Valve core position control method and control system

Abstract

This invention relates to the field of intelligent control algorithm technology, specifically to a valve core position control method and control system. The method includes: S1 establishing a mathematical model of a proportional cartridge valve; S2 determining the input value, output value, multiple parameters affecting the output value, and external parameter factors; S3 designing a PID controller and determining variable parameters; S4 determining a neural network optimization algorithm scheme and building an algorithm model; S5 building a fuzzy controller based on the mathematical model of the proportional cartridge valve, the PID controller, and the neural network optimization algorithm; and S6 connecting different modules to form a whole. The position control method of this invention comprehensively considers factors, fully taking into account external parameters and multiple parameters affecting the output. It predictively regulates the input parameters of the fuzzy PID control, thereby achieving higher control accuracy and faster response speed, greatly improving the position control of the proportional cartridge valve core, and thus improving the overall system performance.

Description

Technical Field

[0001] This invention relates to the field of intelligent control algorithm technology, specifically to a valve core position control method and its control system. Background Technology

[0002] Electro-hydraulic proportional control elements regulate the pressure and flow of hydraulic systems by changing the position of the valve core and controlling the flow rate of hydraulic fluid within the hydraulic components. Proportional cartridge valves are widely used in the die-casting industry. Die-casting speed refers to the speed at which molten metal is injected into the mold. A suitable die-casting speed ensures sufficient filling of the molten metal and reduces air bubbles and gas in the liquid. Therefore, the proportional cartridge valve for controlling the die-casting speed is particularly important, and the rapid and precise control of the valve core position needs to be a key consideration. Patent No. CN118363297A discloses a motion control system based on a PID control algorithm. This method applies fuzzy PID control to control the position of a hydraulic cylinder, significantly improving control accuracy and response speed compared to traditional PID algorithms.

[0003] However, this patent cannot perform the corresponding prediction function when affected by external factors and other multi-parameter factors caused by the input. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a valve core position control method and control system. By using a neural network optimization algorithm to predict the valve core position in advance, the optimized value is input into a fuzzy controller, thereby dynamically adjusting the PID parameters and achieving high-response, high-precision proportional cartridge valve core position control.

[0005] The present invention adopts the following solution:

[0006] One aspect of the present invention provides a method for controlling the position of a valve core, the method comprising the following steps:

[0007] Step S1: Establish the mathematical model of the valve;

[0008] Step S2: Determine the input value, the initial output value, the multiple parameters affected by the initial output value, and external parameter factors;

[0009] Step S3: Design a PID controller and determine the variable parameters of the PID controller;

[0010] Step S4: Determine the neural network optimization algorithm and build the algorithm model to obtain the neural network optimization model;

[0011] Step S5: Build a fuzzy controller based on the valve's mathematical model, PID controller, and neural network optimization model;

[0012] Step S6 involves connecting the PID controller, fuzzy controller, and neural network optimization model to form a unified system and optimizing the valve core position control. Specifically, this includes:

[0013] Step S61: Input the input values, the multiple parameters that affect the output values, and the external parameter factors into the neural network optimization model, and use the output of the neural network optimization model as the predicted value.

[0014] Step S62: The difference between the predicted value and the output value is processed to obtain the error and the rate of change of error. The error and the rate of change of error are then input into the fuzzy controller as input variables.

[0015] Step S63: The fuzzy controller processes the input variables to obtain f. kp f ki and f kd The input is fed into the PID controller to control the valve core movement, obtain the real-time output value, and update the multiple parameters affected by the output value based on the real-time output value;

[0016] Step S64: Compare the real-time output value with the predicted value. If the target requirement is met, the valve core stops displaced; otherwise, return to step S61 and repeat until the target requirement is met.

