ZN model construction method and device of lag process, electronic equipment and medium
By updating the time constant and superposition of the ZN model, the intermediate ZN model is constructed, which solves the problem of large frequency bandwidth error in the acquisition process of the existing ZN model, and improves the accuracy and error reduction effect of the hysteresis process.
Patent Information
- Application Number
- CN202510210427.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-25
- Publication Date
- 2025-05-23
AI Technical Summary
In the prior art, the error in obtaining the process frequency bandwidth through the ZN model is large, resulting in a large error in the ZN model of the established hysteresis process.
By updating the time constant of the original ZN model according to the superposition amount, the intermediate ZN model is determined, and the superposition amount is updated using the lag process of the intermediate ZN model until the intermediate ZN model is output as the best ZN model.
The accuracy of expressing the first-order pure lag process is improved, so that the error of the first-order pure lag process is greatly reduced.
Smart Images
Figure CN120029043A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of PID control technology, and in particular to a method, device, electronic equipment and medium for constructing a ZN model of a hysteresis process. Background Art
[0002] Compressed air energy storage is a form of electric energy storage that can achieve large-capacity and long-term electric energy storage. It can use off-peak electricity from the power grid and new energy electricity such as wind power and photovoltaics to compress air, store the compressed high-pressure air in gas storage facilities, and release the compressed air when needed to drive the turbine and drive the generator to generate electricity. Compressed air energy storage technology has the advantages of large energy storage capacity, long energy storage cycle, high system efficiency, and long operating life. It is considered to be one of the most promising large-scale energy storage technologies. In recent years, the integration and demonstration projects of compressed air energy storage systems in my country have increased significantly, and its promotion, application and industrialization have further developed.
[0003] When the compressed air energy storage unit is in energy storage and release conditions, the compressed air temperature control is closely related to the unit efficiency and storage capacity. The compressed air temperature is adjusted by adjusting the heat exchange between the heat storage medium and the compressed air, and there is a lag process in the temperature response.
[0004] At present, the Accelerated engineering fastest proportional-integral (AEFPI) controller is used to describe the lag process of temperature response, which significantly improves the feedback control performance compared with the proportional-integral (PI) controller. However, the current engineering tuning of AEFPI parameters is mainly based on the ZN model (Ziegler-Nichols model, ZN). The ZN model represents an engineering model of a process. The error of obtaining the process frequency bandwidth using the ZN model is large, which makes the error of the established ZN model of the lag process also large. Summary of the invention
[0005] In view of this, the present invention provides a method, device, electronic device and medium for constructing a ZN model of a lag process, which solves the technical problem that the error of the frequency bandwidth of the process obtained by using the ZN model is large, resulting in a large error in the established ZN model of the lag process.
[0006] The first aspect of the present invention provides a method for constructing a ZN model of a hysteresis process, comprising:
[0007] The time constant of the original ZN model is updated according to the superposition amount, and the intermediate ZN model is determined according to the updated time constant;
[0008] Inputting a unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model;
[0009] According to the hysteresis process output data of the intermediate ZN model, the superposition amount is updated, and based on the updated superposition amount, the step of updating the time constant of the original ZN model according to the superposition amount and determining the intermediate ZN model according to the updated time constant is performed;
[0010] After the lag process output data of the intermediate ZN model meets the preset error data, the intermediate ZN model is output as the optimal ZN model.
[0011] Preferably, the Laplace transfer function of the intermediate ZN model is:
[0012]
[0013] In the formula, is the Laplace transfer function of the intermediate ZN model, is a constant, is the time constant of the ZN model, is the superposition amount, is the Laplace operator, is the pure lag constant of the first-order pure lag process.
[0014] Preferably, before the step of updating the superposition amount according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition amount, proceeding to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant, the method further comprises:
[0015] Determining a Laplace transfer function of the first-order pure lag process according to the gain and time constant of the first-order pure lag process;
[0016] The unit step is input into the Laplace transfer function of the first-order pure lag process to obtain output data of the first-order pure lag process.
[0017] Preferably, the step of updating the superposition amount according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition amount, transferring to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant comprises:
[0018] Determining an absolute value of an error integral according to the lag process output data of the intermediate ZN model and the output data of the first-order pure lag process;
[0019] Taking minimization of the absolute value of the error integral as the optimization goal, optimizing and updating the superposition amount;
[0020] Based on the updated superposition amount, the process proceeds to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant.
