CN115437425A

CN115437425A - Temperature control method, device, equipment and storage medium

Info

Publication number: CN115437425A
Application number: CN202211206401.3A
Authority: CN
Inventors: 雷晓伟
Original assignee: Shenzhen Inovance Technology Co Ltd
Current assignee: Shenzhen Inovance Technology Co Ltd
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2022-12-06

Abstract

The application discloses a temperature control method, a device, equipment and a storage medium, wherein the temperature control method comprises the following steps: acquiring the set temperature, the real-time temperature and the heating speed of a controlled object; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. The method and the device solve the problems of poor robustness and adaptability of the temperature control method, enhance working condition adaptability and improve temperature control effect.

Description

Temperature control method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of temperature control, and more particularly, to a method, an apparatus, a device and a storage medium for temperature control.

Background

In the field of industrial control, particularly in industries strongly related to temperature, such as lithium battery monomer furnaces, foam molding machines, flat vulcanizing machines, molding machines and the like, the requirement on temperature control is high, and therefore, a set of safe and reliable temperature control system is very important. The current temperature control system usually adopts a PID control mode to realize accurate control of temperature, but most temperature control systems have the characteristics of large hysteresis and large inertia and have the characteristics of time variation, uncertainty and nonlinearity, so that the conventional PID control method with better control effect on a linear steady system has the defects of slow temperature rise, large overshoot and the like on the control of a nonlinear and time-varying system, and cannot automatically adjust parameters on line, so that the ideal control effect is difficult to realize.

The fuzzy self-adaptive PID control is a control method combining conventional PID control and fuzzy control, and can dynamically adjust PID parameters to realize temperature control by using a fuzzy control rule based on a basic universe of discourse and a fuzzy universe of discourse according to real-time deviation and deviation change rate. The fuzzy self-adaptive PID control can solve the defects existing in the conventional PID control method to a certain extent, and can realize better dynamic and static performances. However, because a general fuzzy adaptive PID control uses a fixed basic discourse domain and a fuzzy discourse domain, different requirements of different working conditions and different moments on PID parameters are difficult to meet, so that the robustness and the adaptability of a temperature control system are poor, and the control effect on the temperature is poor.

Therefore, it is necessary to provide a temperature control scheme for enhancing the adaptability to the operating conditions.

Disclosure of Invention

The application mainly aims to provide a temperature control method, a temperature control device, temperature control equipment and a storage medium, and aims to solve the problems of poor robustness and adaptability of the temperature control method, enhance working condition adaptability and improve the temperature control effect.

In order to achieve the above object, the present application provides a temperature control method, including:

acquiring the set temperature, the real-time temperature and the heating speed of a controlled object;

determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature;

determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse;

determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table;

and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value.

Optionally, the step of determining the basic domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise rate includes:

acquiring a first deviation expansion coefficient corresponding to the temperature deviation change value; acquiring a second deviation expansion coefficient corresponding to the temperature deviation change rate;

determining the first fundamental domain of discourse according to the first deviation expansion coefficient and the PID initial parameter value; and determining the second fundamental domain of discourse according to the second deviation expansion coefficient and the temperature rise speed.

Optionally, the obtaining a first deviation expansion coefficient corresponding to the temperature deviation change value; and the step of obtaining a second deviation expansion coefficient corresponding to the temperature deviation change rate comprises the following steps:

acquiring working condition information of the controlled object;

and determining a first deviation expansion coefficient corresponding to the temperature deviation change value and a second deviation expansion coefficient corresponding to the temperature deviation change rate according to the working condition information.

Optionally, the mapping relationship table includes a first mapping relationship table and a second mapping relationship table, and the step of establishing the mapping relationship table according to the basic discourse domain includes:

determining a first fuzzy domain and a first quantization scale factor of the temperature deviation change value according to the first fundamental domain; and determining a second ambiguity domain and a second quantization scale factor of the temperature deviation change rate according to the second fundamental domain;

establishing the first mapping relation table according to the first fundamental domain, the first fuzzy domain, the first quantization scale factor, the second fundamental domain, the second fuzzy domain and the second quantization scale factor;

and determining the second mapping relation table according to the first mapping relation table.

Optionally, the step of determining a PID parameter variation value according to the mapping relationship table and a preset fuzzy rule table includes:

quantizing the temperature deviation change value and the temperature deviation change rate according to the mapping relation table to obtain a first quantization result of the temperature deviation change value and a second quantization result of the temperature deviation change rate;

determining a first membership degree of the temperature deviation variation value according to the first quantization result; determining a second membership degree of the temperature deviation change rate according to the second quantification result;

and determining the PID parameter variation value according to the first membership degree, the second membership degree and the preset fuzzy rule table.

Optionally, the step of determining the PID initial parameter value according to the set temperature and the real-time temperature includes:

performing parameter self-tuning on the set temperature and the real-time temperature to obtain a PID tuning parameter value;

acquiring self-tuning empirical values corresponding to the set temperature and the real-time temperature;

and adjusting the PID setting parameter value according to the self-setting empirical value to obtain the PID initial parameter value.

Optionally, the step of determining the PID target parameter value according to the PID initial parameter value and the PID parameter variation value includes:

determining PID control parameter values according to the PID initial parameter values and the PID parameter change values;

and correcting the PID control parameter value to obtain a PID target parameter value.

