A pendulum valve pressure system control method

Abstract

The present invention provides a pendulum valve pressure system control method, which relates to the field of vacuum system control technology. The pendulum valve pressure system control method comprises the steps of: obtaining the optimal control parameters of an ADRC controller based on a PSOBAS algorithm; after inputting the optimal control parameters into the ADRC controller, obtaining the opening value of the pendulum valve through the ADRC controller; and controlling the pendulum valve according to the opening value. The pendulum valve pressure system control method of the present invention solves the problem that the existing control method cannot well suppress the nonlinear influence of the system, and realizes higher precision and faster gas pressure regulation.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum system control, and in particular to a pendulum valve pressure system control method. Background Art

[0002] Gas pressure control systems have characteristics such as nonlinearity and pure hysteresis. Traditional control methods often face challenges that are difficult to overcome, which limits the precise control capabilities of gas systems. Especially inside vacuum equipment, high-precision control of gas pressure has become a technical difficulty, and the pendulum valve is a key control component, and its performance directly affects the control accuracy and stability of the entire system.

[0003] In the existing control method for the vacuum pressure system based on the pendulum valve, the control strategy cannot effectively suppress the nonlinear influence of the system, and thus cannot achieve higher precision and faster gas pressure regulation.

[0004] There is currently no effective technical solution to the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a pendulum valve pressure system control method, which solves the problem that the existing control method cannot well suppress the nonlinear influence of the system and realizes higher precision and faster gas pressure regulation.

[0006] In a first aspect, the present invention provides a pendulum valve pressure system control method, which is applied to a vacuum pressure system, wherein the vacuum pressure system includes a pendulum valve and an ADRC controller;

[0007] The pendulum valve pressure system control method comprises the following steps:

[0008] S1. Obtaining the optimal control parameters of the ADRC controller based on the PSOBAS algorithm;

[0009] S2. After the optimal control parameters are input into the ADRC controller, the opening value of the pendulum valve is obtained by the ADRC controller;

[0010] S3. Control the pendulum valve according to the opening value.

[0011] The pendulum valve pressure system control method of the present invention optimizes the control parameters of the ADRC controller based on the PSOBAS algorithm, thereby realizing adaptive control of the pendulum valve action, reducing the lag time of the pendulum valve and suppressing the nonlinear influence of the system, thereby achieving better control effect.

[0012] Furthermore, the specific steps in step S1 include:

[0013] S11. The randomly generated plurality of population particles are used as the mass center of a plurality of individual beetles and form a beetle group, wherein the population particles are multidimensional vectors containing a plurality of parameters; the plurality of the parameters are used as control parameters of the ADRC controller;

[0014] S12. After step S11, the following steps are executed in a loop:

[0015] S121. Taking the current position of each of the longhorn beetle individuals as the individual optimal position, obtaining the individual optimal fitness of each of the longhorn beetle individuals at the corresponding individual optimal position and recording it as the first fitness;

[0016] S122. Taking the individual optimal position of the longhorn beetle individual corresponding to the current minimum individual optimal fitness as the group optimal position, obtaining the group optimal fitness of the longhorn beetle group at the group optimal position and recording it as the second fitness;

[0017] S123. Calculate the left antenna position and the right antenna position of each of the longhorn beetle individuals;

[0018] S124. According to the left antenna fitness corresponding to the left antenna position of each of the longhorn beetle individuals and the right antenna fitness corresponding to the right antenna position, a new position of the corresponding longhorn beetle individual is obtained and recorded as the first target position;

[0019] S125. Obtaining the individual optimal fitness of each of the longicorn individuals when corresponding to the first target position and recording it as the third fitness;

[0020] S126. Compare the third fitness and the first fitness of the same longhorn beetle individual, and when the third fitness is less than the first fitness, update the individual optimal position of the corresponding longhorn beetle individual to the corresponding first target position, otherwise keep the individual optimal position of the corresponding longhorn beetle individual unchanged;

[0021] S127. After step S126, the third fitness of each of the longicorn individuals is compared with the second fitness, and when the third fitness is less than the second fitness, the optimal position of the group is updated to the corresponding first target position;

[0022] S128. After step S127, according to the individual optimal position and the group optimal position, the new position of each of the longicorn individuals is obtained again and recorded as the second target position;

[0023] S129. Iteratively update the current position of each of the longhorn beetle individuals to the corresponding second target position, and when the number of iterations reaches the maximum number of iterations, exit the loop and output the final optimal position of the group, otherwise return to execute step S121;

[0024] S13. Taking the parameters contained in the population particles corresponding to the final optimal position of the population as the optimal control parameters.