[0021] Preferably, the optimizing and updating of the superposition amount with minimizing the absolute value of the error integral as the optimization target comprises:
[0022] Taking the minimization of the absolute value of the error integral as the optimization goal, an optimization objective function is constructed, and the optimization objective function is:
[0023]
[0024] In the formula, is the absolute value of the error integral, is the time to enter the steady state, Output data for the lag process of the intermediate ZN model, is the output data of the first-order pure lag process, is the derivative symbol, and t is the time.
[0025] Preferably, the method further comprises:
[0026] The optimal ZN model is coupled with a PID controller to obtain a control loop of a first-order pure lag process.
[0027] In a second aspect, the present invention further provides a ZN model construction device for a hysteresis process, comprising:
[0028] A ZN model updating module, used for updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant;
[0029] A ZN model output module, used for inputting a unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model;
[0030] A superposition updating module, configured to update the superposition according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition, proceed to the step of updating the time constant of the original ZN model according to the superposition, and determine the intermediate ZN model according to the updated time constant;
[0031] The ZN model optimization module is used to output the intermediate ZN model as the optimal ZN model when the lag process output data of the intermediate ZN model meets the preset error data.
[0032] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the ZN model construction method for the lag process as described in the first aspect.
[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the steps of the method for constructing a ZN model of a lag process as described in the first aspect are implemented.
[0034] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the ZN model construction method for the lag process as described in the first aspect.
[0035] It can be seen from the above technical scheme that the present invention updates the time constant of the original ZN model according to the superposition amount, determines the intermediate ZN model according to the updated time constant, uses the lag process output data of the intermediate ZN model to update the superposition amount, and outputs the intermediate ZN model as the optimal ZN model until the lag process output data of the intermediate ZN model meets the preset error data, thereby improving the accuracy of expressing the first-order pure lag process and greatly reducing the error of the first-order pure lag process. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 This is a flow chart of compressed air temperature control for a compressed air energy storage system;
[0037] Figure 2 A flowchart of a method for constructing a ZN model of a hysteresis process provided by an embodiment of the present invention;
[0038] Figure 3 A schematic diagram of a compressed air temperature control loop structure designed based on an optimal ZN model provided in an embodiment of the present invention;
[0039] Figure 4 is the step response curve;
[0040] Figure 5 G ZNM The step response curve of the controller controlling the ZN model;
[0041] Figure 6 A schematic diagram of the structure of a ZN model building device for a hysteresis process provided by an embodiment of the present invention;
[0042] Figure 7A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0043] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0044] Compressed air energy storage system compressed air temperature control process Figure 1 At present, the lag process of temperature response is described by the Accelerated engineering fastest proportional-integral (AEFPI) controller, which significantly improves the feedback control performance relative to the proportional-integral (PI) controller. However, the current AEFPI parameter engineering tuning is mainly based on the ZN model (Ziegler-Nicholsmodel, ZN). The ZN model represents an engineering model of a process. The ZN model is expressed as:
[0045]
[0046] In the formula, is the Laplace transfer function of the ZN model; is the gain of the ZN model, unit is dimensionless; is the time constant of the ZN model, in seconds; is the lag time of the ZN model, in seconds.
[0047] The relationship between the frequency bandwidth of the ZN model for the hysteresis process and the ZN model is:
[0048]
[0049] In the formula, is the frequency bandwidth of the lag process, in rad / s.
[0050] To illustrate the problem, the definition process is:
[0051]
[0052] in, is the Laplace transfer function of the process; is the process gain, unit is dimensionless; is the time constant of the process, in seconds; n is the order of the process, in dimensionless integers; is the lag time of the process in seconds.
[0053] Among them, = 100s, and the ZN model given value and theoretical value of the process frequency bandwidth are obtained. The ZNM given value and theoretical value of the process frequency bandwidth are shown in Table 1.
[0054] Table 1
[0055]
[0056] It can be seen that when the ZN model is used to obtain the process frequency bandwidth, the higher the process order n, the closer the value given by the ZN model of the process frequency bandwidth is to the theoretical value, where the error is 100% when the process order n=1 represents a first-order pure lag process; the greater the error in obtaining the process frequency bandwidth using the ZN model, the greater the error of the ZN model of the established process, where the error of the ZN model established for the first-order pure lag process is the largest.
[0057] In view of this, if Figure 2 As shown, the embodiment of the present application provides a method for constructing a ZN model of a hysteresis process, including the following steps S1 to S4. Among them:
[0058] Step S1, updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant.
