Self-adaptive nonlinear control method, device and equipment for valve viscosity compensation, medium and product

Abstract

The invention discloses a self-adaptive nonlinear control method, device and equipment for valve viscosity compensation, a medium and a product, and relates to the field of nonlinear compensation control. The method comprises the following steps: determining a gain value according to a deviation between a process variable and a set value in a control loop based on a nonlinear gain function; determining a nonlinear integral value according to the gain value and the deviation between a process variable in a control loop and a set value; evaluating the performance of the control loop according to the information data by adopting an evaluation function to obtain an evaluation index; judging whether the evaluation index is greater than a preset value, and if yes, outputting a corresponding nonlinear integral value; and if not, updating the parameter value of the non-linear gain function by adopting a differential evolution algorithm determined by the crossover operation based on the genetic imprinting, and returning to'determining the gain value based on the non-linear gain function according to the output of the viscous controller '. The method aims at simply, conveniently and efficiently improving the viscosity compensation effect, and stable operation of the production process is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of nonlinear compensation control, and particularly relates to a valve stick compensation adaptive nonlinear control method, device, equipment, medium and product. BACKGROUND

[0002] In industrial processes, with the increasing expansion of enterprise scale, and the increasing requirements for product quality, energy saving, production safety, environmental protection, cost control and other indicators, the role of process control becomes more and more important. However, due to the increasing complexity of modern industrial processes and the lack of routine maintenance of controllers, the phenomenon of poor performance of control loop systems in industrial processes is widespread. A company conducted a two-year investigation and research on the performance of 26000 PID control loops in continuous industrial processes, and the results showed that only 1 / 3 of the control loop system performance was good, and the system performance of the other control loops needed to be improved. Research shows that the improvement of 1% of the control system performance or the improvement of energy utilization in industrial processes will bring tens of millions or even hundreds of millions of dollars in profits.

[0003] The control valve, also known as the regulating valve, is one of the most commonly used process equipment in chemical, petroleum, power generation and other industrial processes. Research shows that 20%-30% of the control loop oscillations are caused by control valves, and the hysteresis, dead zone, stickiness and other nonlinear characteristics of control valves will affect the performance of the control system to varying degrees. Among them, the stickiness characteristic of the control valve is the most common. Therefore, the research on the compensation of the stickiness characteristic of the control valve, as an important part of the research on the performance monitoring of the control system, has very important significance for the safe, stable and efficient production of chemical, petroleum and other industrial processes.

[0004] Currently, in modern industrial process control, even if the existence of the stickiness characteristic of the control valve has been detected, the enterprise will not stop production because of it, because stopping production for equipment maintenance will bring huge losses to the enterprise. The general enterprise equipment maintenance period is half a year to three years, and during this period, the existence of the stickiness characteristic of the control valve not only increases the energy consumption of the enterprise but also accelerates the wear and tear of the equipment. Therefore, it is of great significance to design a compensation algorithm that can overcome the stickiness characteristic of the control valve without stopping production for maintenance, for energy saving and consumption reduction in industrial processes.

[0005] Many scholars have made further exploratory research and proposed non-invasive, safe and reliable valve stick compensation methods, mainly in three categories: one is to design adjustment rules on the basis of existing linear controllers to achieve valve stick compensation. Two is to increase pulse compensator, three is to directly design advanced controller according to the mechanism model of valve and stick to overcome stick nonlinearity. However, the linear controller method cannot compensate for the case where the open loop frequency curve of the linear part of the control loop and the focus of the imaginary axis is greater than 0, the increase of the pulse compensator is easy to cause the wear of the valve rod and other problems, and the direct design of the advanced controller is complex and difficult to promote. SUMMARY

[0006] The purpose of the present application is to provide a valve stick compensation adaptive nonlinear control method, device, equipment, medium and product, which can simply and efficiently improve the stick compensation effect and ensure the smooth operation of the production process.

