CN115993778A - Fuzzy control method and device for temperature control system of high-low temperature test chamber - Google Patents
Fuzzy control method and device for temperature control system of high-low temperature test chamber Download PDFInfo
- Publication number
- CN115993778A CN115993778A CN202211505855.0A CN202211505855A CN115993778A CN 115993778 A CN115993778 A CN 115993778A CN 202211505855 A CN202211505855 A CN 202211505855A CN 115993778 A CN115993778 A CN 115993778A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- error
- temperature
- low temperature
- test chamber
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
The application provides a fuzzy control method of a temperature control system of a high-low temperature test box, which comprises the following steps: collecting feedback temperature information in a high-low temperature test chamber; comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate; respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table; and correcting correction parameters comprising a proportional coefficient, a time integral constant and a time differential constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters. The fuzzy PID control is realized in the PLC, the action of the heater is controlled according to the output value of the PID, and the collected feedback temperature is compared with the set value so as to more accurately control the temperature.
Description
Technical Field
The application relates to the field of automatic control, in particular to a fuzzy control method of a temperature control system of a high-low temperature test box. The application also relates to a fuzzy control device of the temperature control system of the high-low temperature test box.
Background
The high-low temperature test box plays an irreplaceable role in links such as product delivery test, product design and the like, the temperature test is one of important indexes of the environmental test equipment, the important parameters for guaranteeing the safety of the temperature and humidity test equipment, and the control of the temperature control system is also the guarantee of experiments. The control system is a key part of high-low temperature environment test equipment, directly influences test indexes in the aspects of temperature precision, temperature fluctuation degree and the like, and relates to test effects, stability of equipment operation and convenience of operation.
At present, the market demand for nonstandard high-low temperature test boxes is larger and larger, and the test boxes can be customized according to specific test objects so as to meet the diversity of users on high control precision and functions. The high-low temperature experimental device mainly comprises a condensing evaporator, an exhaust fan, an electromagnetic valve group, a temperature and humidity sensor, a heater and the like.
Referring to fig. 1, in the prior art, both the temperature control of the high and low temperature test chambers adopts a PID temperature control method, and utilizes the control mode of a PID control module SSR (solid state relay) of the controller. However, the temperature cycle time of the test box is longer in high and low temperature experiments, nonlinear influence factors such as box vibration and noise are more frequently experienced, when the problems of large fluctuation, strong coupling and the like are solved, a reasonable mathematical model is difficult to construct, and the traditional PID controller often generates larger overshoot and takes longer time in adjustment, so that the control effect is poor, and the requirement of high-precision control of the system cannot be met.
Disclosure of Invention
In order to solve one or more problems set forth in the background art, the present application provides a fuzzy control method of a temperature control system of a high-low temperature test chamber. The application also relates to a fuzzy control device of the temperature control system of the high-low temperature test box.
The application provides a fuzzy control method of a temperature control system of a high-low temperature test box, which comprises the following steps:
collecting feedback temperature information in a high-low temperature test chamber;
comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate;
respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table;
and correcting correction parameters comprising a proportional coefficient, a time integral constant and a time differential constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters.
Optionally, the obtaining of the correction parameter:
determining a fuzzy universe expression based on the set error dynamic range;
determining the error and the error change rate and a fuzzy subset of the correction parameters according to the fuzzy universe expression;
determining the value of the correction parameter according to a preset regulation rule of the error and the error change rate to the PID corresponding to the correction parameter;
and calculating the PID value according to the value of the correction parameter.
Optionally, the fuzzy domain expression is as follows:
wherein [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ]A set temperature error dynamic range of (m=p, i, d); l (L) k (m), m= (p, i, d) is a scale factor; the e is the error and the ec is the error rate.
An inter-differential constant.
