CN115437425B

CN115437425B - Temperature control method, device, equipment and storage medium

Info

Publication number: CN115437425B
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: 2024-06-21
Anticipated expiration: 2042-09-28
Also published as: CN115437425A

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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation value, and controlling the temperature of the controlled object according to the PID target parameter value. The application solves the problems of poor robustness and self-adaptability of the temperature control method, enhances the working condition adaptability and improves the temperature control effect.

Description

Temperature control method, device, equipment and storage medium

Technical Field

The present application relates to the field of temperature control, and in particular, to a temperature control method, apparatus, device, and storage medium.

Background

In the field of industrial control, especially in industries related to high temperature, such as lithium battery monomer furnaces, foaming molding machines, vulcanizing presses, molding machines and the like, the requirement on temperature control is high, so that a set of safe and reliable temperature control system is particularly important. The existing temperature control system usually adopts a PID control mode to realize accurate control of temperature, but most of the temperature control systems have the characteristics of large hysteresis and large inertia, and meanwhile have the characteristics of time variation, uncertainty and nonlinearity, so that the conventional PID control method with good control effect on a linear steady system often has the defects of low temperature rising speed, large overshoot and the like on the control of the nonlinear and time-varying system, and the ideal control effect cannot be realized because of the inability to self-adjust parameters on line.

The fuzzy self-adaptive PID control is a control method combining conventional PID control and fuzzy control, and can dynamically adjust the parameters of PID to realize temperature control by utilizing a fuzzy control rule based on a basic domain and a fuzzy domain 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 performance. However, the general fuzzy self-adaptive PID control uses a fixed basic domain and a fuzzy domain, so that different requirements of different working conditions and different moments on PID parameters are difficult to meet, the robustness and the self-adaptability of a temperature control system are poor, and the control effect on the temperature is poor.

Therefore, there is a need for a temperature control scheme that enhances operating mode adaptability.

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 self-adaptability of the temperature control method, enhance working condition adaptability and improve temperature control effect.

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

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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument;

determining PID parameter variation values 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 variation value, and controlling the temperature of the controlled object according to the PID target parameter value.

Optionally, the temperature deviation variable includes a temperature deviation change value and a temperature deviation change rate, the basic domain includes 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 rising rate includes:

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

Determining the first basic discourse domain according to the first deviation expansion coefficient and the PID initial parameter value; and determining the second basic argument according to the second deviation expansion coefficient and the heating rate.

Optionally, the first deviation expansion coefficient corresponding to the temperature deviation change value is obtained; 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 relation table includes a first mapping relation table and a second mapping relation table, and the step of building the mapping relation table according to the basic domain includes:

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

establishing the first mapping relation table according to the first basic domain, the first fuzzy domain, the first quantization scale factor, the second basic 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 the PID parameter variation value according to the mapping relationship table and the 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 change value according to the first quantification result; and determining a second membership of the temperature deviation rate of change according to the second quantization result;

And determining the PID parameter change 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:

parameter self-tuning is carried out on the set temperature and the real-time temperature, and PID tuning parameter values are obtained;

Acquiring a self-tuning experience value 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.

Optionally, the step of determining a PID target parameter value from the PID initial parameter value and the PID parameter variation value comprises:

Determining a PID control parameter value according to the PID initial parameter value and the PID parameter variation value;

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

The embodiment of the application also provides a temperature control device, which comprises:

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 construction module is used for determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic domain;

The change value determining module is used for determining PID parameter change values 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 variation value and controlling the temperature of the controlled object according to the PID target parameter value.

The embodiment of the application also provides equipment, which comprises a memory, a processor and a temperature control program stored on the memory and capable of running on the processor, wherein the temperature control program realizes the steps of the temperature control method when being executed by the processor.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a temperature control program, and the temperature control program realizes the steps of the temperature control method when being executed by a processor.