The embodiment of this application still provides a temperature control device, temperature control device includes:

the temperature acquisition module is used for acquiring the set temperature, the real-time temperature and the heating speed of the controlled object;

the temperature calculation module is used for determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature;

the relation table building module is used for determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed and building a mapping relation table according to the basic discourse domain;

the change value determining module is used for determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table;

and the temperature control module is used for determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value and controlling the temperature of the controlled object according to the PID target parameter value.

An embodiment of the present application further provides an apparatus, where the apparatus includes a memory, a processor, and a temperature control program stored in the memory and capable of running on the processor, and the temperature control program, when executed by the processor, implements the steps of the temperature control method described above.

An embodiment of the present application further provides a computer-readable storage medium, where a temperature control program is stored on the computer-readable storage medium, and the temperature control program, when executed by a processor, implements the steps of the temperature control method described above.

According to the temperature control method, the temperature control device, the temperature control equipment and the storage medium, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Drawings

FIG. 1 is a schematic diagram of functional modules of an apparatus to which a temperature control device of the present application belongs;

FIG. 2 is a schematic flow chart of a first exemplary embodiment of a temperature control method of the present application;

FIG. 3 is a schematic flow chart of a second exemplary embodiment of a temperature control method of the present application;

FIG. 4 is a schematic flow chart of a third exemplary embodiment of a temperature control method of the present application;

FIG. 5 is a schematic flow chart of a fourth exemplary embodiment of a temperature control method of the present application;

FIG. 6 is a schematic flow chart of a fifth exemplary embodiment of a temperature control method of the present application;

FIG. 7 is a schematic flow chart of a fuzzy adaptive PID control process based on self-tuning in a domain of discourse according to an embodiment of the temperature control method of the present application;

FIG. 8 is a schematic diagram of a temperature control system according to an embodiment of the temperature control method of the present application;

FIG. 9 is a schematic structural diagram of a temperature control system according to an embodiment of the temperature control method of the present application;

fig. 10 is a schematic flowchart of a fuzzy adaptive algorithm process according to an embodiment of the temperature control method of the present application.

The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The main solution of the embodiment of the application is as follows: acquiring the set temperature, the real-time temperature and the heating speed of a controlled object; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme of the embodiment of the application, the basic discourse domain is determined according to the information such as the temperature rise speed, the mapping relation table is established according to the basic discourse domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as the different temperature rise speeds, and the fuzzy self-adaptive control of the variable discourse domain is realized. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that a control system has strong robustness and adaptability, a controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Specifically, referring to fig. 1, fig. 1 is a functional module schematic diagram of an apparatus to which the temperature control device of the present application belongs. The temperature control device may be a device independent of the apparatus and capable of temperature control, and may be carried on the apparatus in the form of hardware or software.

In this embodiment, the apparatus of the temperature control device at least includes an output module 110, a processor 120, a memory 130 and a communication module 140.

An operating system and a temperature control program are stored in the memory 130, and the temperature control device can store the acquired set temperature, real-time temperature and heating speed of the controlled object, the temperature deviation variable and proportional-integral-derivative PID initial parameter value of the controlled object determined according to the set temperature and the real-time temperature, the fundamental domain of the temperature deviation variable determined according to the PID initial parameter value and the heating speed, the mapping relation table established according to the fundamental domain, the preset fuzzy rule table, the PID parameter variation value determined according to the mapping relation table and the preset fuzzy rule table, and the PID target parameter value determined according to the PID initial parameter value and the PID parameter variation value in the memory 130; the output module 110 may be a display screen or the like. The communication module 140 may include a WIFI module, a mobile communication module, a bluetooth module, and the like, and communicates with an external device or a server through the communication module 140.

Wherein the temperature control program in the memory 130 when executed by the processor implements the steps of:

determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain;

Further, the temperature control program in the memory 130 when executed by the processor further implements the steps of:

determining the first fundamental domain of discourse according to the first deviation expansion coefficient and the PID initial parameter value; and determining the second fundamental domain of discourse according to the second deviation expansion coefficient and the temperature rising speed.

acquiring working condition information of the controlled object;

Further, the temperature control program in the memory 130 when executed by the processor further performs the steps of:

determining a first membership degree of the temperature deviation variation value according to the first quantification result; determining a second membership degree of the temperature deviation change rate according to the second quantification result;

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic discourse domain is determined according to the information such as the temperature rise speed, the mapping relation table is established according to the basic discourse domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as the different temperature rise speeds, and fuzzy self-adaptive control of the variable discourse domain is achieved. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Based on the above device architecture, but not limited to the above architecture, embodiments of the method of the present application are presented.

First embodiment

Referring to fig. 2, fig. 2 is a schematic flow chart of a first exemplary embodiment of the temperature control method of the present application. The application scenario of the method of the embodiment relates to the field of industrial control, and the execution main body of the method can be a temperature control device or a temperature control system or equipment. In this embodiment, a temperature control device is taken as an example, and the temperature control method implemented on the temperature control device includes:

and step S10, acquiring the set temperature, the real-time temperature and the heating speed of the controlled object.

Specifically, a set temperature, a real-time temperature and a heating rate of a controlled object are obtained, wherein the set temperature refers to a preset temperature of the controlled object; the real-time temperature refers to the real-time temperature of the controlled object through detection and acquisition; the temperature increase rate is a change amount of the temperature per unit time. The real-time temperature may be acquired by a temperature sensor.

And S20, determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature.