Optionally, the step of looking up the table is as follows:
obtaining a corresponding output control variable value in a matlab fuzzy tool box RuleViewer according to the error and the error change rate corresponding fuzzy sub-domains;
based on the fuzzy rule table, typing the data in the table into the shared data blocks DB6.DBD0-DB6.DBD584 in sequence from top to bottom and from left to right;
and calculating the address offset of the correction parameter, and determining the correction parameter corresponding to the error and the error change rate.
The application also provides a fuzzy control device of a temperature control system of a high-low temperature test chamber, which comprises:
the acquisition module is used for acquiring feedback temperature information in the high-low temperature test chamber;
the comparison module is used for comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate;
the rule module is used for respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table;
and the calculation module is used for correcting correction parameters comprising a proportional coefficient, a time integration constant and a time differentiation constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters.
Optionally, the computing module includes:
a first unit for determining a fuzzy universe expression based on the set error dynamic range;
a second unit for determining a fuzzy subset of the error and error rate, the correction parameter according to the fuzzy universe expression;
a third unit, for determining the value of the correction parameter according to the preset regulation rule of the error and the error change rate to the PID corresponding to the correction parameter by the even fish;
and a fourth unit for calculating the PID value according to the value of the correction parameter.
Optionally, the fuzzy domain expression is as follows:
wherein [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ]A set temperature error dynamic range of (m=p, i, d); l (L) k (m), m= (p, i, d) is a scale factor; the e is the error and the ec is the error rate.
An inter-differential constant.
Optionally, the computing module further includes:
the control unit is used for obtaining corresponding output control variable values in the matlab fuzzy tool box rule viewer according to the corresponding fuzzy sub-domains of the errors and the error change rates;
a typing unit for sequentially typing the data in the table into the shared data blocks DB6.DBD0-DB6.DBD584 from top to bottom and from left to right based on the fuzzy rule table;
and the parameter unit is used for calculating the address offset of the correction parameter and determining the correction parameter corresponding to the error and the error change rate.
Compared with the prior art, the application has the advantages that:
the application provides a fuzzy control method of a temperature control system of a high-low temperature test box, which comprises the following steps: collecting feedback temperature information in a high-low temperature test chamber; comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate; respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table; and correcting correction parameters comprising a proportional coefficient, a time integral constant and a time differential constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters. The fuzzy PID control is realized in the PLC, the action of the heater is controlled according to the output value of the PID, and the collected feedback temperature is compared with the set value so as to more accurately control the temperature.
Drawings
Fig. 1 is a schematic diagram of a prior art framework in the present application.
FIG. 2 is a schematic diagram of a fuzzy control flow of the temperature control system of the high and low temperature test chamber in the present application.
FIG. 3 is a schematic diagram of a high and low temperature test chamber frame in the present application.
FIG. 4 is a schematic diagram of a fuzzy PID temperature control architecture in the present application.
FIG. 5 is a block diagram of a fuzzy PID program in the present application.
Fig. 6 is a software flow chart in the present application.
FIG. 7 is a schematic diagram of a fuzzy control flow apparatus of the temperature control system of the high and low temperature test chamber in the present application.
Detailed Description
The following are examples of specific implementation provided for the purpose of illustrating the technical solutions to be protected in this application in detail, but this application may also be implemented in other ways than described herein, and one skilled in the art may implement this application by using different technical means under the guidance of the conception of this application, so this application is not limited by the following specific embodiments.
The application provides a fuzzy control method of a temperature control system of a high-low temperature test box, which comprises the following steps: collecting feedback temperature information in a high-low temperature test chamber; comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate; respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table; and correcting correction parameters comprising a proportional coefficient, a time integral constant and a time differential constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters. The fuzzy PID control is realized in the PLC, the action of the heater is controlled according to the output value of the PID, and the collected feedback temperature is compared with the set value so as to more accurately control the temperature.
FIG. 2 is a schematic diagram of a fuzzy control flow of the temperature control system of the high and low temperature test chamber in the present application.
Referring to fig. 2, S101 collects feedback temperature information in the high-low temperature test chamber.
