Control method of intelligent valve positioner based on fuzzy PID and valve positioner system

Abstract

The application discloses a control method of an intelligent valve positioner based on a fuzzy PID and a valve positioner system, and the control method comprises the following steps: step S1: calculating an error and an error change rate; step S2: according to the relationship between the absolute value of the error and a preset first threshold T1 and the relationship between the absolute value of the error change rate and a preset second threshold T2, one of the following three control strategies is selected to output an actual control amount: strategy A: selecting an actual control amount output by a fuzzy PID controller; strategy B: selecting an actual control amount output by an inner-layer PI controller; and strategy C: selecting an actual control amount output by a Bang-bang controller. When the error is large, the fuzzy PID controller is used for rapid approach; when the error is small, the inner-layer PI controller is switched to for fine adjustment; and when the system tends to be stable, the Bang-bang controller is used for locking output, so that small oscillation is avoided.

Description

Technical Field

[0001] This invention relates to the field of valve positioner control technology, and in particular to a control method and valve positioner system for an intelligent valve positioner based on fuzzy PID. Background Technology

[0002] In process industries, control valves are key actuators for precisely controlling process variables such as flow and pressure. As the core component connecting the upstream control system and the valve actuator, the performance of the valve positioner directly determines the positioning accuracy and response speed of the control valve. Modern intelligent valve positioners generally use microprocessors as their control core.

[0003] The valve positioner is responsible for converting the setpoint from the upper control system into actuation of the valve actuator, forming a closed-loop valve position control. Valve positioners have evolved through three stages: purely pneumatic, electronic electro-pneumatic positioning, and intelligent positioners integrating microprocessors and fieldbus. The valve actuator is the I / P conversion unit. Control valves utilize both nozzle-baffle type I / P conversion units and piezoelectric I / P mechanisms with piezoelectric ceramic actuators. Piezoelectric I / P mechanisms, due to their compact structure, fast response, low power consumption, and good vibration resistance, have gradually become the mainstream choice for intelligent positioners, especially in the "intake / holding / exhaust" three-state control and gas consumption management, where they possess inherent advantages.

[0004] As the end-effector in the process industry, the positioning accuracy and response quality of control valves directly affect the stability and efficiency of process control. Third-generation intelligent valve positioners introduce microprocessors and digital communication. Existing intelligent valve positioners generally employ PID (Proportional-Integral-Derivative) control algorithms. However, in practical applications, due to nonlinear factors such as inertia, friction, and gas compressibility in valve actuators, as well as the inherent nonlinearity and hysteresis of piezoelectric I / P conversion units, simply using a fixed-parameter PID control strategy faces numerous challenges. For example, setting large control parameters to pursue fast response can easily lead to severe overshoot when the system approaches the target position; conversely, using conservative parameters to avoid overshoot slows down the system response speed and prolongs the time to reach steady state. Therefore, existing intelligent valve positioners using single PID control struggle to achieve an ideal balance between control accuracy and response speed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a control method and valve positioner system for an intelligent valve positioner based on fuzzy PID that can improve control accuracy and response speed.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: Firstly, a control method for an intelligent valve positioner based on fuzzy PID, comprising the following steps:

[0007] Step S1: Obtain the valve position feedback signal and input setting signal from the valve positioner, and calculate the error and error change rate;

[0008] ;

[0009] ;

[0010] in, Let be the error in valve position control at time k; Input setting signal; This is the valve position feedback signal at time k; The error at time k-1; Let be the rate of change of error at time k; To control the sampling period;

[0011] Step S2: Based on the error The relationship between the absolute value and the preset first threshold T1, and the error change rate. The relationship between the absolute value of the value and the preset second threshold T2 is used to select one of the following three control strategies to execute in order to output the actual control quantity:

[0012] Strategy A: When When the value exceeds the first threshold T1, the actual control quantity is output by the fuzzy PID controller, which includes a fuzzy logic controller and a PID controller, with an error... and error change rate As the input to the fuzzy PID controller, the proportional coefficient correction is obtained through fuzzy inference by the fuzzy logic controller. Integral coefficient correction amount Differential coefficient correction amount ;

[0013] The PID controller uses incremental PID control. The control law of the PID controller is: the output increment in the k-th control cycle. :

[0014] ;

[0015] ;

[0016] in, , , These are the error values ​​for the current period, the previous period, and the two periods prior, respectively.