[0007] To achieve the above purpose, the present application provides the following solutions:

[0008] In a first aspect, the present application provides a valve stick compensation adaptive nonlinear control method, comprising:

[0009] Obtaining information data of a control loop; the information data includes: output of a stick controller and deviation of a process variable and a set value in the control loop;

[0010] Determining a gain value based on a nonlinear gain function according to the deviation of the process variable and the set value in the control loop;

[0011] Determining a nonlinear integral value according to the gain value and the deviation of the process variable and the set value in the control loop;

[0012] Evaluating the performance of the control loop according to the information data by using an evaluation function to obtain an evaluation index;

[0013] Judging whether the evaluation index is greater than a preset value to obtain a first judgment result;

[0014] If the first judgment result is yes, output the corresponding nonlinear integral value;

[0015] If the first judgment result is no, update the parameter value of the nonlinear gain function by using a difference evolution algorithm determined by a genetic imprint-based crossover operation, and return to "determining a gain value based on a nonlinear gain function according to the output of a stick controller".

[0016] In an embodiment, the expression of the nonlinear gain function is:

[0017]

[0018] Where K is the gain; e[k] is the deviation between the process variable and the setpoint in the control loop at time k; K(e[k]) is the gain value corresponding to e[k]; α i , χ i Both ξ and ξ are parameters of the nonlinear gain function.

[0019] In one embodiment, the expression for the evaluation function is:

[0020]

[0021] Where ITAEAU is the evaluation function; e[k] is the deviation between the process variable and the set value in the control loop at time k; u[k] is the output of the viscous controller at time k; W is the acquisition window corresponding to the information data of the control loop; ω k This is the evaluation coefficient.

[0022] In one embodiment, the nonlinear integral value is determined based on the gain value and the deviation between the process variable in the control loop and the set value, specifically including:

[0023] Update parameter i based on the deviation between the process variable and the set value in the control loop. l ; parameter i l The corresponding expression is:

[0024]

[0025] Based on the gain value and parameter i, the nonlinear integral value is determined; the expression corresponding to the nonlinear integral value is:

[0026] i'=K*i l ;

[0027] Where i' is the nonlinear integral value; K is the gain; e[k] is the deviation between the process variable and the set value in the control loop at time k; and W is the acquisition window corresponding to the information data of the control loop.

[0028] In one embodiment, a differential evolution algorithm based on genetic imprinting crossover is used to update the parameter values ​​of the nonlinear gain function, specifically including:

[0029] Determine the initial parameters; the initial parameters include: variable dimension, population size, scaling factor, crossover probability, and set number of iterations; the population consists of multiple individuals; the individual is the parameter value of the nonlinear gain function;

[0030] Initialize population parameters based on variable dimensions and population size;

[0031] Within any given number of iterations, the following is performed:

[0032] Based on the initialized population parameters, three different individuals are randomly selected, and the mutation vector is determined based on the scaling factor.

[0033] The experimental vector is determined by crossover operation based on genetic imprinting and the mutation vector.

[0034] The evaluation function is used as the fitness function, and the fitness values ​​corresponding to the current individual and the experimental vector are calculated respectively.

[0035] Based on the set parameter selection conditions, the optimal parameters are determined according to the fitness values ​​corresponding to the current individual and the experimental vector.

[0036] Determine whether the set number of iterations has been reached to obtain the second determination result;

[0037] If the second judgment result is yes, then the parameter values ​​of the nonlinear gain function are updated based on the optimal parameters;

[0038] If the second judgment result is negative, then return "For any current individual, randomly select three different individuals and determine the mutation vector based on the scaling factor".

[0039] In one embodiment, the parameter selection conditions specifically include:

[0040]

[0041] in, The optimal parameters are given in iteration t+1; u i x is the test vector; i For the current individual; ITIEAU(u i ) for u i The corresponding fitness value; ITIEAU(x) i ) is x i The corresponding fitness value.

[0042] Secondly, this application provides an adaptive nonlinear control device for valve viscosity compensation, comprising:

[0043] The information data acquisition module is used to acquire information data of the control loop; the information data includes: the output of the viscous controller and the deviation between the process variables and the set values ​​in the control loop.

[0044] The gain determination module is used to determine the gain value based on the deviation between the process variable and the set value in the control loop, using a nonlinear gain function.

[0045] The nonlinear integral determination module is used to determine the nonlinear integral value based on the gain value and the deviation between the process variable in the control loop and the set value.