The temperature control method, the temperature control device, the temperature control equipment and the storage medium provided by the embodiment of the application are characterized in that the set temperature, the real-time temperature and the temperature rising 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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation 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 the information such as the temperature rising speed, the mapping relation table is built according to the basic domain, and meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as different temperature rising speeds, so that fuzzy self-adaptive control of the variable domain is realized. The basic domain and the fuzzy domain are dynamically adjusted through fuzzy self-adaptive control of the variable 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 self-adaptability, and the controller can meet control processes of different states, and the debugging process is simplified. Based on the scheme of the application, the problem of poor robustness and self-adaptability of the temperature control method is solved, the adaptability of working conditions is enhanced, and the temperature control effect is improved.

Drawings

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

FIG. 5 is a 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 variable domain fuzzy adaptive PID control flow based on self-tuning 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 present application;

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

fig. 10 is a flow chart of a fuzzy adaptive algorithm according to an embodiment of the temperature control method of the present application.

The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The main solutions of the embodiments of the present application are: 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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation value, and controlling the temperature of the controlled object according to the PID target parameter value. According to the embodiment of the application, the basic domain is determined according to the information such as the temperature rising speed, the mapping relation table is established according to the basic domain, and meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as different temperature rising speeds, so that the fuzzy self-adaptive control of the variable domain is realized. The basic domain and the fuzzy domain are dynamically adjusted through fuzzy self-adaptive control of the variable 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 self-adaptability, and the controller can meet control processes of different states, and the debugging process is simplified. Based on the scheme of the application, the problem of poor robustness and self-adaptability of the temperature control method is solved, the adaptability of working conditions is enhanced, and the temperature control effect is improved.

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

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

The memory 130 stores an operating system and a temperature control program, and the temperature control device may store the acquired set temperature, real-time temperature and heating speed of the controlled object, a temperature deviation variable and a proportional-integral-derivative PID initial parameter value of the controlled object determined according to the set temperature and the real-time temperature, a basic argument of the temperature deviation variable determined according to the PID initial parameter value and the heating speed, a mapping relation table established according to the basic argument, a preset fuzzy rule table, a PID parameter variation value determined according to the mapping relation table and the preset fuzzy rule table, and a 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 performs the steps of:

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

acquiring working condition information of the controlled object;

According to the embodiment, the set temperature, the real-time temperature and the temperature rising 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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation 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 the information such as the temperature rising speed, the mapping relation table is built according to the basic domain, and meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as different temperature rising speeds, so that fuzzy self-adaptive control of the variable domain is realized. The basic domain and the fuzzy domain are dynamically adjusted through fuzzy self-adaptive control of the variable 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 self-adaptability, and the controller can meet control processes of different states, and the debugging process is simplified. Based on the scheme of the application, the problem of poor robustness and self-adaptability of the temperature control method is solved, the adaptability of working conditions is enhanced, and the temperature control effect is improved.

Based on the above device architecture, but not limited to the above architecture, the method embodiments 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 subject of the method can be a temperature control device or a temperature control system or device. The embodiment is exemplified by a temperature control device, and the temperature control method implemented on the temperature control device includes:

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 speed of a controlled object are obtained, wherein the set temperature refers to a preset temperature of the controlled object; the real-time temperature is the real-time temperature of the controlled object through detection and acquisition; the temperature rise rate refers to the amount of change in temperature per unit time. The real-time temperature acquiring mode may be to acquire the temperature acquired in real time by the temperature sensor.

And step 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 obtained set temperature and the real-time temperature, 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. Wherein, the temperature deviation variable refers to a variable related to the temperature change trend of the controlled object, such as the speed of heating or cooling. The PID is a control loop feedback mechanism widely applied to industrial control systems, and mainly comprises proportional control (P) for controlling the output and input proportional relation of a controlled object, integral control (I) for eliminating steady-state errors and differential control (D) for weakening overshoot and increasing the inertial response speed. The PID initial parameter value is the initial value of the determined controller three parameters, namely a proportional parameter, an integral parameter and a differential parameter.