[0017] , , These are the proportional, integral, and differential coefficients currently in use;

[0018] , , These are the initial proportional, initial integral, and initial derivative coefficients of the PID controller;

[0019] Direction coefficient;

[0020] The fuzzy PID controller outputs the actual control quantity:

[0021] ;

[0022] Strategy B: When Less than or equal to the first threshold T1 and When the value is greater than the second threshold T2, the actual control quantity is selected to be output by the inner PI controller. The control law of the inner PI controller is:

[0023] The output increment of the kth control cycle :

[0024] ;

[0025] in, This is the basic proportional coefficient of the inner PI controller; The basic integral coefficient of the inner PI controller; The load pressure of the valve actuator; For load adaptive coefficients;

[0026] The inner PI controller outputs the actual control quantity:

[0027] ;

[0028] Strategy C: When Less than or equal to the first threshold T1 and When the value is less than or equal to the second threshold T2, the Bang-bang controller is selected to output the actual control quantity. The control method of the Bang-bang controller is to output the actual control quantity of the previous moment as the current actual control quantity.

[0029] Preferably, the fuzzy logic controller uses the valve position control error. and its discrete error variation As input, the input variable is subjected to amplitude limiting and normalization mapping to obtain the error normalization variable. Normalized variables of sum and error change :

[0030] ;

[0031] ;

[0032] in, This is a limiting function; This is the positive boundary of the fundamental universe of discourse of error; It represents the positive boundary of the fundamental universe of discourse for the variation of discrete error.

[0033] Preferably, the fuzzy logic controller uses the minimum value method to determine the activation degree of the rule antecedent, the minimum value method to perform implication operation, the maximum value method to perform aggregation, and the centroid method to perform defuzzification.

[0034] Preferably, in the fuzzy logic controller, the fuzzy control rule base consists of Tables 1 to 3, where Table 1 represents the normalized correction amount of the proportional coefficient. The fuzzy control rules are shown in Table 2, which contains the normalization correction values ​​for the integral coefficients. The fuzzy control rules are shown in Table 3, which contains the normalization correction values ​​for the differential coefficients. Fuzzy control rules;

[0035] Table 1

[0036]

[0037] Table 2

[0038]

[0039] Table 3

[0040]

[0041] In Tables 1 to 3, rows represent the fuzzy linguistic values ​​of the error normalization variable, columns represent the fuzzy linguistic values ​​of the error change normalization variable, and elements within the tables represent the fuzzy linguistic values ​​of the corresponding output variables.

[0042] The three normalized correction values ​​obtained after defuzzification are mapped to the actual correction range of the PID parameters:

[0043] ;

[0044] in, , and These represent the maximum allowable correction ranges for the proportional coefficient, integral coefficient, and derivative coefficient, respectively.

[0045] Preferably, when the input setting signal for the valve position increases... When the input setting signal for the valve position decreases (set to 1), the valve position setting signal is reduced. Take -1.

[0046] Preferably, the load pressure The data is collected in real time by a pressure sensor installed in the gas chamber or the membrane chamber of the actuator.

[0047] Preferably, when the Bang-bang controller is enabled, it locks the actual control quantity from the previous moment. And continue to output this value until Again greater than the second threshold T2 or If the value exceeds the first threshold T1, the Bang-bang controller will exit.

[0048] In a second aspect, a valve positioner system includes a valve position detection element, a main control MCU, a control strategy selection module, a fuzzy PID controller, an inner PI controller, and a Bang-bang controller.

[0049] The valve position detection element is used to collect the valve position feedback signal from the valve positioner.

[0050] The main control MCU receives the valve position feedback signal collected by the valve position detection element, and calculates the error and error change rate by comparing it with the input setting signal;

[0051] The control strategy selection module is used to select whether the actual control quantity is output by the fuzzy PID controller, the inner PI controller, or the Bang-bang controller, based on the relationship between the absolute value of the error and the first threshold T1, the absolute value of the error change rate and the second threshold T2.

[0052] The fuzzy PID controller includes a fuzzy logic controller and an incremental PID control module, which is used to output the actual control quantity when the absolute value of the error is greater than the first threshold T1.

[0053] The inner PI controller is used to output the actual control quantity when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is greater than the second threshold T2.

[0054] The Bang-bang controller is used to lock and continuously output the actual control quantity of the previous moment when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is less than or equal to the second threshold T2.

[0055] Preferably, in the valve positioner system, the control law of the inner PI controller is:

[0056] The output increment of the kth control cycle :

[0057] ;

[0058] This is the basic proportional coefficient of the inner PI controller; The basic integral coefficient of the inner PI controller; The load pressure of the valve actuator; For load adaptive coefficients;

[0059] The inner PI controller outputs the actual control quantity:

[0060] ;

[0061] Preferably, the actual control quantity is transmitted to the main control MCU for PWM duty cycle mapping, and the main control MCU converts the actual control quantity into a PWM output signal with a duty cycle of 0%-100%; the PWM output signal output by the main control MCU is amplified by the drive circuit and then drives the I / P conversion unit to perform intake, exhaust or pressure holding actions.