[0046] The evaluation index determination module is used to evaluate the performance of the control loop based on the information data using an evaluation function to obtain evaluation indexes;

[0047] The judgment module is used to determine whether the evaluation index is greater than a preset value and obtain a first judgment result;

[0048] The output module is used to output the corresponding nonlinear integral value when the first judgment result is yes;

[0049] The parameter update and return module is used to update the parameter values ​​of the nonlinear gain function using a differential evolution algorithm determined by crossover operation based on genetic imprinting when the first judgment result is negative, and then return to the "gain determination module".

[0050] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described adaptive nonlinear control method for valve viscosity compensation.

[0051] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned adaptive nonlinear control method for valve viscosity compensation.

[0052] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned adaptive nonlinear control method for valve viscosity compensation.

[0053] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0054] This application provides an adaptive nonlinear control method, device, equipment, medium, and product for valve viscosity compensation. Based on the viscosity characteristics of valves, it utilizes the output of the viscosity controller and the deviation between the process variables and the setpoint in the control loop, along with a nonlinear gain function, to overcome the partial failure shortcomings of traditional linear controller methods. It also inherits the advantage of traditional controllers that they do not require modification of the original control loop, making it easy to promote. Furthermore, an evaluation function, ITAEAU, is proposed to assess the performance of the control loop, obtaining evaluation indices for parameter optimization of the nonlinear gain function, ensuring the valve viscosity compensation effect of the algorithm over long-term operation. Based on the crossover operation of genetic imprinting, the population diversity and evolutionary direction stability of the differential evolution algorithm are guaranteed, thus exhibiting stronger global search capabilities and faster convergence speed in parameter optimization problems. Therefore, this application can simply and efficiently improve the viscosity compensation effect, ensuring stable operation of the production process. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart of an adaptive nonlinear control method for valve viscosity compensation;

[0057] Figure 2 A flowchart illustrating the design of an adaptive nonlinear control method for valve viscosity compensation;

[0058] Figure 3 This is a schematic diagram for use in an actual control loop;

[0059] Figure 4 This is a schematic diagram illustrating the change of the nonlinear gain function;

[0060] Figure 5 This is a diagram illustrating the compensation effect;

[0061] Figure 6 This is a structural diagram of an adaptive nonlinear control device for valve viscosity compensation.

[0062] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] This application proposes an adaptive nonlinear control technique for online compensation of valve stickiness faults. Compared with traditional methods based on additional pulses, linear controller retuning, and advanced control, this application proposes a novel adaptive nonlinear compensation technique for online compensation of valve stickiness faults. This technique can adaptively adjust the parameters of the controller's integral element online according to the performance status of the control loop, thereby improving the stickiness compensation effect. Furthermore, since the nonlinear adaptive compensation technique does not require changes to the original control loop structure, this application is also conducive to large-scale application in actual industrial settings.

[0065] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] In one exemplary embodiment, such as Figure 1 As shown, an adaptive nonlinear control method for valve viscosity compensation is provided, including:

[0067] Step 100: Acquire information data from the control loop. This information data includes the output of the viscous controller and the deviations between the process variables and setpoints in the control loop.

[0068] Step 200: Based on the nonlinear gain function, determine the gain value according to the deviation between the process variable and the set value in the control loop.

[0069] The expression for the nonlinear gain function is:

[0070]

[0071] Where K is the gain; e[k] is the deviation between the process variable and the setpoint in the control loop at time k; K(e[k]) is the gain value corresponding to e[k]; α i , χ i Both ξ and ξ are parameters of the nonlinear gain function.

[0072] Step 300: Determine the nonlinear integral value based on the gain value and the deviation between the process variable and the set value in the control loop.

[0073] The nonlinear integral value is determined based on the gain value and the deviation between the process variable and the set value in the control loop, specifically including:

[0074] Update parameter i based on the deviation between the process variable and the set value in the control loop. l ; parameter i l The corresponding expression is:

[0075]

[0076] The nonlinear integral value is determined based on the gain value and parameter i; the expression corresponding to the nonlinear integral value is:

[0077] i'=K*i l .

[0078] Where i' is the nonlinear integral value; K is the gain; e[k] is the deviation between the process variable and the set value in the control loop at time k; and w is the acquisition window corresponding to the information data of the control loop.