In the application, the functions of data acquisition in the test box, implementation of a fuzzy algorithm and a PID algorithm, automatic and manual logic control and the like can be completed through a Programmable Logic Controller (PLC).
FIG. 3 is a schematic diagram of a high and low temperature test chamber frame in the present application.
Referring to fig. 3, the I/O terminal of the PLC is connected to analog signals such as digital signals of switching value, temperature and humidity, and the like, and conversion operation of digital and analog values can be completed. The control of the relay, the heater and the alarm unit can be completed through the fuzzy PID program operation of the PLC by collecting the temperature in the test box.
Referring to fig. 2, S102 compares the feedback temperature information with a preset temperature threshold to obtain an error and an error rate.
The method sets the test box to be a required temperature value, and the sensor is used for collecting the feedback temperature value in the box to the PLC, and the error e and the error change rate ec are obtained through comparison of the feedback value and the set value.
According to the method, fuzzy set operation is carried out, the PID controller with corrected parameters drives the relay to adjust the heater, the acquired temperature analog quantity AD is converted into digital quantity, and the digital quantity AD is compared with the set value again, so that temperature adjustment is achieved.
Referring to fig. 2, S103 respectively fuzzifies the errors and the error change rates in the domains of the respective fuzzy rules according to the preset fuzzy rules, and establishes a fuzzy rule table.
Specifically, the fuzzy PID control is realized in a PLC program, and the basic design principle of the Siemens user program adopted in the application is to carry out logic programming in a modularized structure.
Specifically, based on the siemens user program, the blocks commonly found in the STEP7 user program mainly include an Organization Block (OB), a Function Block (FB), a Function (FC), a System Function Block (SFB), a System Function (SFC), a background data block (DI), and a shared Data Block (DB), and the present application implements a fuzzy algorithm based on a structured system to program, as shown in fig. 5.
Specifically, according to the regulation rule of the collected temperature value error and the error change rate to the PID control parameter, according to expert experience, 49 fuzzy control rules in the temperature control system are summarized, for example:
(1) “ifEisNBandECisNB,thenΔk p ＝PBandΔk i ＝NBandΔk d ＝PS”；
(2) “ifEisNBandECisNS,thenΔk p ＝PMandΔk i ＝NMandΔk d ＝NB”；
……
(49)“ifEisPBandECisPB,thenΔk p ＝NBandΔk i ＝NBandΔk d ＝PB”。
the obtained fuzzy rule can be properly adjusted according to the control requirement, and different fuzzy sets e and ec can be respectively obtained to correspond to different output quantities delta k through offline calculation p 、Δk i 、Δk d And a fuzzy control lookup table is established by querying delta k p 、Δk i 、Δk d The values of the three parameters can be used to determine the output value of the PID.
Further, the present application encompasses complex process objects with large inertia and time-varying parameters that are possessed by temperature control characteristics. Although the conventional PID control is widely applied, the defect that the control parameters are not adjustable exists, and in industrial control, a fuzzy algorithm is not generally used independently, and the fuzzy algorithm is combined with the PID, so that the control rule forms a quantized value through the PID parameters, and the system is controlled to operate.
Therefore, the fuzzy control in this application is designed as follows:
let the error dynamic range of temperature be [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ](m=p, i, d); setting Lk (m) m= (p, i, d) as a scale factor, a fuzzy discourse domain transformation expression can be derived:
fuzzifying e and ec, and fuzzifying e, ec and fuzzifying correction parameter output delta k p 、Δk i 、Δk d The fuzzy subset argument of (1) is taken as { -3,3}, { -1.5,1.5}, respectively. Seven levels are selected for fuzzy sets: namely negative large (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), medium (PM), positive large (PB).
Referring to fig. 2, S104 corrects correction parameters including a proportional coefficient, a time integration constant, and a time differentiation constant by looking up a table based on the fuzzy rule table, and generates a control command according to the correction parameters.