[0062] The beneficial effects of this invention are:

[0063] 1. Improved control accuracy and response speed: This invention achieves segmented composite control by setting different control zones. When the error is large, a fuzzy PID controller is used for rapid approach, fully utilizing its fast response advantage; when the error is small, the inner PI controller is switched for fine adjustment; when the system tends to stabilize, a Bang-bang controller is used to lock the output, avoiding the small oscillations near the setpoint of traditional PID control. Compared with single PID control, this combined strategy can effectively reduce system overshoot and shorten the settling time.

[0064] 2. Enhanced system robustness and adaptability: The fuzzy PID controller can adjust PID parameters online based on real-time errors and error change rates, enabling the control system to better adapt to changes in operating conditions and external disturbances. Simultaneously, the inner PI controller introduces adaptive coefficients based on real-time load pressure, which can compensate for actuator load changes, further improving the stability and robustness of the control system under different loads.

[0065] 3. Eliminates steady-state jitter and reduces energy consumption: When the system enters steady state (with very small error and error rate of change), the Bang-bang controller is activated. By maintaining the control output of the previous moment, the frequent calculation and output adjustment of the PID controller caused by sensor noise or small disturbances are avoided, thereby effectively suppressing the steady-state jitter of the valve, reducing unnecessary actions of the actuator and air source consumption, and reducing the overall energy consumption of the system. Attached Figure Description

[0066] Figure 1This is a flowchart of the control method of the intelligent valve positioner based on fuzzy PID according to the present invention;

[0067] Figure 2 This is a block diagram illustrating the principle of the control method for the intelligent valve positioner based on fuzzy PID of the present invention.

[0068] Figure 3 This is a schematic diagram of the valve positioner system of the present invention;

[0069] Figure 4 These are membership function graphs of the input variables of the fuzzy logic controller, where (a) is the membership function graph of the error normalized variable and (b) is the membership function graph of the error change normalized variable.

[0070] Figure 5 It is a membership function graph of the output variables of the fuzzy logic controller, where (a) is the membership function graph of the proportional coefficient correction and the differential coefficient correction, and (b) is the membership function graph of the integral coefficient correction. Detailed Implementation

[0071] The present invention will now be described in further detail with reference to the accompanying drawings and preferred embodiments. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0072] Before providing a further detailed description of the embodiments of the present invention, some of the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0073] (1) Fuzzy PID controller: This refers to a controller that includes a fuzzy logic controller and a PID controller. The fuzzy logic controller corrects the proportional, integral, and derivative coefficients of the PID controller online based on the input error and the rate of change of the error. This controller combines the accuracy of PID control with the flexibility and robustness of fuzzy control.

[0074] (2) Bang-bang controller: A controller that maintains system stability and avoids fluctuations in control quantity caused by minor disturbances by locking and continuously outputting the control quantity from the previous moment. It is essentially a switching controller designed to achieve low power consumption and high stability in steady state.

[0075] (3) Load pressure (T) L ): This refers to the load encountered by the valve actuator during operation, such as the pressure in the air chamber or the diaphragm chamber of the actuator. This pressure affects the dynamic response characteristics of the actuator. In this invention, this parameter can be acquired in real time by a pressure sensor for adaptive adjustment of the control algorithm.

[0076] This invention provides a control method for an intelligent valve positioner based on fuzzy PID control, aiming to solve the problem that existing single PID controllers struggle to balance response speed, positioning accuracy, and stability when facing complex operating conditions such as nonlinearity, time delay, and variable load in valve actuators. This invention achieves efficient, accurate, and robust valve position control by designing a composite control strategy that switches between control strategies in different operating ranges.

[0077] The following will combine Figure 1 , Figure 2 The control method of a basic embodiment of the present invention will be described in detail.

[0078] First, in step S1, signal acquisition and error calculation are performed. This occurs within one control sampling period. Initially, the main control MCU receives two key signals: one is an externally given input setting signal. (For example, a 4-20mA signal from the host DCS system represents the target valve position), and another is the current valve position feedback signal fed back via the valve position detection element. Subsequently, the main control MCU performs internal calculations to obtain the valve position control error at the current k-th moment. and error change rate The calculation formula is as follows:

[0079] ;

[0080] ;

[0081] in, Let be the error in valve position control at time k; Input setting signal; This is the valve position feedback signal at time k; The error at time k-1; Let be the rate of change of error at time k; To control the sampling period.

[0082] Next, we proceed to step S2, which involves selecting the control strategy. For example... Figure 1 As shown, this is a decision-making stage. Based on the calculations from the previous step... and The absolute value is compared with the preset first threshold T1 and second threshold T2, and one of the three predefined control strategies is selected to be executed.