[0079] Step 400: Use an evaluation function to assess the performance of the control loop based on the information data to obtain evaluation indicators.

[0080] The expression for the evaluation function is:

[0081]

[0082] Where ITAEAU is the evaluation function; e[k] is the deviation between the process variable and the set value in the control loop at time k; u[k] is the output of the viscous controller at time k; W is the acquisition window corresponding to the information data of the control loop; ω k This is the evaluation coefficient.

[0083] Step 500: Determine whether the evaluation index is greater than the preset value, and obtain the first judgment result.

[0084] Step 600: If the first judgment result is yes, then output the corresponding nonlinear integral value.

[0085] Step 700: If the first judgment result is negative, then the parameter values ​​of the nonlinear gain function are updated using the differential evolution algorithm determined by the crossover operation based on genetic imprints, and the process returns to step 200.

[0086] A differential evolution algorithm based on genetic imprinting crossover is used to update the parameter values ​​of the nonlinear gain function, specifically including:

[0087] Determine the initial parameters; the initial parameters include: variable dimension, population size, scaling factor, crossover probability, and the number of iterations; the population consists of multiple individuals; the individuals are the parameter values ​​of the nonlinear gain function.

[0088] Population parameters are initialized based on variable dimensions and population size.

[0089] Within any given number of iterations, the following is performed:

[0090] Based on the initialized population parameters, three different individuals are randomly selected, and the mutation vector is determined based on the scaling factor.

[0091] Experimental vectors are determined based on crossover operations using genetic imprinting and mutation vectors.

[0092] The evaluation function is used as the fitness function, and the fitness values ​​corresponding to the current individual and the experimental vector are calculated respectively.

[0093] Based on the set parameter selection conditions, the optimal parameters are determined according to the fitness values ​​corresponding to the current individual and the experimental vector.

[0094] The parameter selection criteria are as follows:

[0095]

[0096] in, The optimal parameters are given in iteration t+1; u i x is the test vector;i For the current individual; ITIEAU(u i ) for u i The corresponding fitness value; ITIEAU(x) i ) is x i The corresponding fitness value.

[0097] Determine whether the set number of iterations has been reached and obtain the second determination result; if the second determination result is yes, then update the parameter values ​​of the nonlinear gain function based on the optimal parameters; if the second determination result is no, then return "For any current individual, randomly select three different individuals and determine the mutation vector based on the scaling factor".

[0098] This application proposes a novel adaptive nonlinear technique for online compensation of valve stickiness faults. The method involves online acquisition of control loop data and updating the nonlinear gain function to compensate for valve stickiness faults. Furthermore, when the control loop performance fails to meet certain conditions, the parameters of the nonlinear gain function are automatically updated to ensure stable operation of the production process.

[0099] Existing methods for compensating valve viscosity mainly include: linear controller-based readjustment methods, additional pulse-based methods, and advanced control-based methods.

[0100] Currently, linear controller retuning algorithms minimize stability deviations in measurements by readjusting rules, such as switching from a PI controller to a P controller or adjusting linear controller parameters, to reduce oscillations caused by viscosity. The steps are as follows: First, the algorithm monitors the stability deviation of the system output in real time to determine if there are oscillations or steady-state errors caused by valve viscosity. Second, when the deviation exceeds a preset threshold, the algorithm switches the controller structure (e.g., from a PI controller to a P controller) and readjusts the controller parameters to reduce the oscillation amplitude and stability deviation. Finally, the adjusted control strategy is applied to the controlled object in real time, and combined with continuous feedback evaluation and parameter optimization, the system's stability and control accuracy are dynamically ensured, effectively mitigating the impact of viscosity on control performance.

[0101] Advantages: No need to change the original control loop structure, reducing production costs and easy to promote.

[0102] Disadvantage: When the intersection of the open-loop frequency curve and the imaginary axis of the linear part of the control loop is greater than 0, the method of tuning the traditional linear controller fails.