Specifically, according to the acquired set temperature and the acquired real-time temperature, and according to a set correlation rule, determining and obtaining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object. The temperature deviation variable refers to a variable related to the trend of the temperature change of the controlled object, such as the speed/speed of temperature increase or temperature decrease. The PID is a control loop feedback mechanism widely applied to an industrial control system, and mainly comprises a proportional control (P) for controlling the proportional relation between the output and the input of a controlled object, an integral control (I) for eliminating a steady-state error and a differential control (D) for weakening overshoot and increasing the inertia response speed. The PID initial parameter values are initial values of three determined parameters of the controller, namely a proportional parameter, an integral parameter and a differential parameter.

And S30, determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse.

Specifically, according to the PID initial parameter value and the temperature rise rate, a fundamental domain of the temperature deviation variable is determined according to a set correlation rule, and a mapping relationship table is established according to the fundamental domain, wherein the mapping relationship table is a rule table in which the determined fundamental domain and the determined fuzzy domain form a corresponding relationship according to a certain corresponding rule, and is used for mapping a specific value of the variable in the fundamental domain into a range parameter of the variable in the fuzzy domain. Further, according to information such as different heating rates and the like acquired under different working conditions and different moments, the basic domain of discourse of the temperature deviation variable is determined again, and the mapping relation table is dynamically adjusted and updated according to the basic domain of discourse.

And S40, determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table.

Specifically, the temperature deviation variable is mapped into a range parameter of the temperature deviation variable in a fuzzy domain according to the established mapping relation table, and a PID parameter change value is determined by combining the obtained range parameter and a preset fuzzy rule table.

And S50, determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value.

Specifically, a PID target parameter value is determined and obtained according to the determined PID initial parameter value and the PID parameter variation value, and then the temperature of the controlled object is controlled according to the obtained PID target parameter value.

Second embodiment

Based on the first embodiment, the present embodiment also discloses a method for determining the basic domain of the temperature deviation variable. Referring to fig. 3, fig. 3 is a schematic flow chart of a second exemplary embodiment of the temperature control method of the present application. In this embodiment, the temperature deviation variables may include a temperature deviation change value and a temperature deviation change rate, wherein the temperature deviation change value E is obtained by acquiring the set temperature (T) ₀ ) And real time temperature (T) ₁ ) Performing difference obtaining, namely E = T ₀ -T ₁ (ii) a The temperature deviation change rate EC is obtained by derivation of the temperature deviation change value EThis resulted in EC = dE/dt = E (t) -E (t-1).

In addition, in this embodiment, the basic domains may include a first basic domain corresponding to the temperature deviation change value and a second basic domain corresponding to the temperature deviation change rate, and the step of determining the basic domain of the temperature deviation variable according to the PID initial parameter value and the temperature increase rate may include:

step S31, acquiring a first deviation expansion coefficient corresponding to the temperature deviation change value; acquiring a second deviation expansion coefficient corresponding to the temperature deviation change rate;

step S32, determining the first basic discourse domain according to the first deviation expansion coefficient and the PID initial parameter value; and determining the second fundamental domain of discourse according to the second deviation expansion coefficient and the temperature rising speed.

Specifically, a first deviation expansion coefficient corresponding to the temperature deviation change value is obtained, and the first basic discourse domain is determined according to the first deviation expansion coefficient and the PID initial parameter value. And then, acquiring a second deviation expansion coefficient corresponding to the temperature deviation change rate, and determining the second basic domain of discourse according to the second deviation expansion coefficient and the temperature rise speed.

Further, the embodiment also discloses a method for acquiring a first deviation expansion coefficient corresponding to the temperature deviation change value and acquiring a second deviation expansion coefficient corresponding to the temperature deviation change rate. In the step S31, a first deviation expansion coefficient corresponding to the temperature deviation variation value is obtained; and obtaining a second deviation expansion coefficient corresponding to the temperature deviation change rate may include:

step S311, obtaining the working condition information of the controlled object;

step S312, determining a first deviation expansion coefficient corresponding to the temperature deviation change value and a second deviation expansion coefficient corresponding to the temperature deviation change rate according to the operating condition information.

Specifically, the operating condition information of the controlled object is obtained, where the operating condition information includes, but is not limited to, an expert experience value corresponding to a temperature deviation change value, an expert experience value corresponding to a temperature deviation change rate, and the like. And then, determining a first deviation expansion coefficient corresponding to the temperature deviation change value and a second deviation expansion coefficient corresponding to the temperature deviation change rate according to the working condition information.

Illustratively, the first basic domain of discourse is determined by: acquiring a first deviation expansion coefficient alpha corresponding to the temperature deviation change value E, wherein the value of the first deviation expansion coefficient alpha is 1.2 according to expert experience; according to PID initial parameter value-proportional initial parameter value K _p 0, setting the proportional output to K _p 0 × E; when the proportional output is at a maximum, according to E = (100/K) _p 0) A is calculated where 100 is the maximum proportional output. When the proportional initial parameter value K is under one working condition _p When 0=5, E = (100/5) × 1.2=24, and the basic domain of the temperature deviation change value E at this time is (-24, 24); and when the other working condition is the proportional initial parameter value K _p When 0= (100/20) × 1.2=6, then the basic domain for the temperature deviation E is (-6,6).