In this application, the position PID control algorithm:
the fuzzy self-adaptive PID control algorithm is controlled in two steps in the system, namely, the initial parameters k, i and k, d are defined, and the second is according to the setting value delta k output by the fuzzy controller p 、Δk i 、Δk d The parameter adjustment is completed by itself, and the output value u (k) of the PID can be obtained according to the following correction formula.
In the application, the key of the application of the fuzzy PID control algorithm to the training system is the realization of the fuzzy control program of the lower computer, and the difficulty is the query problem of the fuzzy control query table.
FIG. 5 is a block diagram of a fuzzy PID program in the present application.
As shown in fig. 5, OB1 is a main loop block, and completes the call to the sub program (FB module, FC module). In the control program, a set functional block FB5 is a fuzzy control algorithm module, and comprises four sub-functional modules, wherein FC3 is used for realizing e and ec calculation and fuzzy quantization processing, FC4 is used for carrying out program query on a fuzzy control table, and FC5 is used for carrying out fuzzy processing on three parameters KP, KI and KD of PID. DB5 is the background block corresponding to FB5, which mainly stores parameters such as the set value, feedback value, error value, scale factor, quantization factor, etc. of the system, and is set as real data. DB6 is a shared data block where the fuzzy table output data value exists. FB41 is the PID control module in the system module library, DB7 is its corresponding background data block.
Fuzzy table lookup is critical in this application. Knowing that e, ec corresponds to fuzzy arguments { -3,3}, corresponding output control variable values can be derived in matlab fuzzy toolbox rule viewer.
After the fuzzy table is established, the data in the table is sequentially entered into the shared data blocks DB6.DBD0 to DB6.DBD584 from top to bottom and from left to right.
The access is performed in the PLC program by adopting the addressing mode of base address and index. The data are Real type and each data occupies a double word, and because the base address of the output quantity is 0, the offset addresses of three parameters delta Kp, delta Ki and delta Kd are respectively:
{[(EC+3)×21+(E+3)×3+0]}×4；
{[(EC+3)×21+(E+3)×3+1]}×4；
{[(EC+3)×21+(E+3)×3+2]}×4；
for example, when e=2, ec=3, Δk p 、Δk i 、Δk d Namely db6.dbd564, db6.dbd568 and db6.dbd572, read the contents in the addresses.
The method comprises the steps of establishing a temperature control method for adaptively adjusting PID parameters based on a fuzzy algorithm; e and ec shown in fig. 4 represent errors and error change rates between feedback temperature and set temperature, and the key of fuzzy control is a fuzzy rule, which is a control theory based on language rules and fuzzy reasoning and is based on expert experience rather than simply relying on mathematical formulas to work.
The theory of fuzzy aggregation is combined with PID control, the error and the error change rate are respectively fuzzified in respective domains, a fuzzy rule table is established, and the values of corrected delta KP, delta KI and delta KD are inquired in a table look-up mode, so that the adjustment of three parameters KP (proportional coefficient), KI (integral time constant) and KD (differential time constant) of the PID is completed. The fuzzy PID control of the design is realized in the PLC, the action of the heater is controlled according to the output value of the PID, and the temperature is controlled according to the system by comparing the collected feedback temperature with a set value.
The application also provides a fuzzy control device of a temperature control system of a high-low temperature test chamber, which comprises: the system comprises an acquisition module 201, a comparison module 202, a rule module 203 and a calculation module 204.
FIG. 7 is a schematic diagram of a fuzzy control flow apparatus of the temperature control system of the high and low temperature test chamber in the present application.
Referring to fig. 7, an acquisition module 201 is configured to acquire feedback temperature information in the high-low temperature test chamber.
In the application, the functions of data acquisition in the test box, implementation of a fuzzy algorithm and a PID algorithm, automatic and manual logic control and the like can be completed through a Programmable Logic Controller (PLC).
FIG. 3 is a schematic diagram of a high and low temperature test chamber frame in the present application.
Referring to fig. 3, the I/O terminal of the PLC is connected to analog signals such as digital signals of switching value, temperature and humidity, and the like, and conversion operation of digital and analog values can be completed. The control of the relay, the heater and the alarm unit can be completed through the fuzzy PID program operation of the PLC by collecting the temperature in the test box.
