Vacuum exhaust throttle valve setting method and system

Abstract

The invention discloses a vacuum exhaust throttle valve setting method and system. The method comprises the steps that the current pressure value in a vacuum pipeline is obtained; calculating a pressure deviation and a pressure deviation change rate according to a preset target pressure value and the collected current pressure value; fuzzifying the calculated pressure deviation and the pressure deviation change rate to obtain a corresponding fuzzy linguistic variable; performing fuzzy reasoning based on a preset fuzzy rule base to obtain a valve opening adjustment amount in a fuzzy form; performing defuzzification processing on the valve opening adjustment amount in the fuzzy form to obtain an accurate valve opening adjustment amount value; and the opening degree of the exhaust throttle valve is adjusted according to the accurate valve opening degree adjustment amount value. The opening degree of the exhaust throttle valve is dynamically adjusted, and rapid and accurate control is achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of industrial automation control, and relates to a vacuum exhaust throttle valve setting method and system. BACKGROUND

[0002] The vacuum exhaust throttle valve is an important component of the industrial vacuum system, and its main function is to adjust the gas flow in the vacuum pipeline by changing the valve opening to achieve specific pressure and flow control. However, the existing technology has the following problems in controlling the vacuum exhaust throttle valve: Manual setting complexity: Traditional vacuum exhaust throttle valve adjustment requires professional personnel to manually set control parameters such as gain parameters in proportional-integral-derivative (PID) control according to actual working conditions. This method is highly dependent on the experience of the operator, and it is difficult to respond quickly in complex and variable working environments.

[0003] Response lag: The nonlinear characteristics of the vacuum system make it difficult for traditional PID control methods to achieve accurate control when the working conditions change rapidly. For example, when the system switches from a high-pressure state to a low-pressure state, the exhaust throttle valve response may lag, resulting in reduced system efficiency.

[0004] Poor environmental adaptability: Industrial vacuum systems often operate under various working conditions, such as high temperature, low pressure, or strong vibration environment. Traditional control methods are difficult to adapt to these complex conditions, which can easily lead to system instability or control errors.

[0005] Energy waste problem: Due to the inability to achieve precise gas flow control, the system often consumes additional energy to compensate for errors, increasing operating costs. SUMMARY

[0006] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art and provide a vacuum exhaust throttle valve setting method and system that dynamically adjusts the exhaust throttle valve opening to achieve fast and accurate control.

[0007] To achieve the above-mentioned purpose, the following technical solutions are adopted: A vacuum exhaust throttle valve setting method, comprising the following processes: Obtain the current pressure value in the vacuum pipeline; Calculate the pressure deviation and pressure deviation change rate based on the preset target pressure value and the collected current pressure value; Fuzzify the calculated pressure deviation and pressure deviation change rate to obtain corresponding fuzzy language variables; Based on the preset fuzzy rule base, perform fuzzy reasoning to obtain a fuzzy form of valve opening adjustment amount; The fuzzy valve opening adjustment amount is defuzzified to obtain a precise valve opening adjustment amount value; Adjust the opening of the exhaust throttle valve according to the precise valve opening adjustment value.

[0008] Preferably, when performing fuzzification processing, the pressure deviation, the rate of change of pressure deviation, and the valve opening adjustment amount are all divided into five fuzzy subsets, namely negative large, negative small, zero, positive small, and positive large.

[0009] Preferably, when performing fuzzification, a membership function is used to define each fuzzy subset; wherein, the membership function of the zero fuzzy subset is a triangular membership function, and the membership functions of the negative and positive fuzzy subsets are trapezoidal membership functions.

[0010] Preferably, the fuzzy rule base includes the following rule: when the pressure deviation is positive and the rate of change of the pressure deviation is negative, the valve opening adjustment is zero.

[0011] Preferably, when performing defuzzification, the weighted average centroid method is used to calculate the accurate valve opening adjustment value.

[0012] A vacuum exhaust throttle valve tuning system includes an exhaust throttle valve body, a sensor, and a fuzzy controller; The sensor is connected to the vacuum pipe to collect real-time pressure values ​​inside the vacuum pipe; The fuzzy controller is electrically connected to the sensor. The fuzzy controller is configured to receive real-time pressure values, calculate pressure deviation and pressure deviation change rate based on preset target pressure values, fuzzify the pressure deviation and pressure deviation change rate, perform fuzzy inference based on the fuzzy rule base to obtain the valve opening adjustment amount, and then defuzzify the valve opening adjustment amount before outputting it. The exhaust throttle valve body is electrically connected to the fuzzy controller to receive the valve opening adjustment amount and perform the opening adjustment.