The second basic discourse domain determining method comprises the following steps: acquiring a second deviation expansion coefficient beta corresponding to the temperature deviation change rate EC, wherein the value of the second deviation expansion coefficient beta is 2 according to expert experience; and according to the obtained heating rate, selecting the maximum heating rate S (the unit is ℃/t, and t is the sampling period) of each sampling period according to the heating rate, and then calculating according to EC = S. Under one working condition, the maximum temperature rise speed S =0.2 ℃/t in the parameter self-setting process, so EC = S beta = 0.2X 2=0.4, so the basic domain of the deviation change rate EC of the current working condition is (-0.4, 0.4); and under the other working condition, the maximum temperature rise speed S =4 ℃/t in the parameter self-setting process, so EC = S ×. Beta =4 ×. 2=8, so the basic domain of the deviation change rate EC of the current working condition is (-8,8).

Third embodiment

Based on the first embodiment and the second embodiment, this embodiment also discloses a method for establishing a mapping relation table according to the basic domain of discourse. Referring to fig. 4, fig. 4 is a schematic flow chart of a third exemplary embodiment of the temperature control method of the present application. In this embodiment, the mapping relationship table may include a first mapping relationship table and a second mapping relationship table, and the step of establishing the mapping relationship table according to the basic domain of discourse may include:

step S33, determining a first fuzzy domain and a first quantization scale factor of the temperature deviation change value according to the first basic domain; and determining a second ambiguity domain and a second quantization scale factor of the temperature deviation change rate according to the second fundamental domain.

Specifically, a first ambiguity domain and a first quantization scale factor of the temperature deviation variation value are determined according to a first fundamental domain, wherein the first fundamental domain is determined as [ E [ [ ] _min ,E _max ]Using a negative large [ NB]Negative pressureMiddle [ NM ]]Negative small [ NS ]]Zero [ ZO ]]Small and positive [ PS ]]Middle (middle) of the body (PM)]Positive large [ PB]The 7 linguistic variables are used as fuzzy subsets of the variation value of the temperature deviation, and the corresponding first fuzzy domain can be determined as [ -6,6]The first quantization scale factor may be determined to be 6/E _max . Then, a second ambiguity domain and a second quantization scale factor of the temperature deviation change rate are determined according to a second fundamental domain, wherein the second fundamental domain is determined as [ EC _min ,EC _max ]Using a negative large [ NB]Negative middle [ NM]Negative small [ NS ]]Zero [ ZO ]]Small and positive [ PS ]]Middle (middle) of the body (PM)]Positive large [ PB]The 7 linguistic variables are used as fuzzy subsets of the temperature deviation change rate, and the corresponding second fuzzy domain can be determined as [ -6,6]The second quantization scale factor may be determined to be 6/EC _max 。

Step S34, establishing the first mapping relation table according to the first fundamental domain, the first domain of confusion, the first quantization scale factor, the second fundamental domain of discourse, the second domain of confusion and the second quantization scale factor.

Specifically, a first mapping relation table shown in the following table one is established according to correspondence between the first fundamental domain, the first domain of confusion, the first quantization scale factor, the second fundamental domain of confusion, the second domain of ambiguity, and the second quantization scale factor:

table one: first mapping relation table

And step S35, determining the second mapping relation table according to the first mapping relation table.

Specifically, a mapping relationship table of the PID parameters, i.e., a second mapping relationship table, is determined according to the established first mapping relationship table, as shown in the following table two:

a second table: second mapping relation table

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Fourth embodiment

Based on the first, second, and third embodiments, this embodiment also discloses a method for determining a PID parameter variation value according to the mapping relationship table and a preset fuzzy rule table. Referring to fig. 5, fig. 5 is a schematic flow chart of a fourth exemplary embodiment of the temperature control method of the present application. In this embodiment, the step S40 of establishing the mapping relationship table according to the basic discourse domain may include:

step S41, quantizing the temperature deviation change value and the temperature deviation change rate according to the mapping relation table to obtain a first quantization result of the temperature deviation change value and a second quantization result of the temperature deviation change rate.

Specifically, the temperature deviation change value and the temperature deviation change rate are quantized according to the established mapping relation table, and a first quantization result of the temperature deviation change value and a second quantization result of the temperature deviation change rate are correspondingly obtained.

Step S42, determining a first membership degree of the temperature deviation variation value according to the first quantization result; and determining a second membership of the rate of change of temperature deviation according to the second quantization result.

Specifically, according to the first quantization result, a first membership of the temperature deviation variation value is determined by combining a preset triangular membership function of the temperature deviation variation value. And determining a second membership degree of the temperature deviation change rate by combining a preset triangular membership degree function of the temperature deviation change rate according to the second quantization result.

And S43, determining the PID parameter variation value according to the first membership degree, the second membership degree and the preset fuzzy rule table.

Specifically, the PID parameter variation value is determined by comparing the determined first membership degree, the determined second membership degree and a preset fuzzy rule table. In this embodiment, before the step of determining the PID parameter variation value according to the first degree of membership, the second degree of membership, and a preset fuzzy rule table, the method may further include a step of designing and generating the fuzzy rule table, and specifically may include:

according to a formulated ratio K _p Fuzzy rule of (3), generating K _p Fuzzy rule table (iv).