[0103] The algorithm based on additional pulses adds a pulse signal with specific width, amplitude, and period to the control signal to overcome the sticky state of the valve stem. The algorithm steps are as follows: First, design the amplitude, width, and triggering conditions of the additional pulse signal and superimpose it onto the output signal of the original controller; second, if an event-triggered mechanism is used, it is necessary to determine in real time whether the system has entered a sticky state, and only apply the additional pulse when the triggering conditions are met, in order to reduce system interference and energy consumption; finally, apply the synthesized control signal to the actuator to drive the valve stem to overcome the sticky region and achieve precise control.

[0104] Advantages: Non-intrusive method, simple and effective algorithm.

[0105] Disadvantages: Causes valve stem wear and is not suitable for long-term use.

[0106] Based on advanced control methods (model predictive control, sliding mode control, etc.), and according to the dynamic model of the valve stem and the nonlinear characteristics of the viscous valve, an advanced controller is designed to effectively compensate for the viscous behavior of the valve. Specifically, the steps include: First, establishing a predictive model of the system; then, constructing a rolling optimization problem and setting corresponding performance indicators and constraints; next, obtaining the real-time state of the system based on current measurements or estimates; subsequently, solving the optimization problem online to obtain the optimal control sequence within the current control time domain; finally, shifting the control time forward by one sampling period and repeating the above process to achieve closed-loop control.

[0107] Advantages: Good valve viscosity compensation effect.

[0108] Disadvantages: High implementation complexity and weak controller generalization ability.

[0109] The linear controller retuning method aims to minimize stability deviations in measurements by readjusting rules, such as switching from a PI controller to a P controller or adjusting linear controller parameters, to reduce oscillations caused by viscosity. However, in certain situations, such as when the open-loop frequency curve of the linear part of the control loop intersects with the imaginary axis greater than 0, the traditional linear controller tuning method fails.

[0110] To address the partial failure of the linear controller retuning algorithm, a pulse-based method was developed. This method incorporates pulse signals with specific widths, amplitudes, and periods into the control signal to overcome valve stem stickiness. However, this algorithm can cause excessive valve stem movement, leading to valve stem wear and making it unsuitable for long-term use.

[0111] To overcome the main drawbacks of the super-added pulse method, an advanced control-based approach has been proposed. This method designs an advanced controller based on the valve stem dynamics model and the nonlinearity of the viscous valve to achieve valve viscous compensation. Although the valve viscous compensation effect is good, this type of method has high implementation complexity due to the need to establish a mathematical model of the control loop, and the designed controller has weak generalization ability.

[0112] To address the main shortcomings of the three existing methods, this application proposes a novel adaptive nonlinear control technique for online compensation of valve stickiness faults. This method acquires control loop data online and updates the nonlinear gain function to compensate for valve stickiness faults. Furthermore, when the control loop performance fails to meet conditions, the parameters of the nonlinear gain function are automatically updated to ensure smooth operation of the production process. The main advantages of this application are: the introduction of a new nonlinear gain function effectively overcomes the failure problem of traditional linear controller methods under certain operating conditions, and it does not require modification of the original control loop structure, making it simple to implement and easy to promote and apply. Simultaneously, considering the characteristics of valve stickiness faults, a new evaluation function, ITAEAU, is designed to optimize the parameters of the nonlinear gain function, thereby improving the stickiness compensation effect. In addition, to improve parameter tuning efficiency, a novel crossover operation based on genetic imprinting is proposed, further ensuring the population diversity and evolutionary stability of the algorithm.

[0113] The adaptive control technology proposed in this application for online compensation of valve stickiness faults is used to compensate for valve stickiness faults online.

[0114] like Figure 2 and Figure 3 As shown, the acquisition window W corresponding to the acquisition of information data of the control loop is set to 1000. The acquisition window e[k] and u[k] in the industrial control loop are acquired, k = 1, 2, ..., 1000. e[k] represents the deviation between the PV value and the SP value at time k, that is, the deviation between the process variable and the set value in the control loop at time k. u[k] represents the output of the viscous controller at time k.

[0115] Set the following nonlinear gain function parameter α i , χ i And the initial value of ξ, and calculate the gain value at time k.

[0116]

[0117] Update parameter i based on error e[k]. l :

[0118]

[0119] Where W is the acquisition window corresponding to the information data of the control loop, and its value is 1000.