[0017] Furthermore, the establishment of the mathematical model of the valve in step S1 specifically includes:

[0018] Establish the flow-pressure equations for the valve and perform a Laplace transform:

[0019] Flow-pressure equation: Q L =K q x v -K c P L ;

[0020] Laplace transform: Q L (s)=K q x v (s)-K c P L (s);

[0021] Among them, Q L To control the flow rate through the pilot valve, K q For flow gain, K C For the pressure-flow coefficient, x v P is the output displacement of the pilot valve. L For load pressure;

[0022] Establish the valve's flow equation and perform a Laplace transform:

[0023] Flow equation:

[0024] Laplace transform:

[0025] Among them, A p x is the effective area of ​​the valve core. p For valve core displacement, β e V is the bulk modulus of hydraulic oil. t To control the oil chamber volume in the pilot section, C t The total leakage coefficient;

[0026] Establish the force balance equations for the valve and perform a Laplace transform:

[0027] Force balance equations:

[0028] Laplace transform: A1P L (s)=(M t s 2 +B p s+k)x p (s)+F L ;

[0029] Among them, M t B is the total mass of the valve core and the mass transferred to the valve core by the load. p Let F be the coefficient of viscous friction of the valve core and other moving parts such as the load, k be the elastic load stiffness, and F be the coefficient of viscous friction of the moving parts such as the valve core and the load. L For other load forces acting on the valve core, x p For valve core displacement;

[0030] The pilot valve core displacement x is obtained from the above formula. v (s) to the displacement x of the main valve spool p The transfer function of (s):

[0031]

[0032] Among them, K ce =K c +C t A is the total pressure-flow coefficient, including leakage. p This represents the effective area of ​​the valve core.

[0033] Furthermore, the determination of input values, output values, multiple parameters affecting the output values, and external parameter factors in step S2 specifically includes:

[0034] The input value is the target position value of the proportional cartridge valve spool.

[0035] The output value is the real-time position value of the proportional cartridge valve spool;

[0036] The output value is affected by multiple parameters, including the real-time pressure changes before and after the valve, vibration amplitude, and flow rate changes.

[0037] External parameters include controller performance parameters, ambient temperature, and oil temperature.

[0038] Furthermore, in step S3, designing the PID controller and determining the variable parameters specifically includes:

[0039] The deviation between the system's input and output is used as the controller's input. The input to the control system is tuned using proportional, integral, and derivative components. The resulting output value is then applied to the controlled object to achieve closed-loop control of the system. The relationship between the PID controller's output u and input e is as follows:

[0040]

[0041] Among them, K P =k p +f kp , K I =k i +f ki , K D =k d +f kd t is time, k p The initial input to the system is a fixed proportional coefficient, k i The system is initially input with fixed integral coefficients, k d The system has fixed initial input differential coefficients, f kp f is the real-time changing fuzzy scaling factor obtained by the fuzzy controller. ki f represents the real-time changing fuzzy integral coefficients obtained by the fuzzy controller. kd These are the real-time changing fuzzy differential coefficients obtained by the fuzzy controller.

[0042] Furthermore, step S4, which involves determining the neural network optimization scheme and building the algorithm model, specifically includes:

[0043] The set of input variables includes real-time pressure changes and vibration amplitudes before and after the valve, as well as controller performance parameters, ambient temperature, oil temperature, and flow rate changes.

[0044] During the model debugging phase, the network performance was optimized by adjusting the weights through multiple iterations of training and using the backpropagation algorithm. At the same time, the learning vector quantization algorithm was applied to fine-tune the model, improving classification performance and generalization ability.

[0045] In the operating environment, the trained neural network model will predict the valve core position in real time. By comparing the predicted output value with the input value, the design error value e(t) and its rate of change Δe(t) will be optimized.

[0046] Based on the error analysis results, the parameters of the neural network model are dynamically adjusted.

[0047] Furthermore, the specific steps in step S5 of building the fuzzy controller include:

[0048] The input variables error e(t) and error change rate Δe(t) are used as inputs to the fuzzy controller, and the real-time changing fuzzy scaling coefficient f is obtained by the fuzzy controller. kp Real-time changing fuzzy integral coefficient f ki and the real-time changing fuzzy differential coefficients f kd As output;

[0049] The input and output variables are described using fuzzy language. The fuzzy sets of the input variables e(t) and Δe(t) are defined as negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and are respectively denoted as NB, NM, NS, ZO, PS, PM, and PB. e(t) and Δe(t) are quantized to the [-a, a] region. The output quantity f is defined as... kp f ki f kd The fuzzy sets are {NB, NM, NS, ZO, PS, PM and PB}, which are quantized to the regions [-b, b], [-c, c] and [-d, d] respectively, where a, b, c and d are all positive integers.