Referring to fig. 7, a comparison module 202 is configured to compare the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate.
The method sets the test box to be a required temperature value, and the sensor is used for collecting the feedback temperature value in the box to the PLC, and the error e and the error change rate ec are obtained through comparison of the feedback value and the set value.
According to the method, fuzzy set operation is carried out, the PID controller with corrected parameters drives the relay to adjust the heater, the acquired temperature analog quantity AD is converted into digital quantity, and the digital quantity AD is compared with the set value again, so that temperature adjustment is achieved.
Referring to fig. 7, a rule module 203 is configured to blur the errors and the error change rates in respective fuzzy kneeling domains according to a preset fuzzy rule, and build a fuzzy rule table.
Specifically, the fuzzy PID control is realized in a PLC program, and the basic design principle of the Siemens user program adopted in the application is to carry out logic programming in a modularized structure.
Specifically, based on the siemens user program, the blocks commonly found in the STEP7 user program mainly include an Organization Block (OB), a Function Block (FB), a Function (FC), a System Function Block (SFB), a System Function (SFC), a background data block (DI), and a shared Data Block (DB), and the present application implements a fuzzy algorithm based on a structured system to program, as shown in fig. 5.
Specifically, according to the regulation rule of the collected temperature value error and the error change rate to the PID control parameter, according to expert experience, 49 fuzzy control rules in the temperature control system are summarized, for example:
(1)“ifEisNBandECisNB,thenΔk p ＝PBandΔk i ＝NBandΔk d ＝PS”；
(2)“ifEisNBandECisNS,thenΔk p ＝PMandΔk i ＝NMandΔk d ＝NB”；
……
(49)“ifEisPBandECisPB,thenΔk p ＝NBandΔk i ＝NBandΔk d ＝PB”。
the obtained fuzzy rule can be properly adjusted according to the control requirement, and different fuzzy sets e and ec can be respectively obtained to correspond to different output quantities delta k through offline calculation p 、Δk i 、Δk d And a fuzzy control lookup table is established by querying delta k p 、Δk i 、Δk d The values of the three parameters can be used to determine the output value of the PID.
Further, the present application encompasses complex process objects with large inertia and time-varying parameters that are possessed by temperature control characteristics. Although the conventional PID control is widely applied, the defect that the control parameters are not adjustable exists, and in industrial control, a fuzzy algorithm is not generally used independently, and the fuzzy algorithm is combined with the PID, so that the control rule forms a quantized value through the PID parameters, and the system is controlled to operate.
Therefore, the fuzzy control in this application is designed as follows:
let the error dynamic range of temperature be [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ](m=p, i, d); setting Lk (m) m= (p, i, d) as a scale factor, a fuzzy discourse domain transformation expression can be derived:
fuzzifying e and ec, and fuzzifying e, ec and fuzzifying correction parameter output delta k p 、Δk i 、Δk d The fuzzy subset argument of (1) is taken as { -3,3}, { -1.5,1.5}, respectively. Seven levels are selected for fuzzy sets: namely negative large (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), medium (PM), positive large (PB).
Referring to fig. 7, a calculation module 204 is configured to modify a modification parameter including a proportional coefficient, a time integration constant, and a time differentiation constant by looking up a table based on the fuzzy rule table, and generate a control command according to the modification parameter.
In this application, the position PID control algorithm:
the fuzzy self-adaptive PID control algorithm is controlled in two steps in the system, namely, the initial parameters k, i and k, d are defined, and the second is according to the setting value delta k output by the fuzzy controller p 、Δk i 、Δk d The parameter adjustment is completed by itself, and the output value u (k) of the PID can be obtained according to the following correction formula.
In the application, the key of the application of the fuzzy PID control algorithm to the training system is the realization of the fuzzy control program of the lower computer, and the difficulty is the query problem of the fuzzy control query table.