[0025] Furthermore, the specific steps in step S123 include:

[0026] S1231. Calculate the left antenna position and the right antenna position of each of the longhorn beetle individuals according to the following formula:

[0027] ;

[0028] ;

[0029] in, is the position of the left antenna of the i-th longicorn individual after the j-th iteration, is the centroid position of the i-th longhorn beetle individual after the j-th iteration, is the distance between the left and right tentacles, is the walking step length of the i-th beetle individual after the j-th iteration, is the position of the right antenna of the i-th longicorn individual after the j-th iteration, j represents the current iteration number, and J is the maximum iteration number.

[0030] Furthermore, the specific steps in step S124 include:

[0031] S1241. Calculate the first target position of each of the longicorn individuals according to the following formula:

[0032] ;

[0033] ;

[0034] in, is the first target position of the i-th longhorn beetle individual after the j+1th iteration, is the position increment of the i-th individual beetle after the j-th iteration, Expressed as about and The symbol function of is the fitness of the left antenna of the i-th beetle individual after the j-th iteration, is the fitness of the right antenna of the i-th longicorn individual after the j-th iteration.

[0035] Furthermore, the specific steps in step S128 include:

[0036] S1281. Calculate the second target position of each of the longicorn individuals according to the following formula:

[0037] ;

[0038] in, is the second target position of the i-th longicorn individual after the j+1th iteration, is the inertia factor after the j+1th iteration, is the first learning factor, Expressed as about and The random function of is the optimal position of the i-th beetle individual after the j-th iteration, is the second learning factor, Expressed as about and The random function of is the optimal position of the group after the jth iteration, is the third learning factor, Expressed as about A random function.

[0039] Furthermore, the specific steps in step S2 include:

[0040] S21. Smoothing the input signal to obtain a smooth tracking signal and a differential signal; the input signal is the target pressure data sent by the host computer;

[0041] S22. Obtain disturbance compensation through a preset linear extended state observer;

[0042] S23. According to the tracking signal and the differential signal, a control signal is obtained by a preset nonlinear state error feedback control law;

[0043] S24. According to the disturbance compensation and the control signal, the opening value is obtained by using the ADRC controller with the optimal control parameters.

[0044] Furthermore, the specific steps in step S21 include:

[0045] S211. Obtain the tracking signal and the differential signal according to the following formula:

[0046] ;

[0047] ;

[0048] in, To track the corresponding value of the signal at time t+1, To track the corresponding value of the signal at time t, is the integration step length, is the corresponding value of the differential signal at time t, is the corresponding value of the differential signal at time t+1, Expressed as about , , and The fastest comprehensive function of , , and are all intermediate variables. is the preset value at time t, is the speed factor, is the first filter factor.

[0049] Furthermore, the parameters included in the population particles include a first feedback coefficient, a second feedback coefficient and a third feedback coefficient;

[0050] In step S22, the expression of the linear extended state observer is:

[0051] ;

[0052] in, is the pressure deviation at time t, is the pressure value observed by the linear expansion state observer at time t, is the cavity pressure at time t, is the pressure value observed by the linear expansion state observer at time t+1, is the pressure differential value observed by the linear extended state observer at time t, is the first feedback coefficient, is the pressure differential value observed by the linear extended state observer at time t+1, is the disturbance compensation at time t, is the second feedback coefficient, Expressed as about , and A nonlinear function of is the first nonlinear factor, is the second filter factor, is the pressure regulating valve gain coefficient, is the disturbance-free pressure output compensation at time t, is the disturbance compensation at time t+1, is the third feedback coefficient, Expressed as about , and A nonlinear function of is the second nonlinear factor.