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

Specifically, according to the PID initial parameter value and the heating speed, determining a basic domain of the temperature deviation variable according to a set correlation rule, and establishing a mapping relation table according to the basic domain, wherein the mapping relation table is a rule table for forming a corresponding relation between the determined basic domain and the determined fuzzy domain according to a certain corresponding rule and is used for mapping a specific value of the variable in the basic domain into a range parameter of the variable in the fuzzy domain. Further, according to the information such as different heating speeds and the like obtained under different working conditions and at different moments, the basic domain of the temperature deviation variable is redetermined, and the mapping relation table is dynamically adjusted and updated according to the basic domain.

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

Specifically, according to the established mapping relation table, mapping the temperature deviation variable into a range parameter of the temperature deviation variable in a fuzzy theory domain, and determining a PID parameter change value 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 variation 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 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 variable may include a temperature deviation change value and a temperature deviation change rate, where the temperature deviation change value E is obtained by differentiating the acquired set temperature (T ₀) and real-time temperature (T ₁), i.e., e=t ₀-T₁; the temperature deviation change rate EC is obtained by deriving the temperature deviation change value E, i.e., ec=de/dt=e (t) -E (t-1).

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

step S31, a first deviation expansion coefficient corresponding to the temperature deviation change value is obtained; obtaining 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 basic argument according to the second deviation expansion coefficient and the heating rate.

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

Further, the embodiment also discloses a method for obtaining the first deviation expansion coefficient corresponding to the temperature deviation change value and obtaining the second deviation expansion coefficient corresponding to the temperature deviation change rate. Step S31, obtaining a first deviation expansion coefficient corresponding to the temperature deviation variation value; and obtaining the 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 working condition information.

Specifically, the working condition information of the controlled object is obtained, wherein the working condition information comprises, but is not limited to, expert experience values corresponding to temperature deviation change values, expert experience values corresponding to temperature deviation change rates 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 discussion 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; setting the output of the proportion as K _p 0*E according to the PID initial parameter value, namely the proportion initial parameter value K _p 0; at maximum proportional output, the calculation is performed according to e= (100/K _p 0) ×α, where 100 is the maximum proportional output. When the initial proportional parameter value K _p =5 under one working condition, e= (100/5) ×1.2=24, and the basic argument of the temperature deviation change value E is (-24, 24); when the ratio initial parameter value K _p 0 =20 under another working condition, e= (100/20) ×1.2=6, the basic argument of the temperature deviation value E is (-6, 6).

The second basic 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 selecting a maximum heating rate S (wherein the unit is a speed per t and t is a sampling period) of each sampling period according to the acquired heating rate, and then calculating according to EC=S. Under one working condition, the maximum heating rate S=0.2 ℃/t in the parameter self-tuning process, so EC=Sβ=0.2×2=0.4, and the basic theory of the deviation change rate EC of the current working condition is (-0.4, 0.4); in another working condition, the maximum temperature rising speed S=4 ℃/t in the parameter self-tuning process, so EC=S=β= 4*2 =8, and the basic theory 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, the present embodiment further discloses a method for establishing a mapping relationship table according to the basic domain. Referring to fig. 4, fig. 4 is a schematic flow chart of a third exemplary embodiment of the temperature control method according to the present application. In this embodiment, the mapping table may include a first mapping table and a second mapping table, and the step of establishing the mapping table according to the basic argument may include:

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

Specifically, a first fuzzy theory and a first quantization scale factor of the temperature deviation change value are determined according to a first basic theory, wherein the first basic theory is determined as [ E _min,E_max ], 7 linguistic variables of negative big [ NB ], negative middle [ NM ], negative small [ NS ], zero [ ZO ], positive small [ PS ], middle [ PM ] and positive big [ PB ] are adopted as fuzzy subsets of the temperature deviation change value, the corresponding first fuzzy theory can be determined as [ -6,6], and the first quantization scale factor can be determined as 6/E _max. Then, a second fuzzy theory and a second quantization scale factor of the temperature deviation change rate are determined according to a second basic theory, wherein the second basic theory is determined as [ EC _min,EC_max ], 7 linguistic variables of negative big [ NB ], negative middle [ NM ], negative small [ NS ], zero [ ZO ], positive small [ PS ], middle [ PM ], positive big [ PB ] are adopted as fuzzy subsets of the temperature deviation change rate, the corresponding second fuzzy theory can be determined as [ -6,6], and the second quantization scale factor can be determined as 6/EC _max.