[0059] Among them, the original ZN model is expressed as:
[0060]
[0061] In the formula, is the Laplace transfer function of the ZN model; is the gain of the ZN model, unit is dimensionless; is the time constant of the ZN model, in seconds; is the lag time of the ZN model, in seconds.
[0062] The superposition is the value of updating the time constant of the original ZN model. The Laplace transfer function of the intermediate ZN model is:
[0063]
[0064] In the formula, is the Laplace transfer function of the intermediate ZN model, is a constant, is the time constant of the ZN model, is the superposition amount, is the Laplace operator, is the pure lag constant of the first-order pure lag process.
[0065] Step S2: input the unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model.
[0066] Among them, when a unit step input (i.e., a step signal with an amplitude of 1) is applied to the intermediate ZN model, assuming that the intermediate ZN model is a linear system with a specific hysteresis characteristic, then the hysteresis process output number of the intermediate ZN model including the step response will be obtained, which is recorded as:
[0067] ,in, Is dimensionless.
[0068] Step S3, updating the superposition amount according to the lag process output data of the intermediate ZN model, and going to step S1 based on the updated superposition amount.
[0069] Among them, in order to judge whether the error of the lag process output data of the intermediate ZN model meets the requirements, the embodiment of the present application also constructs the Laplace transfer function of the first-order pure lag process, and uses the Laplace transfer function of the first-order pure lag process to determine the output data of the first-order pure lag process, and uses the output data of the first-order pure lag process to determine whether the lag process output data of the intermediate ZN model meets the requirements.
[0070] Specifically, the process of determining the output data of a first-order pure lag process includes:
[0071] Step S31: Determine the Laplace transfer function of the first-order pure lag process according to the gain and time constant of the first-order pure lag process.
[0072] Among them, the Laplace transfer function of the first-order pure lag process is:
[0073]
[0074] In the formula, is the Laplace transfer function of the first-order pure lag process, is the gain of the first-order pure lag process, unit is dimensionless, is the time constant of the first-order pure lag process, is the pure lag constant of the first-order pure lag process, in s.
[0075] Step S32: input the unit step into the Laplace transfer function of the first-order pure lag process to obtain output data of the first-order pure lag process.
[0076] Among them, the output data of the first-order pure lag process is , where PV FOLPD (t) is the process output data of the first-order pure lag process, and its unit is dimensionless.
[0077] In some embodiments, in step S3, updating the superposition amount according to the hysteresis process output data of the intermediate ZN model, and turning to step S1 based on the updated superposition amount, comprises:
[0078] Step S301, determining the absolute value of the error integral according to the lag process output data of the intermediate ZN model and the output data of the first-order pure lag process.
[0079] Step S302: Taking minimization of the absolute value of the error integral as the optimization target, the superposition amount is optimized and updated.
[0080] Among them, the optimization objective is to minimize the absolute value of the error integral, and the optimization objective function is constructed. The optimization objective function is:
[0081]
[0082] In the formula, is the absolute value of the error integral, is the time to enter the steady state, Output data for the lag process of the intermediate ZN model, is the output data of the first-order pure lag process, is the derivative symbol, and t is the time.
[0083] Step S303: Go to step S1 based on the updated superposition amount.
[0084] It can be understood that when the function value of the optimization objective function is minimized, that is, converged, the iteration stops and the optimal superposition amount is obtained.
[0085] Step S4: until the hysteresis process output data of the intermediate ZN model meets the preset error data, the intermediate ZN model is output as the optimal ZN model.
[0086] In some embodiments, in order to effectively combine the optimal ZN model and the PID controller, the method further includes: coupling the optimal ZN model and the PID controller to obtain a control loop of a first-order pure lag process.
[0087] In practical applications, such as Figure 3 The compressed air temperature control loop is designed based on the optimal ZN model shown in the figure. Where R is the compressed air temperature set value, unit ℃; G c (s) is the transfer function of the PID controller, and the input is the deviation between the temperature set value and the actual temperature value, in °C; f NZNM(s) is the Laplace transfer function of the new ZN model of the first-order pure lag process; Y is the actual value of the compressed air temperature.
[0088] The first-order pure lag process is
[0089]
[0090] Where fFOLPD(s) is the Laplace transfer function of the first-order pure lag process;
[0091] The ZN model of the first-order pure lag process is
[0092]
[0093] In the formula, f ZNM (s) is the Laplace transfer function of the ZN model of the first-order pure lag process.