[0053] Furthermore, the parameters of the population particles include proportional coefficients and differential coefficients;

[0054] In step S23, the expression of the nonlinear state error feedback control law is:

[0055] ;

[0056] in, is the pressure observation deviation at time t, is the pressure differential observation deviation at time t, is the corresponding value of the control signal at time t, is the proportionality coefficient, Expressed as about , and A nonlinear function of is the differential coefficient, Expressed as about , and The nonlinear function of .

[0057] Furthermore, the parameters included in the population particles also include a gain coefficient;

[0058] The specific steps in step S24 include:

[0059] S241. The ADRC controller calculates the opening value according to the following formula:

[0060] ;

[0061] in, is the opening value at the tth moment, is the gain coefficient.

[0062] From the above, it can be seen that the pendulum valve pressure system control method provided by the present invention applies an ADRC controller with anti-disturbance and decoupling functions to the pendulum valve pressure system, and optimizes the parameters of the ADRC controller through the PSOBAS algorithm, so that the ADRC controller has better control performance, thereby optimizing the dynamic characteristics of the pendulum valve, which is beneficial to reducing the lag time of the pendulum valve response, and can effectively suppress the nonlinear influence in the system, thereby achieving higher precision and faster gas pressure regulation.

[0063] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart of a pendulum valve pressure system control method provided in an embodiment of the present invention.

[0065] Figure 2 Schematic diagram of the structure of the pendulum valve pressure system in an embodiment of the present invention.

[0066] Description of labels:

[0067] 100, pendulum valve; 200, ADRC controller; 300, cavity; 400, flow meter; 500, pressure sensor; 600, ADC conversion module; 700, host computer; 800, actuator; 900, vacuum pump. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0069] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0070] Reference Figure 1 and attached Figure 2 , the present invention provides a pendulum valve pressure system control method, which is applied to a vacuum pressure system, the vacuum pressure system includes a pendulum valve 100 and an ADRC controller 200;

[0071] The pendulum valve pressure system control method comprises the following steps:

[0072] S1. Obtain the optimal control parameters of the ADRC controller based on the PSOBAS algorithm;

[0073] S2. After the optimal control parameters are input into the ADRC controller, the opening value of the pendulum valve is obtained through the ADRC controller;

[0074] S3. Control the pendulum valve according to the opening value.

[0075] This embodiment applies the ADRC control mode with anti-disturbance and decoupling functions to the pendulum valve pressure control system, which can effectively improve the pressure control accuracy of the system. At the same time, the PSOBAS algorithm is used to optimize the control parameters of the ADRC controller, thereby further improving the control performance of the system. It aims to optimize the dynamic characteristics of the valve and reduce the lag time of the valve response. Through the adaptive control strategy, the nonlinear influence of the system is effectively suppressed, achieving a higher precision and faster gas pressure regulation effect.

[0076] The vacuum pressure system also includes a cavity 300 connected to the pendulum valve 100. An external gas source is quantitatively input into the cavity 300 through a flow meter 400. A pressure sensor 500 measures the cavity air pressure in real time. The cavity air pressure is converted into a digital signal through an ADC conversion module 600 and input into the ADRC controller 200 together with the target pressure data sent by the host computer 700. After obtaining the optimal control parameters of the ADRC controller 200 through the PSOBAS algorithm, the ADRC controller 200 controls the movement of the pendulum valve 100 through the actuator 800 according to the optimal control parameters, thereby driving the vacuum pump 900 to control the system pressure.