And step S34, the first mapping relation table is built according to the first basic domain, the first fuzzy domain, the first quantization scale factor, the second basic domain, the second fuzzy domain and the second quantization scale factor.

Specifically, according to the first basic domain, the first fuzzy domain, the first quantization scale factor, the second basic domain, the second fuzzy domain and the second quantization scale factor, a first mapping relation table shown in the following table one is established:

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 relation table of PID parameters, namely a second mapping relation table, is determined according to the established first mapping relation table, and the second mapping relation table is shown as the following table II:

and (II) table: second mapping relation table

Fourth embodiment

Based on the first, second and third embodiments, the present embodiment further discloses a method for determining the PID parameter variation value according to the mapping relationship table and the 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, establishing a mapping relationship table according to the basic domain may include:

And S41, quantifying the temperature deviation change value and the temperature deviation change rate according to the mapping relation table to obtain a first quantification result of the temperature deviation change value and a second quantification 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 respectively, 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 change value according to the first quantification result; and determining a second membership of the temperature deviation change rate according to the second quantification result.

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

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

Specifically, the PID parameter change value is determined by comparing the determined first membership degree, the 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 membership degree, the second membership degree, and the preset fuzzy rule table, the step of designing and generating the fuzzy rule table may further include:

And generating a fuzzy rule table of K _p according to the formulated fuzzy rule of the proportion K _p.

Specifically, a fuzzy rule table of the proportion K _p is generated according to the formulated fuzzy rule of the proportion K _p. Wherein, the fuzzy rule of the proportion K _p is as follows: the selection of the proportion K _p determines the response speed of the system; According to the functions of the three parameters of the proportion K _p, the integral K _i and the differential K _d at different moments and the relation between the three parameters, the response speed can be improved by increasing the K _p, Reducing steady state deviation, but too large a value of K _p can produce larger overshoot and even make the system unstable; Reducing K _p can reduce overshoot and improve stability, but too small a value of K _p can slow down response speed and prolong adjustment time; therefore, the initial stage of adjustment should be appropriately taken to be larger in K _p value to improve the response speed; in the middle adjustment period, the K _p value takes a smaller value so that the system has smaller overshoot and a certain response speed is ensured; and in the later stage of the adjusting process, the K _p value is adjusted to a larger value to reduce static difference and improve control precision. Based on the formulated fuzzy rule of the proportion K _p, generating a fuzzy rule table of the proportion K _p shown in the following table III:

Table three: fuzzy rule table of proportion K _p

Then, a fuzzy rule table of K _i is generated according to the formulated fuzzy rule of integral K _i.

Specifically, a fuzzy rule table of the integral K _i is generated according to the formulated fuzzy rule of the integral K _i. The fuzzy rule of the integral K _i is as follows: the integral control is mainly used for eliminating steady-state deviation of the system, and for some reasons (such as saturation nonlinearity, etc.), the integral process may generate integral saturation in the initial stage of the adjustment process, so as to cause a large overshoot of the adjustment process, therefore, in the initial stage of the adjustment process, in order to prevent the integral saturation, the integral effect should be weaker, and even zero can be taken; in the middle of the regulation, the integral action of the regulator should be moderate in order to avoid affecting the stability; finally, at the later stage of the adjustment process, the integration should be enhanced to reduce the adjustment dead space. Generating a fuzzy rule table of the integral K _i shown in the following table four according to the formulated fuzzy rule of the integral K _i:

table four: fuzzy rule table integrating K _i

Finally, a fuzzy rule table of K _d is generated according to the formulated fuzzy rule of the differential K _d.