In particular, according to a defined ratio K _p Fuzzy rule of (1), generating the ratio K _p Fuzzy rule table (iv). Wherein the ratio K _p The fuzzy rule of (1) is: ratio K _p The selection of the system determines the response speed of the system; according to the ratio K at different times _p Integral K _i And a differential K _d The effect of the three parameters and the relationship between them, increasing K _p Can improve response speed and reduce steady state deviation, butIs K _p Too large a value will produce a large overshoot, even making the system unstable; reduction of K _p Can reduce overshoot and improve stability, but K _p If the value is too small, the response speed is reduced, and the adjustment time is prolonged; therefore, the initial adjustment should be made to have a suitably large K _p To increase the response speed; and in the middle of regulation, K _p The value is smaller, so that the system has smaller overshoot and certain response speed is ensured; and at the later stage of the regulation process, K is added _p The value is adjusted to a larger value to reduce the static difference and improve the control precision. Based on the formulated ratio K _p The fuzzy rule of (2) generates a ratio K as shown in Table three below _p Fuzzy rule table of (1):

a third table: ratio K _p Fuzzy rule table of

Then, according to the established integral K _i Fuzzy rule of (1), generating K _i The fuzzy rule table of (1).

In particular, according to a formulated integral K _i To generate the integral K _i Fuzzy rule table (iv). Wherein the integral K _i The fuzzy rule of (1) is: the integral control is mainly used for eliminating the steady-state deviation of the system, and due to some reasons (such as saturation nonlinearity and the like), the integral saturation is possibly generated in the initial stage of the adjusting process in the integral process, so that the larger overshoot of the adjusting process is caused, and therefore, in the initial stage of the adjusting process, in order to prevent the integral saturation, the integral action of the integral control is weak, and even can be zero; in the middle of the adjustment, in order to avoid affecting the stability, the integral action of the method should be moderate; finally, later in the adjustment process, the integration should be enhanced to reduce the adjustment dead center. According to the established integral K _i To generate the integral K shown in table four below _i Fuzzy rule table of (1):

table four: integral K _i Fuzzy rule table of

Finally, according to the differential K _d Fuzzy rule of (3), generating K _d The fuzzy rule table of (1).

In particular, according to the established differential K _d Generating a differential K _d Fuzzy rule table (iv). Wherein the differential K _d The fuzzy rule of (1) is: the adjustment of the differential control is mainly introduced aiming at the large inertia process, and the differential element coefficient has the function of changing the dynamic characteristic of the system. The differential link coefficient of the system can reflect the trend of signal change, and an effective early correction signal can be introduced into the system before the change of a deviation signal is too large, so that the response speed is accelerated, the adjustment time is shortened, the oscillation is eliminated, and the dynamic performance of the system is finally changed. Therefore, the differential K _d The choice of values has a great influence on the regulation dynamics. K _d If the value is too large, the braking will be advanced in the adjusting process, so that the adjusting time is too long; k _d If the value is too small, the braking will lag behind during the adjustment process, resulting in increased overshoot. Therefore, according to the experience of the actual process, the differential action is required to be increased at the initial stage of adjustment, so that smaller overshoot can be obtained and even overshoot is avoided; and in the middle stage, due to the regulation characteristic pair K _d The change in value is relatively sensitive, therefore, K _d The value should be suitably smaller and should remain fixed; then at the later stage of regulation, K _d The value should be reduced to reduce the braking effect of the controlled process and thus to compensate for K in the early part of the regulation process _d The longer the adjustment process, the larger the value. According to the established differential K _d To generate a differential K as shown in table five below _d Fuzzy rule table of (1):

table five: differential K _d Fuzzy rule table of

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Fifth embodiment

Based on the first embodiment, this embodiment also discloses a method for determining the PID initial parameter value according to the set temperature and the real-time temperature. Referring to fig. 6, fig. 6 is a schematic flow chart of a fifth exemplary embodiment of the temperature control method of the present application. In this embodiment, the step of determining the PID initial parameter value according to the set temperature and the real-time temperature may include:

step S21, performing parameter self-tuning on the set temperature and the real-time temperature to obtain a PID tuning parameter value;

s22, obtaining a self-tuning empirical value corresponding to the set temperature and the real-time temperature;

and S23, adjusting the PID setting parameter value according to the self-setting empirical value to obtain the PID initial parameter value.

In this embodiment, first, according to the set temperature and the real-time temperature of the controlled object, a parameter is self-tuned by a relay feedback self-tuning algorithm to obtain a PID tuning parameter value. More specifically, according to the set temperature and the real-time temperature, the critical gain and the critical oscillation period of the controlled object are obtained by calculating relevant parameters of the controller by using a critical proportionality method. As shown in equations 1 and 2, the following equations constructed based on the critical ratio method can be used to calculate the controller parameters in the relay type self-tuning process:

where d is the amplitude of the relay characteristic, A is the amplitude generated by the system, and T is the amplitude of the relay characteristic _u Is the critical oscillation period, K _u Is the critical gain, y _max And y _min Respectively the peak and trough values, t ₁ And t ₂ The time corresponding to the peak and the trough, respectively, and ω is the angular velocity.

And then, calculating by using a Z-N formula in combination with the critical gain and the critical oscillation period obtained by calculation, as shown in formulas 3 and 4, respectively calculating an integral time constant and a differential time constant of the controlled object:

T _i ＝0.5T _u (3)

T _d ＝0.125T _u (4)

wherein, T _i Is the integration time constant, T _d Is the differential time constant, T _u Is the critical oscillation period.