[0077] In some embodiments, the specific steps in step S1 include:

[0078] S11. The randomly generated multiple population particles are used as the mass center of multiple longhorn beetle individuals to form a longhorn beetle group, and the population particles are multidimensional vectors containing multiple parameters; the multiple parameters are used as control parameters of the ADRC controller;

[0079] S12. After step S11, the following steps are executed in a loop:

[0080] S121. Taking the current position of each individual longhorn beetle as the individual optimal position, obtaining the individual optimal fitness of each individual longhorn beetle at the corresponding individual optimal position and recording it as the first fitness;

[0081] S122. Taking the individual optimal position of the longhorn beetle individual corresponding to the current minimum individual optimal fitness as the group optimal position, obtaining the group optimal fitness of the longhorn beetle group when it is at the group optimal position and recording it as the second fitness;

[0082] S123. Calculate the left antenna position and the right antenna position of each individual longhorn beetle;

[0083] S124. According to the left antenna fitness corresponding to the left antenna position of each longhorn beetle individual and the right antenna fitness corresponding to the right antenna position, a new position of the corresponding longhorn beetle individual is obtained and recorded as the first target position;

[0084] S125. Obtain the individual optimal fitness of each longhorn beetle individual when corresponding to the first target position and record it as the third fitness;

[0085] S126. Compare the third fitness and the first fitness of the same longhorn beetle individual, and when the third fitness is less than the first fitness, update the individual optimal position of the corresponding longhorn beetle individual to the corresponding first target position, otherwise keep the individual optimal position of the corresponding longhorn beetle individual unchanged;

[0086] S127. After step S126, the third fitness of each individual longhorn beetle is compared with the second fitness, and when the third fitness is less than the second fitness, the optimal position of the group is updated to the corresponding first target position;

[0087] S128. After step S127, according to the individual optimal position and the group optimal position, the new position of each longhorn beetle individual is obtained again and recorded as the second target position;

[0088] S129. Iteratively update the current position of each individual longhorn beetle to the corresponding second target position, and when the number of iterations reaches the maximum number of iterations, exit the loop and output the final optimal position of the group, otherwise return to step S121;

[0089] S13. The parameters contained in the population particles corresponding to the final optimal position of the population are used as the optimal control parameters.

[0090] In this embodiment, PSOBAS is initialized and the population particles are set to include (proportional coefficient), (differential coefficient), (first feedback coefficient), (second feedback coefficient) and (The third feedback coefficient) is a five-dimensional vector of five parameters. The maximum velocity of the population particles is one tenth of the value range of the corresponding dimension. On this basis, the positions and velocities of multiple population particles are randomly generated.

[0091] According to the current positions of individual beetles and groups of beetles, the fitness of each individual beetle and group of beetles is determined by the ADRC controller, and the optimal position of the group is determined according to the fitness.

[0092] In this embodiment, five parameters are used as the five dimensions of a population particle, and the performance index of the controlled object is used as the fitness function. Each population particle updates its own optimization process according to the BAS algorithm, and updates the optimal position of the group through the PSO algorithm, so as to continuously approach the optimal value. The integral of the absolute value of the error and the integral of the square of the error are used to select the objective function of the ADRC controller. The purpose of the algorithm optimization is to find the specific values ​​of the five parameters that minimize the integral of the absolute value of the error and the integral of the square of the error through continuous iteration, thereby obtaining the optimal control parameters.

[0093] Furthermore, the specific steps in step S123 include:

[0094] S1231. Calculate the left antenna position and the right antenna position of each longhorn beetle individual according to the following formula:

[0095] ;

[0096] ;

[0097] in, is the position of the left antenna of the i-th longicorn individual after the j-th iteration, is the centroid position of the i-th longhorn beetle individual after the j-th iteration, is the distance between the left and right tentacles, is the walking step length of the i-th beetle individual after the j-th iteration, is the position of the right antenna of the i-th longicorn individual after the j-th iteration, j represents the current iteration number, and J is the maximum iteration number.

[0098] It should be noted that when j = 0, it means that the longhorn beetle individual has not been iterated, and it represents the initial state of each longhorn beetle individual when multiple population particles are randomly generated.

[0099] Furthermore, the specific steps in step S124 include:

[0100] S1241. Calculate the first target position of each longhorn beetle individual according to the following formula:

[0101] ;

[0102] ;

[0103] in, is the first target position of the i-th longhorn beetle individual after the j+1th iteration, is the position increment of the i-th individual beetle after the j-th iteration, Expressed as about and The symbol function of is the fitness of the left antenna of the i-th beetle individual after the j-th iteration, is the fitness of the right antenna of the i-th longicorn individual after the j-th iteration.