Specifically, a fuzzy rule table of the derivative K _d is generated according to the formulated fuzzy rule of the derivative K _d. Wherein, the fuzzy rule of the derivative K _d is as follows: the adjustment of differential control is mainly introduced for large inertia processes, and the effect of the differential link coefficients is to change the dynamic characteristics of the system. The differential link coefficient of the system can reflect the trend of signal change, and can introduce an effective early correction signal into the system before the variation of the deviation signal is too large, thereby accelerating the response speed, reducing the adjustment time, eliminating the oscillation and finally changing the dynamic performance of the system. Therefore, the selection of the differential K _d value greatly affects the tuning dynamics. When the value of K _d is too large, the brake is advanced in the adjusting process, so that the adjusting time is too long; too small a value of K _d, the braking will fall back during adjustment, resulting in increased overshoot. Therefore, according to practical process experience, in the initial stage of adjustment, the differential action should be increased, so that smaller overshoot can be obtained and even overshoot can be avoided; in the middle phase, however, since the regulation characteristic is relatively sensitive to changes in the value of K _d, the value of K _d should be suitably small and should remain constant; then, in the later stage of the adjustment, the value of K _d should be reduced to reduce the braking action of the controlled process, thereby compensating for the time extension of the adjustment process caused by the larger value of K _d in the initial stage of the adjustment process. Generating a fuzzy rule table of the differential K _d shown in the following table five according to the formulated fuzzy rule of the differential K _d:

Table five: fuzzy rule table of differentiation K _d

Fifth embodiment

Based on the first embodiment, the present 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 a temperature control method according to 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:

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

Step S22, obtaining self-tuning experience values corresponding to the set temperature and the real-time temperature;

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

In this embodiment, first, according to the set temperature of the controlled object and the real-time temperature, the parameter is self-set by a relay feedback self-setting algorithm, so as to obtain the PID setting parameter value. More specifically, according to the set temperature and the real-time temperature, calculating relevant parameters of the controller by using a critical proportionality method to obtain critical gain and critical oscillation period of the controlled object. As shown in formulas 1 and 2, the following formulas constructed based on the critical proportionality 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, T _u is the critical oscillation period, K _u is the critical gain, y _max and y _min are the peak and trough values, respectively, T ₁ and T ₂ are the times corresponding to the peak and trough, respectively, and ω is the angular velocity.

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

T_i＝0.5T_u (3)

T_d＝0.125T_u (4)

Where T _i is the integration time constant, T _d is the differentiation time constant, and T _u is the critical oscillation period.

Then, PID tuning parameter values are calculated according to K _p0＝0.6K_u,K_i0＝K_p0/T_i,K_d0＝K_p0*T_d, wherein the PID tuning parameter values include a proportional tuning parameter value K _p 0, an integral tuning parameter value K _i 0, and a differential tuning parameter value K _d 0.

And then, acquiring a self-tuning empirical value corresponding to the set temperature and the real-time temperature, wherein the self-tuning empirical value can be obtained according to expert experience.

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

K_p0＝0.5K_u (5)

K_d0＝0.75K_uT_u (7)

Where K _p is a proportional initial parameter value, K _i is an integral initial parameter value, K _d is a differential initial parameter value, T _u is a critical oscillation period, and K _u is a critical gain.

Compared with the prior art, the PID tuning parameter value is obtained by carrying out parameter self-tuning on the set temperature and the real-time temperature; acquiring a self-tuning experience value corresponding to the set temperature and the real-time temperature; and adjusting the PID tuning parameter value according to the self-tuning experience value to obtain the PID initial parameter value, providing a parameter self-tuning function for fuzzy self-adaptive control of a variable domain, and improving the control effect of a control system.