Then, according to K _p 0＝0.6K _u ，K _i 0＝K _p 0/T _i ，K _d 0＝K _p 0*T _d Calculating to obtain PID setting parameter value, wherein the PIDThe setting parameter values include a proportional setting parameter value K _p 0. Integral setting parameter value K _i 0 and a differential tuning parameter value K _d 0。

Then, self-tuning empirical values corresponding to the set temperature and the real-time temperature are obtained, wherein the self-tuning empirical values can be obtained according to expert experience.

Finally, the PID tuning parameter value is adjusted according to the obtained self-tuning empirical value, as shown in formulas 5, 6 and 7, the final PID initial parameter value is obtained, and the specific formula is as follows:

K _p 0＝0.5K _u

(5)

K _d 0＝0.75K _u T _u

(7)

wherein, K _p 0 is the proportional initial parameter value, K _i 0 is the value of the integrated initial parameter, K _d 0 is the value of the differential initial parameter, T _u Is the critical oscillation period, K _u Is the critical gain.

Compared with the prior art, the embodiment obtains the PID setting parameter value by performing parameter self-setting on the set temperature and the real-time temperature; acquiring self-tuning empirical values corresponding to the set temperature and the real-time temperature; and adjusting the PID tuning parameter value according to the self-tuning empirical value to obtain the PID initial parameter value, so as to provide a parameter self-tuning function for the fuzzy self-adaptive control of the variable domain and improve the control effect of the control system.

Further, the embodiment also discloses a method for determining the PID target parameter value according to the PID initial parameter value and the PID parameter variation value. In this embodiment, the step of determining the PID target parameter value according to the PID initial parameter value and the PID parameter variation value may include:

step S51, determining PID control parameter values according to the PID initial parameter values and the PID parameter change values;

and S52, correcting the PID control parameter value to obtain a PID target parameter value.

In this embodiment, first, according to a PID initial parameter value obtained after parameter self-tuning and the PID parameter variation value, the following calculation methods of formulas 8, 9 and 10 are used to determine a PID control parameter value, where the specific formulas are as follows:

K _p ＝K _p 0+△K _p 0 (8)

K _i ＝K _i 0+△K _i 0 (9)

K _d ＝K _d 0+△K _d 0 (10)

wherein the PID target parameter value comprises a proportional control parameter K _p Integral control parameter K _i And a differential control parameter K _d ；K _p 0 is the proportional initial parameter value, K _i 0 is the value of the integrated initial parameter, K _d 0 is the differential initial parameter value; delta K _p 0 is a change value of a proportional parameter,. DELTA.K _i 0 is the variation value of the integral parameter,. DELTA.K _d 0 is a differential parameter variation value.

And then, correcting the obtained PID control parameter value to obtain a final PID target parameter value. And finally, controlling the temperature of the controlled object according to the PID target parameter value.

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic discourse domain is determined according to the information such as the temperature rise speed, the mapping relation table is established according to the basic discourse domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as the different temperature rise speeds, and fuzzy self-adaptive control of the variable discourse domain is achieved. Through the fuzzy adaptive control of the variable universe of discourse, the basic universe of discourse and the fuzzy universe of discourse are dynamically adjusted to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified; meanwhile, a parameter self-tuning function is provided, and the control effect of the control system is improved. Based on the scheme of the application, the problems that the robustness and the self-adaptability are poor and the parameter self-tuning cannot be realized in the temperature control method are solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Sixth embodiment

As shown in fig. 7, fig. 7 is a schematic flowchart of a variable-domain fuzzy adaptive PID control process based on self-tuning according to an embodiment of the temperature control method of the present application. In this embodiment, the adaptive fuzzy PID control process based on the self-tuning variable discourse domain may include:

firstly, a self-tuning algorithm is adopted, and expert experience is combined to obtain PID initial parameter values, wherein the method specifically comprises the following steps: acquiring the set temperature, the real-time temperature and the heating speed of a controlled object; determining a temperature deviation variable and a PID initial parameter value of the controlled object according to the set temperature and the real-time temperature, wherein the step of determining the PID initial parameter value according to the set temperature and the real-time temperature may include: performing parameter self-tuning on the set temperature and the real-time temperature to obtain a PID tuning parameter value; acquiring self-tuning empirical values corresponding to the set temperature and the real-time temperature; and adjusting the PID setting parameter value according to the self-setting empirical value to obtain the PID initial parameter value.

And then, determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain. Specifically, based on a domain-variable technology, determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; further, according to different heating rates and other information obtained under different working conditions and different moments, the basic discourse domain of the temperature deviation variable is determined again, and the mapping relation table is dynamically adjusted and updated according to the basic discourse domain.

And then, based on a fuzzy self-adaptive algorithm, determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table.

Then, determining a PID target parameter value according to the PID initial parameter value and the PID parameter variation value, specifically including: determining PID control parameter values according to the PID initial parameter values and the PID parameter change values; and correcting the PID control parameter value to obtain a PID target parameter value.

And finally, outputting the obtained corrected PID target parameter value, and controlling the temperature of the controlled object according to the PID target parameter value.