[0013] Preferably, the sensors include a pressure sensor and a valve position sensor; the pressure sensor is mounted on the vacuum pipeline; the valve position sensor is integrated into the electric actuator of the exhaust throttle valve body.

[0014] Preferably, the fuzzy controller is configured to divide the pressure deviation, the rate of change of pressure deviation, and the valve opening adjustment into five fuzzy subsets: negative large, negative small, zero, positive small, and positive large.

[0015] Preferably, the fuzzy controller is configured with a membership function for defining each fuzzy subset; wherein the membership function of the zero fuzzy subset is a triangular membership function, and the membership functions of the negative and positive fuzzy subsets are trapezoidal membership functions.

[0016] Preferably, the fuzzy rule base configured by the fuzzy controller includes the following rule: when the pressure deviation is positive and the rate of change of the pressure deviation is negative, the valve opening adjustment is zero.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention replaces the traditional linear controller with a nonlinear fuzzy controller. Traditional control methods struggle to establish accurate mathematical models of vacuum systems. This invention, however, uses fuzzification to transform precise pressure deviation and rate of change data into fuzzy language such as negative large, zero, and positive small, and then utilizes a fuzzy rule base for reasoning. This approach does not rely on a precise model but mimics human decision-making, especially by introducing the predictive input of the pressure deviation rate of change, enabling the controller to anticipate system trends. For example, when the pressure deviates from the target but is rapidly approaching it, the controller can brake in advance, reducing adjustment and effectively suppressing overshoot, achieving rapid response and high-precision stable control. This fundamentally solves the limitations of traditional linear control in handling complex nonlinear systems. Attached Figure Description

[0018] Figure 1 This is a structural diagram of the vacuum exhaust throttle valve setting system of the present invention; Figure 2 This is a flowchart of the vacuum exhaust throttle valve setting method of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] like Figure 1As shown, the vacuum exhaust throttle valve setting system described in this embodiment adopts a closed-loop feedback control system, which mainly includes the following core components: Exhaust throttle valve body: This is the system's actuator, consisting of a mechanical valve body and an electric actuator (e.g., a high-precision stepper motor or servo motor). The electric actuator receives electrical signals from the fuzzy controller and converts them into precise mechanical displacement, thereby changing the valve opening and directly regulating the gas flow rate in the vacuum pipeline.

[0022] Sensors: As the system's sensing unit, this module is responsible for monitoring the system status in real time. It contains at least two types of sensors: one is a high-precision pressure sensor installed on the vacuum pipeline, used to measure the absolute pressure value within the pipeline in real time; the other is a valve position sensor (e.g., an encoder or potentiometer) integrated into the electric actuator, used to provide accurate feedback on the current valve opening. This dual feedback mechanism is the foundation for achieving precise control.

[0023] Fuzzy controller: This is the brain of the system, typically implemented on a microcontroller (MCU), programmable logic controller (PLC), or industrial computer (IPC). Its core function is to run a preset fuzzy control algorithm, receive and process real-time data from sensor modules, make decisions based on an internal fuzzy rule base, and ultimately generate control commands to drive the exhaust throttle valve.

[0024] Human-Machine Interface (HMI): This interface (e.g., a touchscreen) provides operators with a window for monitoring and interacting with the system. Its functions include: real-time display of key data such as target pressure, actual pressure, and valve opening percentage; recording historical data curves for process analysis; allowing operators to set target pressure values; and adjusting advanced control parameters or initiating calibration procedures when necessary.

[0025] The system operates on a closed-loop feedback logic. The operator sets the target pressure via an HMI. The sensor module continuously feeds back real-time pressure values ​​and valve positions to the fuzzy controller. The controller internally calculates the deviation between the current pressure and the target pressure, along with its trend, and inputs this information into the fuzzy logic engine. The engine infers from an expert knowledge base (fuzzy rule base) and outputs an optimal valve opening adjustment. This adjustment is sent to the valve actuator, driving the valve to move, thereby changing the gas flow rate in the pipeline and thus affecting the pressure. The pressure sensor detects the pressure change and feeds back the new pressure value to the controller, forming a continuous closed-loop regulation process until the system pressure stabilizes near the target value.