[0083] When the judgment result is an error If the absolute value of the error exceeds the first threshold T1, the system selects strategy A. This typically occurs at the beginning of a control task or when subjected to significant disturbances, resulting in a large deviation between the valve's current position and the target position. In this case, the control objective is a rapid response. The system activates a fuzzy PID controller, which comprises a fuzzy logic controller and a PID controller. and error change rate The error is simultaneously fed into the fuzzy logic controller. and error change rate As the input to the fuzzy PID controller, the proportional coefficient correction is obtained through fuzzy inference by the fuzzy logic controller. Integral coefficient correction amount Differential coefficient correction amount ;

[0084] The PID controller uses incremental PID control. The control law of the PID controller is: the output increment in the k-th control cycle. :

[0085] ;

[0086] ;

[0087] in, , , These are the error values ​​for the current period, the previous period, and the two periods prior, respectively.

[0088] , , These are the proportional, integral, and differential coefficients currently in use;

[0089] , , These are the initial proportional, initial integral, and initial derivative coefficients of the PID controller;

[0090] For direction coefficient; in a preferred embodiment, when the valve position setpoint increases When the valve position setpoint is reduced, select 1. Set the value to -1. Therefore, the fuzzy logic controller can adjust the PID parameters online based on the valve position error, the error change trend, and the direction of action.

[0091] The fuzzy PID controller outputs the actual control quantity:

[0092] ;

[0093] When the judgment result is Less than or equal to the first threshold T1 and If the value exceeds the second threshold T2, the system selects strategy B. This situation indicates that the valve is close to the target position but is still moving at a relatively high speed, potentially posing an overshoot risk. To perform fine-tuning and suppress oscillations, the system switches to the inner PI controller. This controller also employs an incremental algorithm. Its output increment in the k-th control cycle... :

[0094] ;

[0095] in, This is the basic proportional coefficient of the inner PI controller; The basic integral coefficient of the inner PI controller; The load pressure of the valve actuator; This is the load adaptive coefficient.

[0096] It is worth noting that the integral term here includes a load adaptive coefficient. This allows the integral action to be adjusted according to real-time load pressure, enhancing robustness.

[0097] The inner PI controller outputs the actual control quantity:

[0098] ;

[0099] Finally, when the judgment result is when Less than or equal to the first threshold T1 and When the value is less than or equal to the second threshold T2, the system selects execution strategy C. This indicates that the valve has reached the target position and is basically stable. To avoid unnecessary adjustment actions caused by minor noise or calculation fluctuations, the system activates the Bang-bang controller. It outputs the actual control quantity from the previous moment as the current actual control quantity, keeping the actuator in the current state, thereby achieving high stability and low energy consumption in steady state.

[0100] By switching between the three strategies described above, the control method in this embodiment can adopt corresponding control measures according to different stages of valve operation, thereby achieving a fast, accurate, and stable control effect as a whole and overcoming the limitations of a single control strategy.

[0101] The core of the control method provided in this invention lies in dynamically selecting a control strategy based on the current state of the system (mainly characterized by error and error change rate).

[0102] In obtaining error and error change rate Subsequently, the system does not employ a single control algorithm but enters a decision-making phase. This phase will determine the error... The absolute value is compared with a preset first threshold T1, while the error rate of change is also considered. The absolute value is compared with a preset second threshold T2. This judgment mechanism based on dual thresholds is designed to divide the entire adjustment process of the valve into different dynamic regions, such as the "large error rapid approach region", the "small error oscillation adjustment region" and the "steady-state holding region", so as to match a control strategy for each region.

[0103] When the error When the error value exceeds the first threshold T1, indicating a large absolute error, it means the valve's current position is far from the target position. In this case, the primary control objective is to approach the target quickly and without overshoot. This invention employs a fuzzy PID control strategy. This strategy will control the error... and error change rate As input to the fuzzy logic controller, the three key parameters of the PID controller—proportional, integral, and derivative coefficients—are adjusted online in real time using preset fuzzy rules. In this way, the system can apply a larger control action to accelerate the response when the error is large, and reduce the control action to suppress overshoot when approaching the target, thus solving the contradiction between "fast" and "stable" in traditional fixed-parameter PID controllers.

[0104] when Less than or equal to the first threshold T1 and When the error exceeds the second threshold T2, meaning the absolute value of the error is relatively small but the absolute value of the error rate of change is still large, it usually indicates that the system is near the target position but may experience oscillations or rapid disturbances. Continuing to use the fuzzy PID control strategy at this point may exacerbate instability due to its complex calculations and large adjustment actions. Therefore, the system switches to an inner-layer PI control strategy. This strategy has a simpler structure, faster response, and focuses on quickly eliminating residual errors and suppressing oscillations within a small range. By employing a PI controller, the system can be effectively stabilized, preparing it for eventual steady-state operation.