[0094] Get PV FOLPD (t) Time to enter steady state T S =600s, at superposition T o =98s, and the absolute value of the error integral V is obtained. DIAV Minimum, the ZN model of the first-order pure lag process is:
[0095]
[0096] Input a unit step and get the process output PV of the first-order pure lag process FOLPD (t), the process output PV of the ZN model of the first-order pure lag process ZNM (t), process output PV of the new ZN model for a first-order pure lag process NZNM (t), such as Figure 4 The step response curve is shown.
[0097] To illustrate the problem, the ZN model and the new ZN model are used to obtain the process frequency band of the first-order pure lag process in the embodiment. For the ZN model,
[0098]
[0099] In the formula, ω PFB is the process frequency bandwidth, in rad / s; T ZN is the time constant of the ZN model, in seconds; T FOLPD is the time constant of the first-order pure lag process, in seconds;
[0100] The best ZN model for the embodiment of the present application is:
[0101]
[0102] The theoretical value of the frequency bandwidth of the first-order pure lag process is
[0103]
[0104] In the formula, ω PFB:T is the theoretical value of the process frequency bandwidth, in rad / s; T FOLPD is the time constant of the first-order pure lag process, in seconds;
[0105] The theoretical value of the frequency bandwidth of a first-order pure lag process is ω PFB:T =0.01red / s; the ZN model of the frequency bandwidth of the first-order pure lag process gives a value of 0.02rad / s; the optimal ZN model of the frequency bandwidth of the first-order pure lag process gives a value of 0.0101rad / s; it can be seen that the ZN model proposed in the embodiment of the present application improves the accuracy of expressing the first-order pure lag process.
[0106] According to the ITAE index optimal PTD controller empirical formula, the compressed air temperature controller based on the ZN model is designed as follows:
[0107]
[0108] According to the ITAE index optimal PTD controller empirical formula, the compressed air temperature controller based on the ZN model proposed in the embodiment of the present application is designed as follows:
[0109]
[0110] G ZNM The step response curve of the controller controlling the ZN model is as follows: Figure 5 Y_ZNM curve, G NZNM The step response curve of the controller controlling the ZN model is as follows: Figure 5 In the Y_NZNM curve, the ITAE index of the Y_ZNM curve is calculated to be 39920, and the ITAE of the Y_NZNM curve is 31080. The ITAE index of Y_NZNM is optimized by 22.1% compared with that of Y_ZNM.
[0111] It should be noted that the embodiment of the present application updates the time constant of the original ZN model according to the superposition amount, determines the intermediate ZN model according to the updated time constant, uses the lag process output data of the intermediate ZN model to update the superposition amount, and outputs the intermediate ZN model as the optimal ZN model until the lag process output data of the intermediate ZN model meets the preset error data, thereby improving the accuracy of expressing the first-order pure lag process and greatly reducing the error of the first-order pure lag process.
[0112] Based on the same inventive concept, an embodiment of the present application further provides a ZN model building device for a hysteresis process for implementing the ZN model building method for the hysteresis process involved above.
[0113] The solution to the problem provided by the device is similar to the solution described in the above method. Therefore, the specific limitations in the embodiments of the ZN model construction device for one or more hysteresis processes provided below can be found in the above limitations on the ZN model construction method for hysteresis processes, which will not be repeated here.
[0114] like Figure 6 As shown, the embodiment of the present application also provides a ZN model construction device for a hysteresis process, comprising:
[0115] A ZN model updating module 100, for updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant;
[0116] A ZN model output module 200, for inputting a unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model;
[0117] The superposition updating module 300 is used to update the superposition according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition, to update the time constant of the original ZN model according to the superposition, and determine the intermediate ZN model according to the updated time constant;
[0118] The ZN model optimization module 400 is used to output the intermediate ZN model as the optimal ZN model when the lag process output data of the intermediate ZN model meets the preset error data.
[0119] In some embodiments, the Laplace transfer function of the intermediate ZN model is:
[0120]
[0121] In the formula, is the Laplace transfer function of the intermediate ZN model, is a constant, is the time constant of the ZN model, is the superposition amount, is the Laplace operator, is the pure lag constant of the first-order pure lag process.
[0122] In some embodiments, the device further comprises:
[0123] A lag process transfer module, used for determining the Laplace transfer function of the first-order pure lag process according to the gain and time constant of the first-order pure lag process;
[0124] The hysteresis output module is used to input a unit step into the Laplace transfer function of a first-order pure lag process to obtain output data of the first-order pure lag process.
[0125] In some embodiments, the superposition updating module 300 is specifically used to determine the absolute value of the error integral based on the output data of the lag process of the intermediate ZN model and the output data of the first-order pure lag process; optimize and update the superposition with minimizing the absolute value of the error integral as the optimization goal; based on the updated superposition, update the time constant of the original ZN model according to the superposition, and determine the intermediate ZN model according to the updated time constant.