[0026] The fuzzy controller is the core of achieving the technical effects of this invention. Its design mainly includes four key steps: defining input and output variables, fuzzification, fuzzy inference, and defuzzification.

[0027] Definition and quantification of input and output variables.

[0028] To achieve effective control of the system, the controller needs two input variables to fully describe the system state and an output variable to execute control actions.

[0029] Input variable 1: Pressure deviation e: defined as target pressure The current pressure measured at time k The difference. Its mathematical expression is:

[0030] This variable directly reflects the degree and direction in which the current pressure deviates from the target.

[0031] Input variable 2: Pressure deviation change rate : Defined as the pressure deviation at the current moment Pressure deviation from the previous moment The difference. Its mathematical expression is:

[0032] This variable reflects the trend of pressure deviation and has predictive properties. For example, A positive value indicates that the deviation is increasing (the pressure is moving away from the target value). A negative value indicates that the deviation is decreasing (the pressure is approaching the target value).

[0033] Output variable: Valve opening adjustment Δu: Defined as the incremental change that should be applied to the current valve opening. The new valve opening command u(k) will be determined by the opening u(k-1) of the previous moment and the calculated adjustment Δu(k). u(k) = u(k-1) + Δu(k) Incremental output helps ensure smooth valve operation and avoids mechanical shock.

[0034] Fuzzification process: Design and implementation of membership function.

[0035] Fuzzification is the process of converting precise, quantized input values ​​(e and...) received by the controller. The process of converting ) into fuzzy linguistic variables.

[0036] Language variable set: This includes the input variable e. The universe of discourse (i.e. the range of values) of the output variable Δu is divided into five fuzzy subsets, which are represented by linguistic values: negative large (NB), negative small (NS), zero (Z), positive small (PS), and positive large (PB).

[0037] Membership function: Each fuzzy subset is defined by a membership function, which describes the degree to which any exact input value belongs to the fuzzy subset (the value ranges from 0 to 1). This embodiment uses a membership function combining triangular and trapezoidal forms. This choice has clear engineering significance: For the zero (Z) subset, a triangular membership function is typically used. This gives the controller the highest sensitivity near the setpoint (i.e., when the deviation is close to zero), enabling it to respond to minute deviations, which is key to achieving high-precision control.

[0038] For positive small (PS) and negative small (NS) subsets, narrower triangular or trapezoidal functions can be used to ensure a smooth transition from the zero state to the application of control.

[0039] For the positive (PB) and negative (NB) subsets, a wider trapezoidal membership function is typically used. The flat top of the trapezoid means that once the deviation exceeds a certain threshold, its membership reaches its maximum value of 1. This allows the controller to generate a continuous and strong control action when faced with large deviations, quickly pulling the system back to the vicinity of the setpoint, while avoiding the problem of the output increasing indefinitely due to excessive deviations, thus achieving a saturation effect.

[0040] The membership function formula for a trapezoidal curve is as follows:

[0041]

[0042] In the formula, x represents the pressure deviation or the rate of change of pressure deviation.

[0043] a: The starting point (left foot) of the trapezoid. When the input value x is less than or equal to a, it does not belong to this fuzzy set at all, and its membership degree is 0.

[0044] b: The left peak point (left shoulder point) of the trapezoid. When the input value x is between a and b, its membership degree increases linearly from 0 to 1.

[0045] c: The right peak point (right shoulder point) of the trapezoid. When the input value x is between b and c, it completely belongs to this fuzzy set with a membership degree of 1. This interval is the flat top of the trapezoid.

[0046] d: The right endpoint (right foot) of the trapezoid. When the input value x is between c and d, its membership degree decreases linearly from 1 to 0. When x is greater than d, the membership degree is also 0.

[0047] The core of fuzzy reasoning: the establishment and optimization of a fuzzy rule base.

[0048] The fuzzy rule base is the knowledge core of the fuzzy controller. It consists of a series of IF-THEN logical rules, reflecting how control experts determine the system state (e and t) based on the fuzzy controller's rules. The control strategy (Δu) is determined by the two input variables. For each variable, there are 5 fuzzy subsets, which can form a total of 5×5=25 rules, fully covering all possible operating conditions. A typical rule base is shown in Table 1.

[0049] Table 1: Fuzzy Control Rule Base

[0050] Analyzing this rule base reveals its sophisticated control strategies: Stable rule: The rule located at the center, if e is Z and If Z is the value of Δu, then Δu is Z, ensuring that the controller does not generate any output when the system has stabilized at the target point, thus avoiding unnecessary valve vibration near the target point.