[0050] On the other hand, the present invention provides a control system for a valve core position control method, which includes: a human-machine interface, a controller, a proportional cartridge valve, a displacement sensor, and other parameter sensors; the human-machine interface communicates with the controller via a communication protocol; the controller reads data from the displacement sensor and other parameter sensors in real time via the SSI protocol, and the controller sends control commands to the valve based on predicted values.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This invention provides a valve core position control method. This method further optimizes fuzzy PID control by using a neural network optimization algorithm to optimize multiple parameters. Based on the initial input target value, the optimized target value is obtained. Optimization starts from the signal input, and real-time position judgment is performed in advance to improve the response speed. The neural network algorithm is used to judge and optimize different situations of fuzzy rules and quantization intervals in the fuzzy controller, further improving the control accuracy and control performance of the fuzzy controller.

[0053] This invention takes into account all factors, has high control precision, and fast response speed, which greatly improves the position control of proportional cartridge valves and thus improves the overall system performance. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a structural diagram of the proportional cartridge valve of the present invention;

[0056] Figure 2 This is a simplified schematic diagram of the proportional cartridge valve controlling the propulsion cylinder of the present invention;

[0057] Figure 3 This is a diagram of the proportional cartridge valve control system of the present invention;

[0058] Figure 4 This is a block diagram of the PID control of the present invention;

[0059] Figure 5 A control diagram for the spool position of a proportional cartridge valve based on multi-parameter objective optimization using fuzzy PID control;

[0060] Figure 6 This is a flowchart of the valve core position control method of the present invention;

[0061] Figure 7 This is a flowchart of step six in the valve core position control method of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the specific embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0063] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0064] This embodiment uses the control of a proportional cartridge valve for illustration. Figure 1This is a structural diagram of a proportional cartridge valve. In the diagram, 1 is the pilot valve, 2 is the displacement sensor, 3 is the valve cover, 4 is the valve core, and 5 is the valve sleeve. The proportional cartridge valve is mainly composed of the pilot valve 1, the displacement sensor 2, the valve cover 3, the valve core 4, and the valve sleeve 5. The pilot valve 1 controls the valve core 4 to control the flow in the upper and lower chambers, thereby causing the valve core 4 to move. The displacement sensor 2 provides real-time feedback on the position of the valve core 4.

[0065] Figure 2 This is a simplified schematic diagram of a proportional cartridge valve controlling a propulsion cylinder. The control signal controls the opening of the proportional cartridge valve, which in turn causes changes in flow and pressure before and after the proportional cartridge valve. Changes in oil flow cause real-time changes in the speed and pressure of the propulsion cylinder. This schematic diagram briefly explains the source and basis of the parameters in the multi-parameter diagram.

[0066] Figure 3 The diagram shows a proportional cartridge valve control system. The controller receives and processes the control program, which transmits signals from the analog quantity change module to the pilot valve and receives real-time position signals from the displacement sensor, which are then input into the controller.

[0067] Figures 1-7 The diagram shown illustrates the structure and composition of a fuzzy PID control system, as disclosed in this invention. It enables real-time adjustment of the proportional parameter K in the PID algorithm. P Integral parameter K I and differential parameter K D The error e(t) and the rate of change of error Δe(t) at different times satisfy the control parameter f. kp f ki f kd The requirement for fuzzy self-tuning enables the proportional cartridge valve spool to reach the target position quickly and efficiently.

[0068] This invention proposes a method for controlling the position of a valve core, the specific steps of which are as follows:

[0069] Step S1: Establish a mathematical model for the proportional cartridge valve.

[0070] Establish the flow-pressure equations for the proportional cartridge valve pilot valve and perform Laplace transform: Flow-pressure equation: Q L =K q x v -K c P L Laplace transform: Q L (s)=K q x v (s)-K c P L (s), where Q L To control the flow rate through the pilot valve, K q For flow gain, K C For the pressure-flow coefficient, xv P is the output displacement of the pilot valve. L This refers to the load pressure.