[0126] In some embodiments, the superposition amount updating module 300 is further used to construct an optimization objective function with minimization of the absolute value of the error integral as the optimization goal, and the optimization objective function is:
[0127]
[0128] In the formula, is the absolute value of the error integral, is the time to enter the steady state, Output data for the lag process of the intermediate ZN model, is the output data of the first-order pure lag process, is the derivative symbol, and t is the time.
[0129] In some embodiments, the device further includes: a coupling module, which is used to couple the optimal ZN model and the PID controller to obtain a control loop of a first-order pure lag process.
[0130] like Figure 7 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the ZN model construction method of the lag process in any of the above embodiments.
[0131] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the ZN model construction method for the lag process in any of the above embodiments are implemented.
[0132] An embodiment of the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the ZN model construction method for the lag process in any of the above embodiments.
[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, computer storage media and computer program products can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0134] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.
[0135] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
[0136] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.
[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for constructing a ZN model of a hysteresis process, characterized in that: include: The time constant of the original ZN model is updated according to the superposition amount, and the intermediate ZN model is determined according to the updated time constant; Inputting a unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model; According to the hysteresis process output data of the intermediate ZN model, the superposition amount is updated, and based on the updated superposition amount, the step of updating the time constant of the original ZN model according to the superposition amount and determining the intermediate ZN model according to the updated time constant is performed; After the lag process output data of the intermediate ZN model meets the preset error data, the intermediate ZN model is output as the optimal ZN model.
2. The method for constructing a ZN model of a hysteresis process according to claim 1, characterized in that: The Laplace transfer function of the intermediate ZN model is: In the formula, is the Laplace transfer function of the intermediate ZN model, is a constant, is the time constant of the ZN model, is the superposition amount, is the Laplace operator, is the pure lag constant of the first-order pure lag process.
3. The method for constructing a ZN model of a hysteresis process according to claim 1, characterized in that: Before the step of updating the superposition amount according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition amount, proceeding to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant, the method further includes: Determining a Laplace transfer function of the first-order pure lag process according to the gain and time constant of the first-order pure lag process; The unit step is input into the Laplace transfer function of the first-order pure lag process to obtain output data of the first-order pure lag process.
4. The method for constructing a ZN model of a hysteresis process according to claim 3, characterized in that: The step of updating the superposition amount according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition amount, switching to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant, comprises: Determining an absolute value of an error integral according to the lag process output data of the intermediate ZN model and the output data of the first-order pure lag process; Taking minimization of the absolute value of the error integral as the optimization goal, optimizing and updating the superposition amount; Based on the updated superposition amount, the process proceeds to the step of updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant.
5. The method for constructing a ZN model of a hysteresis process according to claim 4, characterized in that: The optimizing and updating the superposition amount by taking minimization of the absolute value of the error integral as the optimization target comprises: Taking the minimization of the absolute value of the error integral as the optimization goal, an optimization objective function is constructed, and the optimization objective function is: In the formula, is the absolute value of the error integral, is the time to enter the steady state, Output data for the lag process of the intermediate ZN model, is the output data of the first-order pure lag process, is the derivative symbol, and t is the time.
6. The method for constructing a ZN model of a hysteresis process according to any one of claims 1 to 5, characterized in that: Also includes: The optimal ZN model is coupled with a PID controller to obtain a control loop of a first-order pure lag process.
7. A ZN model construction device for a hysteresis process, characterized in that: include: A ZN model updating module, used for updating the time constant of the original ZN model according to the superposition amount, and determining the intermediate ZN model according to the updated time constant; A ZN model output module, used for inputting a unit step into the intermediate ZN model to obtain the lag process output data of the intermediate ZN model; A superposition updating module, configured to update the superposition according to the hysteresis process output data of the intermediate ZN model, and based on the updated superposition, proceed to the step of updating the time constant of the original ZN model according to the superposition, and determine the intermediate ZN model according to the updated time constant; The ZN model optimization module is used to output the intermediate ZN model as the optimal ZN model when the lag process output data of the intermediate ZN model meets the preset error data.
8. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the ZN model construction method for the hysteresis process according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for constructing a ZN model of a hysteresis process according to any one of claims 1 to 6 are implemented.
10. A computer program product, characterized in that The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the ZN model construction method for a hysteresis process according to any one of claims 1 to 6.