[0051] Fast Response Rule: The rule located at the far end of the diagonal, if e is PB and e If PB is the value of Δu, then PB means that when the pressure is much lower than the target value (the deviation is positive) and continues to decrease (the rate of change of deviation is positive), the maximum positive adjustment (e.g., significantly closing the valve) should be applied to quickly reverse the trend.

[0052] Overshoot suppression rules: Off-diagonal rules, especially combinations with opposite signs, play a crucial role in damping and prediction. For example, the rule if e is PB and... If NB is the current pressure, then Δu is Z. Its physical meaning is: although the current pressure is far below the target value (e is PB), it is rapidly approaching the target value. If a large adjustment is applied at this point (where NB is the reference value), the pressure is very likely to exceed the target point, resulting in overshoot. Therefore, the control rule proactively sets the adjustment to zero, which is equivalent to braking in advance, thereby effectively suppressing overshoot and allowing the system to converge smoothly to the setpoint.

[0053] These 25 discrete rules, combined with membership functions and defuzzification methods, do not simply form a lookup table, but rather generate a smooth, continuous, and highly nonlinear three-dimensional control surface (indicated by e and (where Δu is the input axis and Δu is the output axis). It is this nonlinear control surface that enables it to precisely match the inherent nonlinear dynamic characteristics of a vacuum system. This contrasts sharply with traditional PID controllers, whose control law is essentially linear (its control surface is a plane), thus limiting their ability to handle nonlinear objects. This invention achieves a better fit to the behavior of complex systems by constructing a flexible, "shapeable" nonlinear control law, which is the fundamental reason why its performance surpasses traditional methods.

[0054] Defuzzification: Application of the weighted average centroid method.

[0055] The result of fuzzy inference is a fuzzy set for the output variable Δu. The task of defuzzification (or defuzzification) is to transform this fuzzy output result into a precise, executable numerical value. This invention uses the weighted average centroid method (COG) for defuzzification.

[0056] This method first calculates the intensity at which each rule is triggered, and then uses this intensity as a weight to perform a weighted average of the center values ​​of the output fuzzy sets corresponding to all rules.

[0057] The COG method was chosen because it comprehensively considers the contributions of all triggered rules, producing a smooth and continuous control output signal. This is crucial for controlling mechanical actuators, effectively preventing drastic, abrupt valve movements, thereby reducing mechanical wear and ensuring a stable and smooth pressure regulation process.

[0058] The implementation steps of the vacuum exhaust throttle valve setting method are as follows: Reference Figure 2 The logical flow and specific implementation steps of the method of the present invention are as follows: Step 1: System Initialization and Parameter Loading After the system is powered on, the fuzzy controller first performs a self-test and loads the pre-set fuzzy rule base and membership function parameters defining each fuzzy subset from non-volatile memory. Subsequently, the controller establishes communication with the HMI and sensor modules. The operator inputs the desired pressure target value through the HMI.

[0059] Step Two: Real-time Data Acquisition and Preprocessing. Within a fixed sampling period (e.g., every 10-100 milliseconds), the controller reads the output signal of the pressure sensor through an analog-to-digital converter (ADC) to obtain the actual pressure value, and reads the signal from the valve position sensor to obtain the current valve position. To improve the signal's anti-interference capability, digital filtering algorithms (such as moving average filtering) can be applied to the raw pressure signal to filter out high-frequency noise.

[0060] Step 3: Calculation of Control Deviation. Based on the collected real-time pressure values ​​and the set target values, the controller calculates the two precise input quantities required by the fuzzy logic engine: pressure deviation and deviation change rate.

[0061] Step 4: Execution of Fuzzy Control Decisions. This step is the core of the fuzzy control algorithm and includes three sub-processes: Fuzzification: The calculated target pressure and current pressure are converted into their membership degrees to each fuzzy subset (NB, NS, Z, PS, PB) according to a preset membership function.

[0062] Fuzzy reasoning: The controller iterates through all 25 rules in the rule base. For each rule, it iterates through the rules in its antecedent (IF part) and... The membership degree is used to calculate the trigger strength of the rule (usually using the MIN or PRODUCT operator).

[0063] Defuzzification: Aggregate the conclusions (THEN part) of all triggered rules and their corresponding trigger strengths, and apply the weighted average centroid method (COG) to calculate the final precise valve opening adjustment Δu(k).