[0071] Establish the flow equation for the proportional cartridge valve and perform a Laplace transform:

[0072] Flow equation:

[0073] Laplace transform:

[0074] Among them, A p x is the effective area of ​​the valve core. p For valve core displacement, β e V is the bulk modulus of hydraulic oil. t To control the oil chamber volume in the pilot section, C t The total leakage coefficient is denoted as .

[0075] Establish the force balance equations for the proportional cartridge valve and perform a Laplace transform:

[0076] Force balance equations:

[0077] Laplace transform: A1P L (s)=(M t s 2 +B p s+k)x p (s)+F L ,

[0078] Among them, M t B is the total mass of the valve core and the mass transferred to the valve core by the load. p Let F be the coefficient of viscous friction of the valve core and other moving parts such as the load, k be the elastic load stiffness, and F be the coefficient of viscous friction of the moving parts such as the valve core and the load. L Other load forces acting on the valve core.

[0079] The pilot valve core displacement x can be obtained from the above formula. v (s) to the displacement x of the main valve spool p The transfer function of (s):

[0080]

[0081] Among them, K ce =K c +C t , is the total pressure-flow coefficient including leakage.

[0082] Step S2: Determine the input value, output value, multiple parameters affecting the output value, and external parameter factors.

[0083] The input value is the target position value of the proportional cartridge valve spool; the output value is the real-time position value of the proportional cartridge valve spool; the output value affects multiple parameters including the real-time pressure change value before and after the proportional cartridge valve, vibration amplitude, flow change value, etc.; external parameter factors include controller control performance parameters, ambient temperature, oil temperature, etc.

[0084] Step S3: Design a PID controller and determine the variable parameters.

[0085] The deviation between the system's input and output is used as the controller's input. This deviation, the difference between the actual output and the target value, is the input parameter required for PID control. The input to the control system is tuned using proportional, integral, and derivative elements, and the resulting output value is then applied to the controlled object to achieve closed-loop control. The relationship between the PID controller's output u and input e is as follows: where K P =k p +f kp , K I =k i +f ki , K D =k d +f kd t is time, k p The initial input to the system is a fixed proportional coefficient, k i The system is initially input with fixed integral coefficients, k d The system has fixed initial input differential coefficients, f kp f is the real-time changing fuzzy scaling factor obtained by the fuzzy controller. ki f represents the real-time changing fuzzy integral coefficients obtained by the fuzzy controller. kd These are the real-time changing fuzzy differential coefficients obtained by the fuzzy controller.

[0086] Step S4: Determine the neural network optimization scheme and build the algorithm model.

[0087] The appropriate neural network architecture, such as a Long Short-Term Memory (LSTM) network or a Multilayer Feedforward (MLP) network, is selected based on different operating conditions. The input variable set includes real-time pressure changes and vibration amplitudes before and after the proportional cartridge valve, as well as controller performance parameters, ambient temperature, oil temperature, and flow rate changes. During the model debugging phase, multiple iterations of training are conducted, and the backpropagation algorithm is used to adjust weights to optimize network performance. Simultaneously, the Learning Vector Quantization (LVQ) algorithm is applied to fine-tune the model, improving classification performance and generalization ability. In the actual operating environment, the trained neural network model predicts the valve spool position in real time. By comparing the predicted output value with the optimized value, the design error value e(t) and its rate of change Δe(t) are optimized. Based on the error analysis results, the neural network model parameters are dynamically adjusted to achieve precise control of the proportional valve spool position, thereby achieving the control objective of high precision and high response.

[0088] Step S5: Build a fuzzy controller.

[0089] The input variables error e(t) and error change rate Δe(t) are used as inputs to the fuzzy controller, and the real-time changing fuzzy scaling coefficient f is obtained by the fuzzy controller. kp Real-time changing fuzzy integral coefficient f ki Real-time changing fuzzy differential coefficients f kd As output; the input and output variables are described using fuzzy language. The fuzzy sets of the input variables e(t) and Δe(t) are set as negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, denoted as {NB, NM, NS, ZO, PS, PM, PB}. e(t) and Δe(t) are quantized to the [-a, a] region; the output quantity f is set as... kp f ki f kd The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, which are quantized to the regions [-b, b], [-c, c], and [-d, d], respectively, where a, b, c, and d are positive integers, given according to the specific situation.