[0104] Furthermore, the specific steps in step S128 include:

[0105] S1281. Calculate the second target position of each longhorn beetle individual according to the following formula:

[0106] ;

[0107] in, is the second target position of the i-th longicorn individual after the j+1th iteration, is the inertia factor after the j+1th iteration, is the first learning factor, Expressed as about and The random function of is the optimal position of the i-th beetle individual after the j-th iteration, is the second learning factor, Expressed as about and The random function of is the optimal position of the group after the jth iteration, is the third learning factor, Expressed as about A random function.

[0108] Furthermore, ;

[0109] in, is the maximum value of the inertia factor, is the minimum value of the inertia factor.

[0110] In some embodiments, the specific steps in step S2 include:

[0111] S21. Smoothing the input signal to obtain a smooth tracking signal and a differential signal; the input signal is the target pressure data sent by the host computer;

[0112] S22. Obtain disturbance compensation through a preset linear extended state observer;

[0113] S23. According to the tracking signal and the differential signal, a control signal is obtained by a preset nonlinear state error feedback control law;

[0114] S24. According to the disturbance compensation and the control signal, the opening value is obtained by using the ADRC controller with the optimal control parameters.

[0115] In this embodiment, smoothing the input signal can reduce interference, improve signal quality, improve waveform characteristics and improve signal transmission efficiency, thereby making the signal more reliable and convenient for subsequent analysis and processing. Setting a linear extended state observer can estimate and compensate for interference in real time, thereby eliminating system disturbances.

[0116] Furthermore, the specific steps in step S21 include:

[0117] S211. Obtain the tracking signal and the differential signal according to the following formula:

[0118] ;

[0119] ;

[0120] in, To track the corresponding value of the signal at time t+1, To track the corresponding value of the signal at time t, is the integration step length, is the corresponding value of the differential signal at time t, is the corresponding value of the differential signal at time t+1, Expressed as about , , and The fastest comprehensive function of , , and are all intermediate variables. is the preset value at time t, is the speed factor, is the first filter factor.

[0121] It should be noted that t represents a moment, which can be but not limited to a specified time length of seconds, minutes or hours as a moment, for example, 1 second as a moment; or 5 minutes as a moment. The system collects a signal once at each moment to obtain corresponding data.

[0122] Furthermore, is a nonlinear function defined as follows:

[0123] ;

[0124] in, , , , , Also an intermediate variable (i.e. used in algebra and has no specific meaning), is the cavity pressure (the cavity pressure is measured by a pressure sensor), is the first nonlinear factor, is the second nonlinear factor, and Both said about and The symbol function of and Both said about and The symbol function of Expressed as the fastest comprehensive function, Indicates about The symbol function of .

[0125] by For example, The following conditions are met:

[0126] ;

[0127] The same applies to , , and .

[0128] Furthermore, the parameters included in the population particles include a first feedback coefficient, a second feedback coefficient, and a third feedback coefficient;

[0129] In step S22, the expression of the linear extended state observer is:

[0130] ;

[0131] in, is the pressure deviation at time t, is the pressure value observed by the linear expansion state observer at time t, is the cavity pressure at time t, is the pressure value observed by the linear expansion state observer at time t+1, is the differential pressure value observed by the linear expansion state observer at time t, is the first feedback coefficient, is the pressure differential value observed by the linear expansion state observer at time t+1, is the disturbance compensation at time t, is the second feedback coefficient, Expressed as about , and A nonlinear function of is the first nonlinear factor, is the second filter factor, is the pressure regulating valve gain coefficient, is the disturbance-free pressure output compensation at time t, is the disturbance compensation at time t+1, is the third feedback coefficient, Expressed as about , and A nonlinear function of is the second nonlinear factor.