[0064] Step 5: Valve Opening Update and Execution. The controller calculates the new target valve position: u(k) = u(k-1) + Δu(k). Before sending this command to the actuator of the electric actuator, a boundary check is performed to ensure that the value of u(k) is within the valve's physical stroke range (e.g., 0% to 100%). The actuator then precisely controls the motor to move the valve to the new target position.

[0065] Step Six: System Status Assessment and Iterative Controller Determines Whether the System Has Reached a Stable Operating Condition. A stable state can be defined as: for a given period of time, the absolute value of the pressure deviation remains consistently less than a preset minimum threshold (e.g., ±0.5% of the target value). If stability has not been achieved, the program returns to Step Two and begins the next control cycle. If stability has been achieved, the controller will continue monitoring and make fine adjustments based on minor disturbances.

[0066] Key technology extension: Automatic tuning and safety mechanisms To further enhance the intelligence and reliability of the system, this invention may also include advanced functions such as automatic tuning and fault protection.

[0067] Initial calibration and dynamic adjustment logic.

[0068] Initial Calibration Phase: An automatic calibration procedure can be performed after the system's initial installation or major maintenance. This procedure drives the valve from fully closed to fully open while recording the pressure response curve. By analyzing this curve, the system can automatically determine the pressure change range and rate, thereby automatically scaling the domain of discourse of the input and output variables, allowing the fuzzy controller's performance to be optimized for specific vacuum chamber volumes and pump speeds.

[0069] Dynamic Adjustment Phase: For more advanced implementations, a supervisory control layer can be added above the fuzzy controller. This layer is responsible for long-term monitoring of the system's control performance indicators (such as mean settling time, overshoot, etc.). If a performance degradation due to equipment aging or changes in process gases is detected, the supervisory layer can fine-tune the position or shape of the membership function, or adjust the weights of certain key rules, thereby achieving online adaptive control and keeping the system in its optimal operating state at all times.

[0070] Implementation of fault protection mechanism.

[0071] The system has built-in comprehensive fault diagnosis and protection logic to ensure the safety of equipment and processes under abnormal conditions.

[0072] Sensor fault detection: The controller continuously checks the reasonableness of sensor readings. If the pressure reading exceeds its physical possibility range (such as negative pressure), or if the reading remains unchanged for a long time when the system is dynamically changing, it is determined to be a sensor fault.

[0073] Actuator fault detection: After issuing the adjustment command Δu, the controller compares the feedback from the valve position sensor. If the valve position feedback does not change accordingly within a reasonable time, it is determined that the actuator or its drive is faulty.

[0074] Safety Mode: Once any serious fault is detected, the system will immediately enter the preset safety mode. This mode will immediately stop the fuzzy control algorithm, drive the throttle valve to a defined safe position (which may be fully open or fully closed depending on process requirements), and display a prominent alarm message on the HMI indicating the fault type and prompting the operator to intervene.

[0075] A significant advantage of this method is that it replaces the mathematical model of the physical system, which is difficult to establish precisely in traditional control methods, with a language model based on expert experience. The pressure response of a vacuum system is highly nonlinear, related to various factors such as valve opening, current pressure, and gas type. Attempting to describe this relationship with precise differential equations is extremely difficult. Fuzzy control, on the other hand, captures the macroscopic behavior of the system through linguistic rules such as larger valve adjustments if the pressure deviation is large. Therefore, the true innovation of this method lies in systematizing and algorithmizing this qualitative, experience-based knowledge, thereby constructing a high-performance nonlinear controller.

[0076] This method can precisely control pressure fluctuations within ±0.5% of the setpoint. This is thanks to the high sensitivity of the fuzzy controller in the "zero (Z)" region near the setpoint, which enables fine and smooth adjustments to minute disturbances, effectively avoiding the limit cycle oscillation problem that may occur in traditional PID controllers, and achieving higher steady-state accuracy.

[0077] Experimental data shows that compared to traditional PID control, the settling time of this method can be reduced by more than 30%. The fundamental reason lies in the nonlinear characteristics of fuzzy control. On the one hand, for large initial deviations, the "large deviation" rule in the rule base will drive the valve to perform large-amplitude, rapid actions, quickly pulling the system towards the target area; on the other hand, the deviation change rate is utilized... As input, the controller can predict the future trend of the system and brake in advance by suppressing overshoot rules, thereby avoiding overshoot and greatly shortening the system's settling time.