[0090] According to parameter K P K I K D The impact on the system output characteristics can be summarized as follows: for different e(t) and Δe(t), the controlled parameter f kp f ki f kd The self-tuning requirements and fuzzy control rules are shown in Tables 1, 2, and 3, respectively. The fuzzy control rule is ifA andB then C, for example, e is NB, Δe is NB, and Δf kp It is PB.

[0091] Table 1 △f kp Fuzzy rules

[0092]

[0093] Table 2 △f ki Fuzzy rules

[0094]

[0095] Table 3 △f kd Fuzzy rules

[0096]

[0097] Fuzzy subsets use triangular membership functions. The output after fuzzification is a fuzzy set, which is then defuzzified using the centroid method to determine a precise value.

[0098] Step S6: Connect the different modules to build a whole.

[0099] Input values, changes in multiple parameters affecting the output values, and external parameters are all input into the neural network optimization algorithm. Training, optimization, and processing are performed to obtain the optimized value. The optimized value is then processed with the output feedback value to obtain the error and error rate of change, which are input into the PID controller and the fuzzy controller. The fuzzy controller processes the input variables to obtain f. kp f ki f kd The input is fed into the PID controller to control the proportional cartridge valve spool to actuate and obtain the output value. The output value is compared with the optimized value. If the target requirement is met, the process stops; otherwise, the loop continues.

[0100] This invention has the advantages of high control precision. The control method used is not only applicable to the proportional cartridge valve core position control system proposed in this invention, but also applicable to other position control and speed control fields, such as hydraulic cylinder position control.

[0101] The real-time movement of the proportional cartridge valve spool affects the system, causing changes in upstream and downstream pressures and flow rates. This, in turn, affects the controlled object of the proportional cartridge valve, such as the position, speed, and pressure of the hydraulic cylinder. By pre-processing and optimizing the parameters affected by the proportional cartridge valve's movement, the input target signal can be pre-processed to obtain better control results, thereby improving the performance of the control system. External parameters also affect system performance, such as ambient temperature, liquid properties, and controller performance, including sampling time intervals. These factors require parameter processing and algorithm optimization to obtain the optimal signal for input control.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0103] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling the position of a valve core, characterized in that, It includes the following steps: Step S1: Establish the mathematical model of the valve; Step S2: Determine the input value, the initial output value, the multiple parameters affected by the initial output value, and external parameter factors; Step S3: Design a PID controller and determine the variable parameters of the PID controller; Step S4: Determine the neural network optimization algorithm and build the algorithm model to obtain the neural network optimization model; Determining the neural network optimization scheme and building the algorithm model specifically includes: The set of input variables includes real-time pressure changes and vibration amplitudes before and after the valve, as well as controller performance parameters, ambient temperature, oil temperature, and flow rate changes. During the model debugging phase, the network performance was optimized by adjusting the weights through multiple iterations of training and using the backpropagation algorithm. At the same time, the learning vector quantization algorithm was applied to fine-tune the model, improving classification performance and generalization ability. In the operating environment, the trained neural network model will predict the valve core position in real time. By comparing the predicted output value with the input value, the design error value e(t) and its rate of change Δe(t) will be optimized. Based on the error analysis results, dynamically adjust the parameters of the neural network model; Step S5: Build a fuzzy controller based on the valve's mathematical model, PID controller, and neural network optimization model; Step S6 involves connecting the PID controller, fuzzy controller, and neural network optimization model to form a unified system and optimizing the valve core position control. Specifically, this includes: Step S61: Input the input values, the multiple parameters that affect the output values, and the external parameter factors into the neural network optimization model, and use the output of the neural network optimization model as the predicted value. Step S62: The difference between the predicted value and the output value is processed to obtain the error and the rate of change of error. The error and the rate of change of error are then input into the fuzzy controller as input variables. Step S63: The fuzzy controller processes the input variables to obtain f. kp f ki and f kd The input is fed into the PID controller to control the valve core's movement, obtaining the real-time output value and updating multiple parameters affected by the output value based on the real-time output value; f kp f is the real-time changing fuzzy scaling factor obtained by the fuzzy controller. ki f represents the real-time changing fuzzy integral coefficients obtained by the fuzzy controller. kd These are the real-time changing fuzzy differential coefficients obtained by the fuzzy controller. Step S64: Compare the real-time output value with the predicted value. If the target requirement is met, the valve core stops displaced; otherwise, return to step S61 and repeat until the target requirement is met.