[0105] when Less than or equal to the first threshold T1 and When the value is less than or equal to the second threshold T2, meaning the absolute value of the error and the absolute value of the error rate of change are both less than their respective thresholds, it indicates that the valve has reached and stabilized near the target position. At this point, even minor adjustments to the control input can disrupt this hard-won stability, leading to unnecessary "micro-vibrations" or "jitter," which not only consumes energy but also accelerates the wear of the actuator. To address this issue, a bang-bang control strategy is employed. This strategy is simple and effective: it directly locks in and continuously outputs the actual control input from the previous moment, essentially entering a "hold" state, thus achieving steady-state performance and low energy consumption.

[0106] Furthermore, in a preferred embodiment, the fuzzy logic controller uses the valve position error... and its discrete error variation As input, to make the fuzzy control rules applicable to valve positioners with different ranges, the input variables are first subjected to amplitude limiting and normalization mapping to obtain the error normalization variable. Normalized variables of sum and error change :

[0107] ;

[0108] ;

[0109] in, This is a limiting function; This is the positive boundary of the fundamental universe of discourse of error; This represents the positive boundary of the fundamental universe of discourse for the variation of discrete error. In an implementation corresponding to control codes, Take 600, Taking 6, the fundamental universe of discourse for the error is [-600, 600], and the fundamental universe of discourse for the discrete error change is [-6, 6]. Both are mapped to the fuzzy universe of discourse [-3, 3].

[0110] like Figure 4 and Figure 5 As shown, both input variables E and EC use seven fuzzy linguistic values: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. NB and PB at the two ends of the input variables use Gaussian membership functions, while the five middle fuzzy sets use triangular membership functions. For input variable E, the triangular parameters of NM are [-3, -2, 0]; for input variable EC, the triangular parameters of NM are [-3, -2, 0]; and the triangular parameters of the remaining middle fuzzy sets are [-3, -1, 1], [-2, 0, 2], [-1, 1, 3], and [0, 2, 3], respectively. The centers of the Gaussian membership functions for NB and PB are -3 and 3, respectively, with a corresponding standard deviation of 0.8.

[0111] Output variables include the normalization correction for the scaling factor. Integral coefficient normalization correction and the normalization correction of differential coefficients .in, and The universe of discourse is [-0.2, 0.2], and the centers of the seven Gaussian membership functions are -0.2, -0.1333, -0.06668, 0, 0.06668, 0.1333 and 0.2, respectively. The domain of discourse is [-0.1, 0.1], and the centers of the seven Gaussian membership functions are -0.1, -0.06666, -0.03334, 0, 0.03334, 0.06666 and 0.1, respectively. and The standard deviation of the Gaussian membership function is approximately 0.023. The standard deviation of the Gaussian membership function is approximately 0.0142.

[0112] The fuzzy logic controller employs the Mamdani-type fuzzy inference method. Let the (r)th fuzzy control rule be: if the normalized error... Belongs to fuzzy set And the change in normalization error Belongs to fuzzy set Then the normalized output variable z belongs to the fuzzy set. .in, The output variable z corresponds to the normalization correction amount of the proportional coefficient. Integral coefficient normalization correction and the normalization correction of differential coefficients .

[0113] For each fuzzy control rule, the intersection of the membership degrees of the two input variables is calculated using the minimum value method to obtain the activation degree of the rule's antecedent;

[0114] ;

[0115] in, Let r be the activation degree of the antecedent of the r-th fuzzy control rule; For normalization error For fuzzy sets Membership degree; The change in normalized error For fuzzy sets The degree of membership.

[0116] Based on the antecedent activation degree, the consequent membership function corresponding to the r-th rule is truncated to obtain the output fuzzy set of that rule. The truncation relationship is as follows:

[0117] ;

[0118] in, For the fuzzy set of the consequent in the r-th rule Membership function; This is the output membership function obtained after truncating the antecedent activation.

[0119] Subsequently, the output results of the 49 fuzzy control rules were aggregated using the maximum value method to obtain the aggregated output fuzzy set:

[0120] ;

[0121] In the formula, This represents the membership degree of the aggregated output fuzzy set at the output variable z. Let be the output membership degree of the r-th fuzzy control rule after truncation.

[0122] Finally, the centroid method is used to obtain the refined output. In one implementation corresponding to the control code, each output variable is assigned 30 equally spaced sampling points within its universe of discourse, and the discrete centroid method is used to calculate the output result.