Further, the embodiment also discloses a method for determining a 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 a PID control parameter value according to the PID initial parameter value and the PID parameter variation value;

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

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

K_p＝K_p0+△K_p0 (8)

K_i＝K_i0+△K_i0 (9)

K_d＝K_d0+△K_d0 (10)

Wherein the PID target parameter values include a proportional control parameter K _p, an integral control parameter K _i, and a derivative control parameter K _d;K_p, where K _i 0 is an integral initial parameter value, and K _d 0 is a derivative initial parameter value; Δk _p 0 is a proportional parameter variation value, Δk _i 0 is an integral parameter variation value, and Δk _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 embodiment, the set temperature, the real-time temperature and the temperature rising 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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation 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 the information such as the temperature rising speed, the mapping relation table is built according to the basic domain, and meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as different temperature rising speeds, so that fuzzy self-adaptive control of the variable domain is realized. The basic domain and the fuzzy domain are dynamically adjusted through fuzzy self-adaptive control of the variable 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 self-adaptability, the controller can meet control processes 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 temperature control method has poor robustness and self-adaptability and cannot realize parameter self-tuning are solved, the adaptability of working conditions is enhanced, and the temperature control effect is improved.

Sixth embodiment

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

Firstly, a self-tuning algorithm is adopted, expert experience is combined, and PID initial parameter values are obtained, specifically comprising: 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: parameter self-tuning is carried out on the set temperature and the real-time temperature, and PID tuning parameter values are obtained; acquiring a self-tuning experience value 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.

And then, determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the temperature rising speed, and establishing a mapping relation table according to the basic domain. Specifically, based on a variable domain technology, determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic domain; further, according to the information such as different heating speeds and the like obtained under different working conditions and at different moments, the basic domain of the temperature deviation variable is redetermined, and the mapping relation table is dynamically adjusted and updated according to the basic 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.

And then, determining a PID target parameter value according to the PID initial parameter value and the PID parameter variation value, wherein the PID target parameter value specifically comprises: determining a PID control parameter value according to the PID initial parameter value and the PID parameter variation value; 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.

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 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 fuzzy self-adaptation module is used for determining a basic domain of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic domain; determining PID parameter variation values 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 variation value, specifically comprising: determining a PID control parameter value according to the PID initial parameter value and the PID parameter variation value; 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.

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): the method is used for inputting the acquired set temperature, real-time temperature and heating speed of the controlled object.

Temperature sensor: the system is used for collecting the real-time temperature of the controlled object and transmitting the real-time temperature to the input of the temperature control system.

Setting a PID initial value: the proportional-integral-derivative PID initial parameter values (K _p0、K_i and K _d 0) are used for determining the controlled object according to the set temperature and the real-time temperature; and transmitting the obtained PID initial parameter value to a PID controller.

Derivative module (de/dt): the temperature deviation variable is used for determining the temperature deviation variable of the controlled object 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; deriving the temperature deviation change value to obtain a temperature deviation change rate; and transmitting the obtained temperature deviation variable to a fuzzy controller.

And (3) a fuzzy controller: the basic domain is used for determining the temperature deviation variable according to the PID initial parameter value and the temperature rising speed, and a mapping relation table is established according to the basic domain; determining PID parameter variation values (delta K _p、△K_i and delta K _d) according to the mapping relation table and a preset fuzzy rule table; and then, the obtained PID parameter variation value is transmitted to a PID controller.

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 variation value; and transmitting the obtained PID target parameter value to an executing mechanism of a temperature control system.

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

Controlled object: a temperature information output object and a temperature control object in the temperature control system.

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

Ninth embodiment

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

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

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

Then, a mapping relation table is established according to the basic domain, wherein the mapping relation table comprises a first mapping relation table and a second mapping relation table, and the mapping relation table specifically comprises: determining a first fuzzy theory domain and a first quantization scale factor of the temperature deviation change value according to the first basic theory domain; and determining a second fuzzy theory and a second quantization scale factor of the temperature deviation change rate according to the second basic theory; establishing the first mapping relation table according to the first basic domain, the first fuzzy domain, the first quantization scale factor, the second basic 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, fuzzy reasoning is carried out according to the mapping relation table and the preset fuzzy rule table, and a fuzzy set, namely PID parameter change values, is determined and output, wherein the fuzzy set comprises the following specific steps: 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 change value according to the first quantification result; and determining a second membership of the temperature deviation rate of change according to the second quantization result; and determining the PID parameter change value according to the first membership degree, the second membership degree and the preset fuzzy rule table.