[0078] Because fuzzy control does not rely on a precise mathematical model of the controlled object, it is inherently robust to changes in system parameters (such as changes in vacuum chamber load or gas composition). Even if system characteristics drift to some extent, this control method can still maintain good control performance. Combined with dynamic adjustment logic, the system can more proactively adapt to environmental changes, truly achieving adaptive control without human intervention.

[0079] This method enhances system safety through a dual mechanism. First, the built-in fault diagnosis and protection module can promptly detect and handle hardware faults, preventing process accidents caused by equipment failure. Second, the stability of the fuzzy control algorithm itself and its effective suppression of overshoot reduce the impact of drastic pressure fluctuations on vacuum system components (such as pumps, valves, and chambers), extending equipment life and reducing the risk of product scrap due to pressure runaway.

[0080] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0081] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

[0085] It should be understood that the above description is for illustrative purposes and not for limitation. Many embodiments and applications beyond the provided examples will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined by reference to the above description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.

Claims

1. A method for setting a vacuum exhaust throttle valve, characterized in that, Includes the following processes: Obtain the current pressure value inside the vacuum pipeline; Based on the preset target pressure value and the current pressure value collected, calculate the pressure deviation and the rate of change of pressure deviation; The calculated pressure deviation and pressure deviation change rate are fuzzified to obtain the corresponding fuzzy linguistic variables. Fuzzy reasoning is performed based on a preset fuzzy rule base to obtain a fuzzy form of valve opening adjustment amount; The fuzzy valve opening adjustment amount is defuzzified to obtain a precise valve opening adjustment amount value; Adjust the opening of the exhaust throttle valve according to the precise valve opening adjustment value.

2. The method for adjusting the vacuum exhaust throttle valve according to claim 1, characterized in that, When performing fuzzification, the pressure deviation, the rate of change of pressure deviation, and the valve opening adjustment are all divided into five fuzzy subsets, namely negative large, negative small, zero, positive small, and positive large.

3. The method for adjusting the vacuum exhaust throttle valve according to claim 2, characterized in that, When performing fuzzification, a membership function is used to define each fuzzy subset; the membership function of the zero fuzzy subset is a triangular membership function, and the membership functions of the negative and positive fuzzy subsets are trapezoidal membership functions.

4. The method for adjusting the vacuum exhaust throttle valve according to claim 2, characterized in that, The fuzzy rule base includes the following rule: when the pressure deviation is positive and the rate of change of pressure deviation is negative, the valve opening adjustment is zero.

5. The method for adjusting the vacuum exhaust throttle valve according to claim 2, characterized in that, When performing defuzzification, the weighted average centroid method is used to calculate the precise valve opening adjustment value.

6. A vacuum exhaust throttle valve setting system, characterized in that, Includes the exhaust throttle valve body, sensor, and fuzzy controller; The sensor is connected to the vacuum pipe to collect real-time pressure values ​​inside the vacuum pipe; The fuzzy controller is electrically connected to the sensor. The fuzzy controller is configured to receive real-time pressure values, calculate pressure deviation and pressure deviation change rate based on preset target pressure values, fuzzify the pressure deviation and pressure deviation change rate, perform fuzzy inference based on the fuzzy rule base to obtain the valve opening adjustment amount, and then defuzzify the valve opening adjustment amount before outputting it. The exhaust throttle valve body is electrically connected to the fuzzy controller to receive the valve opening adjustment amount and perform the opening adjustment.

7. The vacuum exhaust throttle valve setting system according to claim 6, characterized in that, The sensors include a pressure sensor and a valve position sensor; the pressure sensor is mounted on the vacuum pipeline; the valve position sensor is integrated into the electric actuator of the exhaust throttle valve body.

8. The vacuum exhaust throttle valve setting system according to claim 6, characterized in that, The fuzzy controller is configured to divide the pressure deviation, the rate of change of pressure deviation, and the valve opening adjustment into five fuzzy subsets: negative large, negative small, zero, positive small, and positive large.

9. The vacuum exhaust throttle valve setting system according to claim 6, characterized in that, The fuzzy controller is configured with a membership function to define each fuzzy subset; the membership function of the zero fuzzy subset is a triangular membership function, and the membership functions of the negative and positive fuzzy subsets are trapezoidal membership functions.

10. The vacuum exhaust throttle valve setting system according to claim 6, characterized in that, The fuzzy rule base configured by the fuzzy controller includes the following rule: when the pressure deviation is positive and the rate of change of the pressure deviation is negative, the valve opening adjustment is zero.

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