[0123] ;

[0124] In the formula, This represents the j-th discrete sampling point within the domain of the output variable; The membership degree of the aggregated output fuzzy set at the j-th discrete sampling point. ; represents the determined output value obtained after defuzzification. This determined output value is used as the normalization correction amount for the scaling factor. Integral coefficient normalization correction and the normalization correction of differential coefficients This is further mapped to the actual correction values ​​of the proportional coefficient, integral coefficient, and derivative coefficient of the PID controller.

[0125] The fuzzy control rule base consists of 49 rules. In Tables 1 to 3, the rows represent the fuzzy linguistic values ​​of the error normalization variable E, the columns represent the fuzzy linguistic values ​​of the error change normalization variable EC, and the elements in the tables represent the fuzzy linguistic values ​​of the corresponding output variables.

[0126] Table 1. Normalization Correction for Proportional Coefficient Fuzzy control rules

[0127]

[0128] Note: NB, NM, NS, ZO, PS, PM and PB represent negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively. In Table 1, the row variable is E and the column variable is EC.

[0129] Table 2. Normalization Correction for Integral Coefficients Fuzzy control rules

[0130]

[0131] Note: In Table 2, the row variable is E and the column variable is EC.

[0132] Table 3. Normalization Correction for Differential Coefficients Fuzzy control rules

[0133]

[0134] Note: In Table 3, the row variable is E and the column variable is EC.

[0135] The three normalized correction values ​​obtained after defuzzification still need to be mapped to the actual correction range of the PID parameters:

[0136] ;

[0137] in, , and These represent the maximum allowable correction ranges for the proportional, integral, and derivative coefficients, respectively. In one implementation corresponding to the control code, the three maximum correction ranges are each taken as 10% of the initial proportional, integral, and derivative coefficients, respectively. Preferably, the direction of adjustment is determined based on the direction of change in the valve position setpoint: when the setpoint increases, the correction is added to the initial coefficients; when the setpoint decreases, the correction is subtracted from the initial coefficients, and the corrected control coefficients are then subject to non-negative limits.

[0138] In one optional implementation, a first threshold T1 and a second threshold T2 define the boundaries of different control regions. The first threshold T1 should typically be significantly larger than the second threshold T2 to ensure a clear, stable region in the control logic that transitions from PI control to Bang-Bang control, avoiding frequent switching between the two modes at the boundary. In a preferred implementation, the first threshold T1 > the second threshold T2 * N, where N is greater than 3. The first threshold T1 and the second threshold T2 are preset.

[0139] Furthermore, to enable the inner PI controller to adapt to different load conditions, this embodiment of the invention also provides a load adaptive mechanism. By installing a pressure sensor in the gas chamber or diaphragm chamber of the valve actuator, the load pressure can be collected and monitored in real time. The pressure signal is converted by the ADC module and then input to the main control MCU. When calculating the output of the inner PI controller, this load pressure is... This variable is introduced into the calculation of the integral term. When the load pressure increases, the integral action is appropriately reduced to prevent slow response and integral saturation caused by the increased load; conversely, when the load decreases, the integral action can be enhanced to eliminate steady-state error more quickly. In this way, the PI controller can "sense" changes in external operating conditions and make corresponding adjustments, significantly improving the robustness and adaptability of the system.

[0140] In another preferred embodiment, the behavior of the Bang-bang controller is defined. When the system enters Bang-bang control mode, the controller does not simply remain inactive, but actively locks the last valid actual control quantity before the switch. And in the following control cycle, this locked value will be output as the current control variable.

[0141] When the absolute value of the rate of change of error If it exceeds the second threshold T2 again (indicating that the system is beginning to exhibit new dynamic changes), or the absolute value of the error... The value exceeds the first threshold T1 (indicating that the system has been subjected to a significant disturbance and deviated from the target point). Once any exit condition is met, the Bang-bang controller will immediately exit, returning control to the inner PI controller or fuzzy PID controller. This locking mechanism for entry and exit ensures that unnecessary actuator movements caused by sensor noise or minor process fluctuations can be effectively suppressed in steady state, thereby achieving energy saving and extending equipment life.

[0142] To translate the calculated numerical control quantity into a physical operation of the valve, this invention provides a specific output implementation method. Actual control quantity This is a digital value. The PWM (Pulse Width Modulation) module inside the main control MCU linearly maps this digital value into a PWM output signal with a duty cycle between 0% and 100%. The PWM output signal is amplified by the drive circuit. The amplified electrical signal is used to drive an I / P (current-to-pressure) conversion unit. This I / P conversion unit controls the valve actuator to perform intake, exhaust, or pressure holding actions based on the strength of the input signal.

[0143] like Figure 3 As shown, this embodiment of the invention also provides a valve positioner system, including a valve position detection element, a main control MCU, a control strategy selection module, a fuzzy PID controller, an inner PI controller, and a Bang-bang controller;

[0144] The valve position detection element is used to collect the valve position feedback signal from the valve positioner.