Then, performing defuzzification processing on the fuzzy set, namely the PID parameter change value, so as to obtain a clear PID parameter change value after defuzzification; and outputting the variation value of the PID parameter after the deblurring, namely the accurate quantity.

Next, determining a PID target parameter value according to the PID initial parameter value and the PID parameter variation value may specifically include: determining a PID control parameter value according to the PID initial parameter value and the PID parameter variation value; and performing 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.

In addition, an embodiment of the present application further provides a temperature control device, where the temperature control device includes:

The principle and implementation process of the temperature control in this embodiment are referred to the above embodiments, and will not be described in detail herein.

In addition, the embodiment of the application also provides equipment, which comprises a memory, a processor and a temperature control program stored in the memory and capable of running on the processor, wherein the temperature control program realizes the steps of the temperature control method when being executed by the processor.

Because the temperature control program is executed by the processor and adopts all the technical schemes of all the embodiments, the temperature control program at least has all the beneficial effects brought by all the technical schemes of all the embodiments and is not described in detail herein.

In addition, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a temperature control program, and the temperature control program realizes the steps of the temperature control method when being executed by a processor.

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 are characterized in that the set temperature, the real-time temperature and the temperature rising 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 argument of the temperature deviation variable according to the PID initial parameter value and the heating speed, and establishing a mapping relation table according to the basic argument; determining PID parameter variation values 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 variation 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 the information such as the temperature rising speed, the mapping relation table is built according to the basic domain, and meanwhile, the mapping relation table can be dynamically adjusted and updated in real time according to the information such as different temperature rising speeds, so that fuzzy self-adaptive control of the variable domain is realized. The basic domain and the fuzzy domain are dynamically adjusted through fuzzy self-adaptive control of the variable 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 self-adaptability, and the controller can meet control processes of different states, and the debugging process is simplified. Based on the scheme of the application, the problem of poor robustness and self-adaptability of the temperature control method is solved, the adaptability of working conditions 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as above, comprising several instructions for causing an apparatus to perform the method of each embodiment of the present application.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims

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

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 temperature deviation variable comprises a temperature deviation change value and a temperature deviation change rate;

acquiring working condition information of the controlled object;

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;

Determining a first basic discourse domain according to the first deviation expansion coefficient and the PID initial parameter value; determining a second basic discourse domain according to the second deviation expansion coefficient and the temperature rising speed;

the mapping relation table comprises a first mapping relation table and a second mapping relation table;

Determining a second mapping relation table according to the first mapping relation table;

2. The method according to claim 1, wherein the step of determining the PID parameter variation value according to the map table and the preset fuzzy rule table comprises:

3. The method of temperature control according to claim 1, wherein the step of determining the PID initial parameter value from the set temperature and the real-time temperature includes:

4. The temperature control method according to claim 1, wherein the step of determining a PID target parameter value from the PID initial parameter value and the PID parameter variation value comprises:

5. A temperature control device, characterized in that the temperature control device comprises:

The temperature calculation module is used for 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 temperature deviation variable comprises a temperature deviation change value and a temperature deviation change rate;

The relation table construction module is used for acquiring the working condition information of the controlled object; the method is also used for 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; the method is also used for determining a first basic discourse domain according to the first deviation expansion coefficient and the PID initial parameter value; determining a second basic discourse domain according to the second deviation expansion coefficient and the temperature rising speed; the mapping relation table comprises a first mapping relation table and a second mapping relation table; the first fuzzy theory domain and the first quantitative scale factor are also used for determining the temperature deviation change value according to the first basic theory domain; and determining a second fuzzy theory and a second quantization scale factor of the temperature deviation change rate according to the second basic theory; establishing the first mapping relation table according to the first basic domain, the first fuzzy domain, the first quantization scale factor, the second basic domain, the second fuzzy domain and the second quantization scale factor; determining a second mapping relation table according to the first mapping relation table;

6. An apparatus comprising a memory, a processor, and a temperature control program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the temperature control method of any of claims 1-4.

7. A computer-readable storage medium, characterized in that the computer-readable storage medium has 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 1-4.