[0132] It should be noted that the linear extended state observer is mainly used to deal with complex systems and uncertainty problems. Its basic principle is to simplify the system state equation through the input and output signals of the system, and to estimate the disturbance as an additional state variable of the system. It regards the disturbance and uncertainty in the system as additional state variables, and estimates the dynamic behavior of the system more accurately by expanding the state space. The linear extended state observer is an existing technology and will not be described in detail here.

[0133] Further, For example, The following conditions are met:

[0134] ;

[0135] Furthermore, the parameters of the population particles include proportional coefficients and differential coefficients;

[0136] In step S23, the expression of the nonlinear state error feedback control law is:

[0137] ;

[0138] in, is the pressure observation deviation at time t, is the pressure differential observation deviation at time t, is the corresponding value of the control signal at time t, is the proportionality coefficient, Expressed as about , and A nonlinear function of is the differential coefficient, Expressed as about , and The nonlinear function of .

[0139] The nonlinear state error feedback control law of this embodiment refers to the PD control link in PID control. In order to meet the requirements of nonlinear control, a nonlinear function is used. and It replaces the proportional and integral calculations in PD.

[0140] In some embodiments, the parameters included in the population particles also include a gain coefficient;

[0141] The specific steps in step S24 include:

[0142] S241.ADRC controller calculates the opening value according to the following formula:

[0143] ;

[0144] in, is the opening value at the tth moment, is the gain factor.

[0145] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0146] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pendulum valve pressure system control method, applied to a vacuum pressure system, characterized in that: The vacuum pressure system includes a pendulum valve and an ADRC controller; The pendulum valve pressure system control method comprises the following steps: S1. Obtaining the optimal control parameters of the ADRC controller based on the PSOBAS algorithm; S2. After the optimal control parameters are input into the ADRC controller, the opening value of the pendulum valve is obtained by the ADRC controller; S3. Controlling the pendulum valve according to the opening value; The specific steps in step S1 include: S11. The randomly generated plurality of population particles are used as the mass center of a plurality of individual beetles and form a beetle group, wherein the population particles are multidimensional vectors containing a plurality of parameters; the plurality of the parameters are used as control parameters of the ADRC controller; S12. After step S11, the following steps are executed in a loop: S121. Taking the current position of each of the longhorn beetle individuals as the individual optimal position, obtaining the individual optimal fitness of each of the longhorn beetle individuals at the corresponding individual optimal position and recording it as the first fitness; S122. Taking the individual optimal position of the longhorn beetle individual corresponding to the current minimum individual optimal fitness as the group optimal position, obtaining the group optimal fitness of the longhorn beetle group at the group optimal position and recording it as the second fitness; S123. Calculate the left antenna position and the right antenna position of each of the longhorn beetle individuals; S124. According to the left antenna fitness corresponding to the left antenna position of each of the longhorn beetle individuals and the right antenna fitness corresponding to the right antenna position, a new position of the corresponding longhorn beetle individual is obtained and recorded as the first target position; S125. Obtaining the individual optimal fitness of each of the longicorn individuals when corresponding to the first target position and recording it as the third fitness; S126. Compare the third fitness and the first fitness of the same longhorn beetle individual, and when the third fitness is less than the first fitness, update the individual optimal position of the corresponding longhorn beetle individual to the corresponding first target position, otherwise keep the individual optimal position of the corresponding longhorn beetle individual unchanged; S127. After step S126, the third fitness of each of the longicorn individuals is compared with the second fitness, and when the third fitness is less than the second fitness, the optimal position of the group is updated to the corresponding first target position; S128. After step S127, according to the individual optimal position and the group optimal position, the new position of each of the longicorn individuals is obtained again and recorded as the second target position; S129. Iteratively update the current position of each of the longhorn beetle individuals to the corresponding second target position, and when the number of iterations reaches the maximum number of iterations, exit the loop and output the final optimal position of the group, otherwise return to execute step S121; S13. Taking the parameters contained in the population particles corresponding to the final optimal position of the population as the optimal control parameters.