[0145] The main control MCU receives the valve position feedback signal collected by the valve position detection element, and calculates the error and error change rate by comparing it with the input setting signal;

[0146] The control strategy selection module is used to select whether the actual control quantity is output by the fuzzy PID controller, the inner PI controller, or the Bang-bang controller, based on the relationship between the absolute value of the error and the first threshold T1, the absolute value of the error change rate and the second threshold T2.

[0147] The fuzzy PID controller includes a fuzzy logic controller and an incremental PID control module, which is used to output the actual control quantity when the absolute value of the error is greater than the first threshold T1.

[0148] The inner PI controller is used to output the actual control quantity when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is greater than the second threshold T2.

[0149] The Bang-bang controller is used to lock and continuously output the actual control quantity of the previous moment when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is less than or equal to the second threshold T2.

[0150] The following is an example of a workflow: Suppose the valve is currently in the fully closed position (0%), and the host system sends a fully open command (100%).

[0151] Workflow begins: The main control MCU receives an input setting signal representing 100% opening. At the same time, the current valve position is read through the valve position detection element (Hall sensor). The initial error is 0%. The main control MCU immediately calculates the initial error. =100%.

[0152] because If the error is significantly greater than the preset first threshold T1 (e.g., T1 is set to 20%), the system immediately enters strategy A and activates the fuzzy PID controller. The fuzzy logic controller, based on the current error and its rate of change, consults the fuzzy rule table and outputs a set of PID parameter corrections. This allows the incremental PID controller to calculate a positive output increment. ,thereby Increase. The main control MCU will increase this. The value is mapped to a high duty cycle PWM signal, which drives the I / P conversion unit to quickly inflate the actuator, and the valve begins to open rapidly.

[0153] As the valve moves rapidly toward the 100% position, the error... Continuously decreasing. When When the error decreases below the first threshold T1, for example, when the valve is opened to 80%, but due to inertia the valve is still moving rapidly, i.e., the error change rate... The absolute value is still greater than the second threshold T2 (e.g., T2 is set to 6%). At this point, the system switches to strategy B and enables the inner PI controller. This controller utilizes the real-time collected load pressure. It performs adaptive load adjustment to suppress overshoot.

[0154] With fine-tuning of the PI controller, the valve stabilizes near the 100% target position, at which point the error... Reduced to below the first threshold T1 and the rate of change of error When the absolute value of the signal decreases below the second threshold T2, the system determines that it has entered a steady state and automatically switches to strategy C, namely Bang-bang control. This causes the I / P conversion unit to enter a pressure-holding state, and the actuator is locked in the target position, no longer reacting to the small noise of the sensor, thus achieving lower energy consumption.

[0155] Through the synergistic effect of the aforementioned technical features, this embodiment achieves significant comprehensive technical effects. First, the application of the fuzzy PID controller enables the system to achieve a fast response speed over a wide range of adjustments, significantly shortening the settling time for step responses compared to traditional PID control. Second, the inner PI controller with load adaptation allows the system to maintain high positioning accuracy even when facing changes in medium pressure and ambient temperature. Finally, the Bang-bang control strategy effectively suppresses minor oscillations in steady state, resulting in a significant reduction in gas and electricity consumption during steady-state operation. Therefore, the synergistic effect of these technical features significantly improves the overall performance of the invention, achieving the technical goals of accuracy, stability, and energy saving.

[0156] The intelligent valve positioner control method and system based on fuzzy PID proposed in this invention can be widely applied in various industrial automation process control fields. For example, in the petrochemical industry, it can be used to control the raw material feed valve of a reactor to ensure the stability of the chemical reaction and the quality of the product. In the power industry, it can be used to control the steam regulating valve of a steam turbine to achieve rapid response to the load of the generator set.

[0157] The above description is merely a specific embodiment of the present invention. Various examples and illustrations do not constitute a limitation on the substantive content of the present invention. Those skilled in the art can modify or transform the specific embodiments described above after reading the specification without departing from the essence and scope of the invention.