2. The valve core position control method according to claim 1, characterized in that, The specific steps in step S1 to establish the mathematical model of the valve include: Establish the flow-pressure equations for the valve and perform a Laplace transform: Flow-pressure equation: ; Laplace transform: ; Among them, Q L To control the flow rate through the pilot valve, K q For flow gain, K C For the pressure-flow coefficient, x v P is the output displacement of the pilot valve. L For load pressure; Establish the valve's flow equation and perform a Laplace transform: Flow equation: ; Laplace transform: ; Among them, A p x is the effective area of ​​the valve core. p For valve core displacement, β e V is the bulk modulus of hydraulic oil. t To control the oil chamber volume in the pilot section, C t The total leakage coefficient; Establish the force balance equations for the valve and perform a Laplace transform: Force balance equations: ; Laplace transform: ; Among them, M t B is the total mass of the valve core and the mass transferred to the valve core by the load. p Let F be the coefficient of viscous friction of the valve core and load moving parts, k be the elastic load stiffness, and F be the coefficient of viscous friction of the valve core and load moving parts. L For other load forces acting on the valve core, x p For valve core displacement; The pilot valve core displacement x is obtained from the above formula. v (s) to the displacement x of the main valve spool p The transfer function of (s): ； Among them, K ce =K c +C t A is the total pressure-flow coefficient, including leakage. p This represents the effective area of ​​the valve core.

3. The valve core position control method according to claim 1, characterized in that, The specific steps in step S2, including determining the input value, output value, multiple parameters affecting the output value, and external parameter factors, include: The input value is the target position value of the proportional cartridge valve spool. The output value is the real-time position value of the proportional cartridge valve spool; The output value is affected by multiple parameters, including the real-time pressure changes before and after the valve, vibration amplitude, and flow rate changes. External parameters include controller performance parameters, ambient temperature, and oil temperature.

4. The valve core position control method according to claim 1, characterized in that, The design of the PID controller and the determination of variable parameters in step S3 specifically include: The deviation between the system's input and output is used as the controller's input. The input to the control system is tuned using proportional, integral, and derivative components. The resulting output value is then applied to the controlled object to achieve closed-loop control of the system. The relationship between the PID controller's output u and input e is as follows: ； Among them, K P =k p +f kp , K I =k i +f ki , K D =k d +f kd t is time, k p The initial input to the system is a fixed proportional coefficient, k i The system is initially input with fixed integral coefficients, k d The system has fixed initial input differential coefficients, f kp f is the real-time changing fuzzy scaling factor obtained by the fuzzy controller. ki f represents the real-time changing fuzzy integral coefficients obtained by the fuzzy controller. kd These are the real-time changing fuzzy differential coefficients obtained by the fuzzy controller.

5. The valve core position control method according to claim 1, characterized in that, Step S5, which involves building the fuzzy controller, specifically includes: The input variables error e(t) and error change rate Δe(t) are used as inputs to the fuzzy controller, and the real-time changing fuzzy scaling coefficient f is obtained by the fuzzy controller. kp Real-time changing fuzzy integral coefficient f ki and the real-time changing fuzzy differential coefficients f kd As output; The input and output variables are described using fuzzy language. The fuzzy sets of the input variables e(t) and Δe(t) are defined as negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and are respectively denoted as NB, NM, NS, ZO, PS, PM, and PB. e(t) and Δe(t) are quantized to the [-a, a] region. The output quantity f is defined as... kp f ki f kd The fuzzy sets are {NB, NM, NS, ZO, PS, PM and PB}, which are quantized to the regions [-b, b], [-c, c] and [-d, d] respectively, where a, b, c and d are all positive integers.

6. A control system for the valve core position control method of claim 1, characterized in that, It includes: Human-machine interface, controller, proportional cartridge valve, displacement sensor and other parameter sensors; The human-computer interface communicates with the controller via a communication protocol; The controller reads data from the displacement sensor and other parameter sensors in real time via the SSI protocol, and sends control commands to the valve based on the predicted values.

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