[0120] Further, the nonlinear integral value i' is obtained:

[0121] i'=K*i l .

[0122] Substitute e[k] and u[k] into the evaluation function to assess the performance of the control loop.

[0123]

[0124] Determine if the ITAEAU index is greater than 10. If it is, output the integral value and exit the loop; otherwise, continue to the next step.

[0125] The parameters for the differential evolution algorithm are set, and the main parameters are shown in Table 1.

[0126] Table 1. Parameters of Differential Evolution Algorithm

[0127] Parameter Meaning Suggested value D Variable dimension 3 NP Population size 10*D F Scaling factor 0.4-0.9 CR Crossover probability 0.6-1.0 G max ]] Maximum number of iterations 10

[0128] Population parameters are initialized: the population size is set to NP, each individual is a D-dimensional vector, and the parameters are randomly initialized within a given range. The update formula is as follows:

[0129]

[0130] Where i = 1, 2, ..., NP represents the individual index; j = 1, 2, ..., D represents the dimension index. i,j It represents the j-th component of the i-th individual; This is the lower bound of the j-th variable; is the upper bound of the j-th variable; rand(0,1) is a uniform random number in the interval 0 to 1.

[0131] Mutation operation: For each individual, randomly select three distinct individuals x. r1 x r2 x r3 Generate mutation vector v i The formula is as follows:

[0132] v i =x r1 +F·(x r2 -x r3 ).

[0133] Where F∈[0,2] is the scaling factor (usually between 0.4 and 0.9), used to control the magnification of the difference vector.

[0134] Genetic imprinting-based crossover operation: utilizing the mutation vector v i and the current individual x i Generate test vector u i :

[0135] Define the imprint weight vector I:

[0136] I = (i1, i2, ... i) D ).

[0137] Among them, i D Let be the imprint weight on the Dth variable.

[0138] Generated based on probability sampling:

[0139]

[0140] α: The proportion biased towards the father (e.g., 0.3); β: The proportion biased towards the mother (e.g., 0.7).

[0141] Based on the mutation vector and i j Generate test vectors:

[0142] u i,j =i j ·v i,j +(1-i j )·x i,j .

[0143] Among them, u i,j Let i be the j-th component of the experimental individual; j The imprint weight is the weight on the j-th dimension; v i,j x is the component of the mutation vector in the j-th dimension; i,j Let j be the j-th component of the i-th individual.

[0144] Select parameter: Compare u i and x i The better fitness value is selected to enter the next generation:

[0145]

[0146] Determine if the maximum number of iterations has been reached. If not, proceed to the mutation operation step; otherwise, proceed to the next step: output the optimal parameters.

[0147] Update the parameter value α of the nonlinear gain function based on the obtained optimal parameters. i , χ i 、ξ. Figure 4 This is a schematic diagram illustrating the change of the nonlinear gain function.

[0148] Specifically, the outlet temperature of a heating furnace in a chemical plant needs to be precisely controlled near the setpoint (SP). The control objective is to maintain the outlet temperature at the setpoint SP = 450℃, ensuring that the process variable (PV) is stably controlled within an error range of ±3℃. See the appendix for specific compensation effects. Figure 5 .

[0149] 1) Set the acquisition window W=1000 and acquire data per second. The data e[k] and u[k] at time k are acquired by the sensor and are 0.5 and 0.5 respectively.

[0150] 2) Set the nonlinear gain function parameter α i , χ i The initial values ​​of ξ are 1.5, 0.5, and 3.3. Substituting the value of e[k] into the nonlinear gain function, we obtain the value of gain K as 1.4543.

[0151] 3) Substituting the value of e[k] into the equation, we get the value of parameter i as 0.7.

[0152] 4) Based on 2) and 3), the nonlinear integral value i' is 1.018.

[0153] 5) Based on the 1000 data points collected in 1), the value of ITAEAU is calculated to be 11.

[0154] 6) If ITAEAU < 10, output the value 1.018 directly; otherwise, proceed to the next step. The value of ITAEAU calculated from step 5) is 11, so proceed to the next step.

[0155] 7) Set NP to 30 and initialize the population parameters according to the formula.

[0156]

[0157] 8) Set F to 0.8, randomly select three distinct individuals and perform a mutation operation to obtain v. i .

