =-εK I0 , ε=0.6-0.8; When the vacuum degree deviation satisfies 0.3E > |e| ≥ 0.7E, ΔK P = -εK P0 , ε = 0 ~ 0.4, to prevent the system from having an excessive overshoot; at the same time, in order to ensure the rapid response capability of the system, ΔK I = -εK I0 , ε = 0.4 ~ 0.6, ΔK D = -εK D0 , ε = 0.5 ~ 0.8; When the vacuum degree deviation |e|≤0.3E, the system approaches the target vacuum degree, in order to make the vacuum degree control more stable, take ΔK P =-εK P0 , ε=0.2~0.5, ΔK I =-εK I0 , ε=0.4~0.6; at the same time, in order to prevent the system from appearing oscillation phenomenon, the value of ΔK D should be selected according to the vacuum degree deviation change rate |ec|: when the vacuum degree deviation change rate |ec|≤0.5Ec, take ΔK D =εK D0 , ε=0.5~1.0; when the vacuum degree deviation change rate 0.7>|ec|>0.5Ec, ΔK D =εK D0 , ε=-0.3~0.3; when the vacuum degree deviation change rate |ec|≥0.7Ec, take ΔK D =-εK D0 , ε=0.3~0.5; The fuzzy processing adopts 7 fuzzy subsets NB, NM, NS, ZO, PS, PM and PB to perform fuzzy calculation on the deviation value of the PID parameters.

6. The control method of variable universe fuzzy PID for vacuum suction system according to claim 5, wherein, In step five, the initial parameters of the initial PID controller are adjusted according to the deviation values ΔK P , ΔK I and ΔK D to build an adjusted PID controller, as follows: where K P1 , K I1 , and K D1 are adjusted PID parameters; K P0 , K I0 , and K D0 are initial parameters of the initial PID controller.

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