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic discourse domain is determined according to the information such as the temperature rise speed, the mapping relation table is established according to the basic discourse domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as the different temperature rise speeds, and fuzzy self-adaptive control of the variable discourse domain is achieved. Through the fuzzy adaptive control of the variable universe of discourse, the basic universe of discourse and the fuzzy universe of discourse are dynamically adjusted to meet different requirements of different working conditions and different moments on PID parameters, so that a control system has strong robustness and adaptability, a controller can meet the control process of different states, and the debugging process is simplified; meanwhile, a parameter self-tuning function is provided, and the control effect of the control system is improved. Based on the scheme, the problems that the robustness and the self-adaptability are poor and the parameter self-tuning cannot be realized in the temperature control method are solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Seventh embodiment

As shown in fig. 8, fig. 8 is a schematic diagram of a temperature control system according to an embodiment of the temperature control method of the present application. In this embodiment, the composition of the temperature control system may include:

the self-tuning module is used for acquiring the set temperature, the real-time temperature and the heating speed of the controlled object; and determining the temperature deviation variable and the PID initial parameter value of the controlled object according to the set temperature and the real-time temperature.

The fuzzy self-adapting module is used for determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and specifically comprising: determining PID control parameter values according to the PID initial parameter values and the PID parameter change values; and correcting the PID control parameter value to obtain a PID target parameter value.

And the PID control module is used for acquiring and outputting the corrected PID target parameter value.

And the output control module is used for controlling the temperature of the controlled object according to the output PID target parameter value.

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. Through the fuzzy adaptive control of the variable universe of discourse, the basic universe of discourse and the fuzzy universe of discourse are dynamically adjusted to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified; meanwhile, a parameter self-tuning function is provided, and the control effect of the control system is improved. Based on the scheme, the problems that the robustness and the self-adaptability are poor and the parameter self-tuning cannot be realized in the temperature control method are solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Eighth embodiment

As shown in fig. 9, fig. 9 is a schematic structural diagram of a temperature control system according to an embodiment of the temperature control method of the present application. In this embodiment, the structure of the temperature control system may include:

input (Input): and the control device is used for inputting the acquired set temperature, real-time temperature and heating speed of the controlled object.

A temperature sensor: the real-time temperature acquisition device is used for acquiring the real-time temperature of a controlled object and transmitting the real-time temperature to the input of the temperature control system.

PID initial value setting: for determining a PID initial parameter value (K) of the controlled object from the set temperature and the real-time temperature _p 0、K _i 0 and K _d 0) (ii) a And transmitting the obtained PID initial parameter value to a PID controller.

Derivative module (de/dt): the temperature deviation variable of the controlled object is determined according to the set temperature and the real-time temperature, wherein the temperature deviation variable comprises a temperature deviation change value and a temperature deviation change rate; the derivative is carried out on the temperature deviation change value to obtain the temperature deviation change rate; and transmitting the obtained temperature deviation variable to a fuzzy controller.

A fuzzy controller: the system is used for determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed and establishing a mapping relation table according to the basic domain of discourse; determining PID parameter change value (delta K) according to the mapping relation table and a preset fuzzy rule table _p 、△K _i And Δ K _d ) (ii) a And then, transmitting the obtained PID parameter variation value to a PID controller.

A PID controller: the PID parameter value determining module is used for determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value; and transmitting the obtained PID target parameter value to an actuating mechanism of a temperature control system.

An executing mechanism: and the temperature control module is used for controlling the temperature of the controlled object according to the PID target parameter value.

The controlled object is as follows: a temperature information output object and a temperature control object in a temperature control system.

Output (Output): used for outputting the temperature information of the controlled object.

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. Through the fuzzy adaptive control of the variable universe of discourse, the basic universe of discourse and the fuzzy universe of discourse are dynamically adjusted to meet different requirements of different working conditions and different moments on PID parameters, so that a control system has strong robustness and adaptability, a controller can meet the control process of different states, and the debugging process is simplified; meanwhile, a parameter self-tuning function is provided, and the control effect of the control system is improved. Based on the scheme of the application, the problems that the robustness and the self-adaptability are poor and the parameter self-tuning cannot be realized in the temperature control method are solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

Ninth embodiment

As shown in fig. 10, fig. 10 is a schematic flow chart of a fuzzy adaptive algorithm process according to an embodiment of the temperature control method of the present application. In this embodiment, the fuzzy adaptive algorithm process may include:

firstly, acquiring a set temperature, a real-time temperature and a heating speed of a controlled object; and determining the temperature deviation variable and the PID initial parameter value of the controlled object according to the set temperature and the real-time temperature.

Then, based on a dynamic domain-of-discourse technology, determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rising speed.

Then, a mapping relationship table is established according to the basic discourse domain, wherein the mapping relationship table includes a first mapping relationship table and a second mapping relationship table, which may specifically include: determining a first fuzzy domain and a first quantization scale factor of the temperature deviation change value according to the first fundamental domain; and determining a second ambiguity domain and a second quantization scale factor of the temperature deviation change rate according to the second fundamental domain; establishing the first mapping relation table according to the first fundamental domain, the first fuzzy domain, the first quantization scale factor, the second fundamental domain, the second fuzzy domain and the second quantization scale factor; and determining the second mapping relation table according to the first mapping relation table.

Then, a fuzzy rule table is designed and generated.

Then, performing fuzzy inference according to the mapping relationship table and a preset fuzzy rule table, and determining and outputting a fuzzy set, that is, a PID parameter variation value, which may specifically include: quantizing the temperature deviation change value and the temperature deviation change rate according to the mapping relation table to obtain a first quantization result of the temperature deviation change value and a second quantization result of the temperature deviation change rate; determining a first membership degree of the temperature deviation variation value according to the first quantization result; determining a second membership degree of the temperature deviation change rate according to the second quantification result; and determining the PID parameter variation value according to the first membership degree, the second membership degree and the preset fuzzy rule table.