[0158] v i =x r1 +F·(x r2 -x r3 ).

[0159] 9) Set the values ​​of α and β to 0.5 and 0.5 respectively, and obtain μ based on the genetic imprinting crossover operation. i .

[0160] 10) Select appropriate parameter values ​​from 8) and 9) according to the following formulas:

[0161]

[0162] 11) Determine if the maximum number of iterations has been reached. If not, go to step 8); otherwise, proceed to the next step.

[0163] 12) Output the optimal parameters.

[0164] Update the parameter value α of the nonlinear gain function in step 2) based on the optimal parameters obtained in step 12). i , χ i 、ξ.

[0165] This application proposes for the first time an adaptive compensation-optimization framework for valve stickiness faults. Based on the control error, i.e., the deviation e between the process variable and the setpoint in the control loop, a novel nonlinear gain function is proposed. Based on the characteristics of valve stickiness, and utilizing the control error e and the output u of the sticky controller, a new control loop performance index, ITAEAU, is proposed for parameter optimization of the nonlinear gain function.

[0166] The crossover operation based on genetic imprinting ensures the population diversity and the stability of the evolutionary direction of the algorithm.

[0167] By using a new nonlinear gain function, the drawback of partial failure of the traditional linear controller method is overcome, while inheriting the advantage of the traditional controller that it does not require changes to the original control loop, making it easy to promote.

[0168] Since the nonlinear gain function adjusts the controller output online based on the error, the algorithm is simple and there is no external pulse signal, so the valve stem will not move excessively and cause wear.

[0169] The control loop performance index ITAEAU is used for parameter optimization of the nonlinear gain function to ensure the valve viscosity compensation effect of the algorithm during long-term operation.

[0170] The differential evolution algorithm based on genetic imprinting ensures the diversity of the population and the stability of the evolutionary direction, thus exhibiting stronger global search capabilities and faster convergence speed in parameter optimization problems.

[0171] In one exemplary embodiment, such as Figure 6 As shown, an adaptive nonlinear control device for valve viscosity compensation is provided, comprising:

[0172] The information data acquisition module is used to acquire information data from the control loop; the information data includes: the output of the viscous controller and the deviation between the process variables and the set values ​​in the control loop.

[0173] The gain determination module is used to determine the gain value based on the deviation between the process variable and the set value in the control loop, using a nonlinear gain function.

[0174] The nonlinear integral determination module is used to determine the nonlinear integral value based on the gain value and the deviation between the process variable and the set value in the control loop.

[0175] The evaluation index determination module is used to evaluate the performance of the control loop based on information data using an evaluation function to obtain evaluation indexes.

[0176] The judgment module is used to determine whether the evaluation index is greater than the preset value and obtain the first judgment result.

[0177] The output module is used to output the corresponding nonlinear integral value when the first judgment result is yes.

[0178] The parameter update and return module is used to update the parameter values ​​of the nonlinear gain function using a differential evolution algorithm determined by crossover operation based on genetic imprinting when the first judgment result is negative, and then return to the "gain determination module".

[0179] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores adaptive nonlinear control data for valve viscosity compensation. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive nonlinear control method for valve viscosity compensation.

[0180] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0181] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0182] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0183] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0184] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0185] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0186] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0187] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, 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 this application.

Claims

1. An adaptive nonlinear control method for valve viscosity compensation, characterized in that, include: Acquire information data from the control loop; the information data includes: the output of the viscous controller and the deviation between the process variables and the set values ​​in the control loop; Based on the nonlinear gain function, the gain value is determined according to the deviation between the process variable and the set value in the control loop; The nonlinear integral value is determined based on the gain value and the deviation between the process variable and the set value in the control loop; The performance of the control loop is evaluated using an evaluation function based on the information data to obtain evaluation indicators; Determine whether the evaluation index is greater than a preset value to obtain a first determination result; If the first judgment result is yes, then output the corresponding nonlinear integral value; If the first judgment result is negative, then the differential evolution algorithm determined by crossover operation based on genetic imprinting is used to update the parameter value of the nonlinear gain function, and the result is "Based on the nonlinear gain function, the gain value is determined according to the output of the viscous controller".