Claims

1. A control method for an intelligent valve positioner based on fuzzy PID, characterized in that: Includes the following steps: Step S1: Obtain the valve position feedback signal and input setting signal from the valve positioner, and calculate the error and error change rate; ； ； in, Let be the error in valve position control at time k; Input setting signal; This is the valve position feedback signal at time k; The error at time k-1; Let be the rate of change of error at time k; To control the sampling period; Step S2: Based on the error The relationship between the absolute value and the preset first threshold T1, and the error change rate. The relationship between the absolute value of the value and the preset second threshold T2 is used to select one of the following three control strategies to execute in order to output the actual control quantity: Strategy A: When When the value exceeds the first threshold T1, the actual control quantity is output by the fuzzy PID controller, which includes a fuzzy logic controller and a PID controller, with an error... and error change rate As the input to the fuzzy PID controller, the proportional coefficient correction is obtained through fuzzy inference by the fuzzy logic controller. Integral coefficient correction amount Differential coefficient correction amount ; The PID controller uses incremental PID control. The control law of the PID controller is: the output increment in the k-th control cycle. : ； ； in, , , These are the error values ​​for the current period, the previous period, and the two periods prior, respectively. , , These are the proportional, integral, and differential coefficients currently in use; , , These are the initial proportional, initial integral, and initial derivative coefficients of the PID controller; Direction coefficient; The fuzzy PID controller outputs the actual control quantity: ； Strategy B: When Less than or equal to the first threshold T1 and When the value is greater than the second threshold T2, the actual control quantity is selected to be output by the inner PI controller. The control law of the inner PI controller is: The output increment of the kth control cycle : ； in, This is the basic proportional coefficient of the inner PI controller; The basic integral coefficient of the inner PI controller; The load pressure of the valve actuator; For load adaptive coefficients; The inner PI controller outputs the actual control quantity: ； Strategy C: When Less than or equal to the first threshold T1 and When the value is less than or equal to the second threshold T2, the Bang-bang controller is selected to output the actual control quantity. The control method of the Bang-bang controller is to output the actual control quantity of the previous moment as the current actual control quantity.

2. The control method for an intelligent valve positioner based on fuzzy PID according to claim 1, characterized in that: The fuzzy logic controller uses valve position control for error. and its discrete error variation As input, the input variable is subjected to amplitude limiting and normalization mapping to obtain the error normalization variable. Normalized variables of sum and error change : ； ； in, This is a limiting function; This is the positive boundary of the fundamental universe of discourse of error; It represents the positive boundary of the fundamental universe of discourse for the variation of discrete error.

3. The control method for an intelligent valve positioner based on fuzzy PID according to claim 2, characterized in that: The fuzzy logic controller uses the minimum value method to determine the activation degree of the rule antecedent, the minimum value method to perform implication operation, the maximum value method to perform aggregation, and the centroid method to perform defuzzification.

4. The control method for an intelligent valve positioner based on fuzzy PID according to claim 1, characterized in that: When the input setting signal for the valve position increases When the input setting signal for the valve position decreases (set to 1), the valve position setting signal is reduced. Take -1.

5. The control method for an intelligent valve positioner based on fuzzy PID according to claim 1, characterized in that: The load pressure The data is collected in real time by a pressure sensor installed in the gas chamber or the membrane chamber of the actuator.

6. The control method for an intelligent valve positioner based on fuzzy PID according to claim 1, characterized in that: When the Bang-bang controller is activated, it locks the actual control quantity from the previous moment. And continue to output this value until Again greater than the second threshold T2 or If the value exceeds the first threshold T1, the Bang-bang controller will exit.

7. A valve positioner system, characterized in that: Includes valve position detection element, main control MCU, control strategy selection module, fuzzy PID controller, inner layer PI controller and Bang-bang controller; The valve position detection element is used to collect the valve position feedback signal from the valve positioner. The main control MCU receives the valve position feedback signal collected by the valve position detection element, and calculates the error and error change rate by comparing it with the input setting signal; The control strategy selection module is used to select whether the actual control quantity is output by the fuzzy PID controller, the inner PI controller, or the Bang-bang controller, based on the relationship between the absolute value of the error and the first threshold T1, the absolute value of the error change rate and the second threshold T2. The fuzzy PID controller includes a fuzzy logic controller and an incremental PID control module, which is used to output the actual control quantity when the absolute value of the error is greater than the first threshold T1. The inner PI controller is used to output the actual control quantity when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is greater than the second threshold T2. The Bang-bang controller is used to lock and continuously output the actual control quantity of the previous moment when the absolute value of the error is less than or equal to the first threshold T1 and the absolute value of the error change rate is less than or equal to the second threshold T2.

8. The valve positioner system according to claim 7, characterized in that: The control law of the inner PI controller is: The output increment of the kth control cycle : ； This is the basic proportional coefficient of the inner PI controller; The basic integral coefficient of the inner PI controller; The load pressure of the valve actuator; For load adaptive coefficients; The inner PI controller outputs the actual control quantity: 。 9. The valve positioner system according to claim 7, characterized in that: The actual control quantity is transmitted to the main control MCU for PWM duty cycle mapping. The main control MCU converts the actual control quantity into a PWM output signal with a duty cycle of 0%-100%. The PWM output signal output by the main control MCU is amplified by the drive circuit and then drives the I / P conversion unit to perform intake, exhaust, or pressure holding actions.

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