Then, carrying out deblurring processing on the fuzzy set, namely the PID parameter change value to obtain a clarified PID parameter change value after deblurring; and outputting the PID parameter change value after the ambiguity resolution, namely the accurate quantity.

Then, determining a PID target parameter value according to the PID initial parameter value and the PID parameter variation value, which may specifically include: determining PID control parameter values according to the PID initial parameter values and the PID parameter change values; and carrying out offset correction on the PID control parameter value to obtain a PID target parameter value.

And finally, controlling the temperature of the controlled object according to the corrected PID target parameter value.

According to the scheme, the set temperature, the real-time temperature and the heating speed of the controlled object are obtained; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic domain of discourse of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic domain of discourse; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic discourse domain is determined according to the information such as the temperature rise speed, the mapping relation table is established according to the basic discourse domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as the different temperature rise speeds, and fuzzy self-adaptive control of the variable discourse domain is achieved. Through the fuzzy adaptive control of the variable universe of discourse, the basic universe of discourse and the fuzzy universe of discourse are dynamically adjusted to meet different requirements of different working conditions and different moments on PID parameters, so that a control system has strong robustness and adaptability, a controller can meet the control process of different states, and the debugging process is simplified; meanwhile, a parameter self-tuning function is provided, and the control effect of the control system is improved. Based on the scheme, the problems that the robustness and the self-adaptability are poor and the parameter self-tuning cannot be realized in the temperature control method are solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

In addition, this application embodiment also provides a temperature control device, temperature control device includes:

For the principle and implementation process of implementing temperature control in this embodiment, please refer to the above embodiments, which are not described in detail herein.

In addition, an apparatus is further provided in an embodiment of the present application, where the apparatus includes a memory, a processor, and a temperature control program stored in the memory and executable on the processor, and the temperature control program implements the steps of the temperature control method described above when executed by the processor.

Since the temperature control program is executed by the processor, all technical solutions of all the foregoing embodiments are adopted, so that at least all the beneficial effects brought by all the technical solutions of all the foregoing embodiments are achieved, and details are not repeated herein.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a temperature control program is stored, and the temperature control program, when executed by a processor, implements the steps of the temperature control method as described above.

Compared with the prior art, the temperature control method, the temperature control device, the temperature control equipment and the storage medium provided by the embodiment of the application acquire the set temperature, the real-time temperature and the heating speed of the controlled object; determining a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object according to the set temperature and the real-time temperature; determining a basic discourse domain of the temperature deviation variable according to the PID initial parameter value and the temperature rise speed, and establishing a mapping relation table according to the basic discourse domain; determining a PID parameter change value according to the mapping relation table and a preset fuzzy rule table; and determining a PID target parameter value according to the PID initial parameter value and the PID parameter change value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the scheme, the basic domain is determined according to information such as the temperature rise speed, the mapping relation table is established according to the basic domain, meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to different information such as the temperature rise speed, and fuzzy self-adaptive control of the variable domain is achieved. The basic discourse domain and the fuzzy discourse domain are dynamically adjusted through the fuzzy adaptive control of the variable discourse domain so as to meet different requirements of different working conditions and different moments on PID parameters, so that the control system has strong robustness and adaptability, the controller can meet the control process of different states, and the debugging process is simplified. Based on the scheme, the problem that the robustness and the self-adaptability of the temperature control method are poor is solved, the adaptability of the working condition is enhanced, and the temperature control effect is improved.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as above, and includes several instructions for causing an apparatus to execute the method of each embodiment of the present application.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. A temperature control method, characterized in that the temperature control method comprises:

2. The method according to claim 1, wherein the temperature deviation variable comprises a temperature deviation change value and a temperature deviation change rate, the fundamental domains comprise a first fundamental domain corresponding to the temperature deviation change value and a second fundamental domain corresponding to the temperature deviation change rate, and the step of determining the fundamental domain of the temperature deviation variable based on the PID initial parameter value and the temperature rise rate comprises:

3. The temperature control method according to claim 2, wherein the obtaining of the first deviation expansion coefficient corresponding to the temperature deviation change value; and the step of obtaining a second deviation expansion coefficient corresponding to the temperature deviation change rate comprises the following steps:

acquiring working condition information of the controlled object;

4. The method of temperature control according to claim 2, wherein said mapping table comprises a first mapping table and a second mapping table, and said step of building a mapping table based on said fundamental domain of discourse comprises:

5. The temperature control method according to claim 2, wherein the step of determining the PID parameter variation value according to the mapping relationship table and a preset fuzzy rule table comprises:

6. The method of claim 1, wherein the step of determining the PID initial parameter value based on the set temperature and the real-time temperature comprises:

7. The temperature control method of claim 1, wherein the step of determining a PID target parameter value based on the PID initial parameter value and the PID parameter variation value comprises:

8. A temperature control apparatus, characterized in that the temperature control apparatus comprises:

9. An apparatus comprising a memory, a processor, and a temperature control program stored on the memory and executable on the processor, the temperature control program when executed by the processor implementing the steps of the temperature control method of any one of claims 1-7.

10. A computer-readable storage medium, having stored thereon a temperature control program which, when executed by a processor, implements the steps of the temperature control method according to any one of claims 1-7.