2. The adaptive nonlinear control method for valve viscosity compensation according to claim 1, characterized in that, The expression for the nonlinear gain function is: Where K is the gain; e[k] is the deviation between the process variable and the setpoint in the control loop at time k; K(e[k]) is the gain value corresponding to e[k]; α i , χ i Both ξ and ξ are parameters of the nonlinear gain function.

3. The adaptive nonlinear control method for valve viscosity compensation according to claim 1, characterized in that, The expression for the evaluation function is: Where ITAEAU is the evaluation function; e[k] is the deviation between the process variable and the set value in the control loop at time k; u[k] is the output of the viscous controller at time k; W is the acquisition window corresponding to the information data of the control loop; ω k This is the evaluation coefficient.

4. The adaptive nonlinear control method for valve viscosity compensation according to claim 1, characterized in that, The nonlinear integral value is determined based on the gain value and the deviation between the process variable and the set value in the control loop, specifically including: Update parameter i based on the deviation between the process variable and the set value in the control loop. l ; parameter i l The corresponding expression is: Based on the gain value and parameter i l The nonlinear integral value is determined; the expression corresponding to the nonlinear integral value is: i'=K*i l ; Where i' is the nonlinear integral value; K is the gain; e[k] is the deviation between the process variable and the set value in the control loop at time k; and W is the acquisition window corresponding to the information data of the control loop.

5. The adaptive nonlinear control method for valve viscosity compensation according to claim 1, characterized in that, The parameter values ​​of the nonlinear gain function are updated using a differential evolution algorithm determined by crossover operations based on genetic imprinting, specifically including: Determine the initial parameters; the initial parameters include: variable dimension, population size, scaling factor, crossover probability, and set number of iterations; the population consists of multiple individuals; the individual is the parameter value of the nonlinear gain function; Initialize population parameters based on variable dimensions and population size; Within any given number of iterations, the following is performed: Based on the initialized population parameters, three different individuals are randomly selected, and the mutation vector is determined based on the scaling factor. The experimental vector is determined by crossover operation based on genetic imprinting and the mutation vector. The evaluation function is used as the fitness function, and the fitness values ​​corresponding to the current individual and the experimental vector are calculated respectively. Based on the set parameter selection conditions, the optimal parameters are determined according to the fitness values ​​corresponding to the current individual and the experimental vector. Determine whether the set number of iterations has been reached to obtain the second determination result; If the second judgment result is yes, then the parameter values ​​of the nonlinear gain function are updated based on the optimal parameters; If the second judgment result is negative, then return "For any current individual, randomly select three different individuals and determine the mutation vector based on the scaling factor".

6. The adaptive nonlinear control method for valve viscosity compensation according to claim 5, characterized in that, The parameter selection criteria are as follows: in, The optimal parameters are given in iteration t+1; u i x is the test vector; i For the current individual; ITIEAU(u i ) for u i The corresponding fitness value; ITIEAU(x) i ) is x i The corresponding fitness value.

7. An adaptive nonlinear control device for valve viscosity compensation, characterized in that, include: The information data acquisition module is used to acquire information data of the control loop; the information data includes: the output of the viscous controller and the deviation between the process variables and the set values ​​in the control loop. The gain determination module is used to determine the gain value based on the deviation between the process variable and the set value in the control loop, using a nonlinear gain function. The nonlinear integral determination module is used to determine the nonlinear integral value based on the gain value and the deviation between the process variable in the control loop and the set value. The evaluation index determination module is used to evaluate the performance of the control loop based on the information data using an evaluation function to obtain evaluation indexes; The judgment module is used to determine whether the evaluation index is greater than a preset value and obtain a first judgment result; The output module is used to output the corresponding nonlinear integral value when the first judgment result is yes; The parameter update and return module is used to update the parameter values ​​of the nonlinear gain function using a differential evolution algorithm determined by crossover operation based on genetic imprinting when the first judgment result is negative, and then return to the "gain determination module".

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the adaptive nonlinear control method for valve viscosity compensation according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the adaptive nonlinear control method for valve viscosity compensation as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the adaptive nonlinear control method for valve viscosity compensation as described in any one of claims 1-6.

Images