2. The pendulum valve pressure system control method according to claim 1, characterized in that: The specific steps in step S123 include: S1231. Calculate the left antenna position and the right antenna position of each of the longhorn beetle individuals according to the following formula: ； ； in, is the position of the left antenna of the i-th longicorn individual after the j-th iteration, is the centroid position of the i-th longhorn beetle individual after the j-th iteration, is the distance between the left and right tentacles, is the walking step length of the i-th beetle individual after the j-th iteration, is the position of the right antenna of the i-th longicorn individual after the j-th iteration, j represents the current iteration number, and J is the maximum iteration number.

3. The pendulum valve pressure system control method according to claim 2, characterized in that: The specific steps in step S124 include: S1241. Calculate the first target position of each of the longicorn individuals according to the following formula: ； ； in, is the first target position of the i-th longhorn beetle individual after the j+1th iteration, is the position increment of the i-th beetle individual after the j-th iteration, Expressed as about and The symbol function of is the fitness of the left antenna of the i-th beetle individual after the j-th iteration, is the fitness of the right antenna of the i-th longicorn individual after the j-th iteration.

4. The pendulum valve pressure system control method according to claim 3, characterized in that: The specific steps in step S128 include: S1281. Calculate the second target position of each of the longicorn individuals according to the following formula: ； in, is the second target position of the i-th longicorn individual after the j+1th iteration, is the inertia factor after the j+1th iteration, is the first learning factor, Expressed as about and The random function of is the optimal position of the i-th beetle individual after the j-th iteration, is the second learning factor, Expressed as about and The random function of is the optimal position of the group after the jth iteration, is the third learning factor, Expressed as about A random function.

5. The pendulum valve pressure system control method according to claim 1, characterized in that: The specific steps in step S2 include: S21. Smoothing the input signal to obtain a smooth tracking signal and a differential signal; the input signal is the target pressure data sent by the host computer; S22. Obtain disturbance compensation through a preset linear extended state observer; S23. According to the tracking signal and the differential signal, a control signal is obtained by a preset nonlinear state error feedback control law; S24. According to the disturbance compensation and the control signal, the opening value is obtained by using the ADRC controller with the optimal control parameters.

6. The pendulum valve pressure system control method according to claim 5, characterized in that: The specific steps in step S21 include: S211. Obtain the tracking signal and the differential signal according to the following formula: ； ； in, To track the corresponding value of the signal at time t+1, To track the corresponding value of the signal at time t, is the integration step length, is the corresponding value of the differential signal at time t, is the corresponding value of the differential signal at time t+1, Expressed as about , , and The fastest comprehensive function of , , and are all intermediate variables. is the preset value at time t, is the speed factor, is the first filter factor.

7. The pendulum valve pressure system control method according to claim 6, characterized in that: The parameters included in the population particles include a first feedback coefficient, a second feedback coefficient and a third feedback coefficient; In step S22, the expression of the linear extended state observer is: ； in, is the pressure deviation at time t, is the pressure value observed by the linear expansion state observer at time t, is the cavity pressure at time t, is the pressure value observed by the linear expansion state observer at time t+1, is the pressure differential value observed by the linear extended state observer at time t, is the first feedback coefficient, is the pressure differential value observed by the linear extended state observer at time t+1, is the disturbance compensation at time t, is the second feedback coefficient, Expressed as about , and A nonlinear function of is the first nonlinear factor, is the second filter factor, is the pressure regulating valve gain coefficient, is the disturbance-free pressure output compensation at time t, is the disturbance compensation at time t+1, is the third feedback coefficient, Expressed as about , and A nonlinear function of is the second nonlinear factor.

8. The pendulum valve pressure system control method according to claim 7, characterized in that: The parameters of the population particles also include proportional coefficients and differential coefficients; In step S23, the expression of the nonlinear state error feedback control law is: ； in, is the pressure observation deviation at time t, is the pressure differential observation deviation at time t, is the corresponding value of the control signal at time t, is the proportionality coefficient, Expressed as about , and A nonlinear function of is the differential coefficient, Expressed as about , and The nonlinear function of .

9. The pendulum valve pressure system control method according to claim 8, characterized in that: The specific steps in step S24 include: S241. The ADRC controller calculates the opening value according to the following formula: ； in, is the opening value at the tth moment, is the gain factor.

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