FIG. 5 is a block diagram of a fuzzy PID program in the present application.
As shown in fig. 5, OB1 is a main loop block, and completes the call to the sub program (FB module, FC module). In the control program, a set functional block FB5 is a fuzzy control algorithm module, and comprises four sub-functional modules, wherein FC3 is used for realizing e and ec calculation and fuzzy quantization processing, FC4 is used for carrying out program query on a fuzzy control table, and FC5 is used for carrying out fuzzy processing on three parameters KP, KI and KD of PID. DB5 is the background block corresponding to FB5, which mainly stores parameters such as the set value, feedback value, error value, scale factor, quantization factor, etc. of the system, and is set as real data. DB6 is a shared data block where the fuzzy table output data value exists. FB41 is the PID control module in the system module library, DB7 is its corresponding background data block.
Fuzzy table lookup is critical in this application. Knowing that e, ec corresponds to fuzzy arguments { -3,3}, corresponding output control variable values can be derived in matlab fuzzy toolbox rule viewer.
After the fuzzy table is established, the data in the table is sequentially entered into the shared data blocks DB6.DBD0 to DB6.DBD584 from top to bottom and from left to right.
The access is performed in the PLC program by adopting the addressing mode of base address and index. The data are Real type and each data occupies a double word, and because the base address of the output quantity is 0, the offset addresses of three parameters delta Kp, delta Ki and delta Kd are respectively:
{[(EC+3)×21+(E+3)×3+0]}×4；
{[(EC+3)×21+(E+3)×3+1]}×4；
{[(EC+3)×21+(E+3)×3+2]}×4；
for example, when e=2, ec=3, Δk p 、Δk i 、Δk d I.e., DB6.DBD564, DB6.DBD568, and DB6.DBD572, in the read addressAnd (3) capacity.
The method comprises the steps of establishing a temperature control method for adaptively adjusting PID parameters based on a fuzzy algorithm; e and ec shown in fig. 4 represent errors and error change rates between feedback temperature and set temperature, and the key of fuzzy control is a fuzzy rule, which is a control theory based on language rules and fuzzy reasoning and is based on expert experience rather than simply relying on mathematical formulas to work.
The theory of fuzzy aggregation is combined with PID control, the error and the error change rate are respectively fuzzified in respective domains, a fuzzy rule table is established, and the values of corrected delta KP, delta KI and delta KD are inquired in a table look-up mode, so that the adjustment of three parameters KP (proportional coefficient), KI (integral time constant) and KD (differential time constant) of the PID is completed. The fuzzy PID control of the design is realized in the PLC, the action of the heater is controlled according to the output value of the PID, and the temperature is controlled according to the system by comparing the collected feedback temperature with a set value.
Claims (10)
1. A fuzzy control method of a temperature control system of a high-low temperature test chamber is characterized by comprising the following steps:
collecting feedback temperature information in a high-low temperature test chamber;
comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate;
respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table;
and correcting correction parameters comprising a proportional coefficient, a time integral constant and a time differential constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters.
2. The fuzzy control method of the temperature control system of the high and low temperature test chamber according to claim 1, wherein the correction parameters are obtained by:
determining a fuzzy universe expression based on the set error dynamic range;
determining the error and the error change rate and a fuzzy subset of the correction parameters according to the fuzzy universe expression;
determining the value of the correction parameter according to a preset regulation rule of the error and the error change rate to the PID corresponding to the correction parameter;
and calculating the PID value according to the value of the correction parameter.
3. The fuzzy control method of a temperature control system of a high and low temperature test chamber according to claim 2, wherein the fuzzy discourse expression is as follows:
wherein [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ]A set temperature error dynamic range of (m=p, i, d); l (L) k (m), m= (p, i, d) is a scale factor; the e is the error and the ec is the error rate.
4. The fuzzy control method of a temperature control system of a high and low temperature test chamber according to claim 2, wherein the expression of the PID is as follows:
wherein the K is p ，K i ，K d Is the proportional coefficient, time integral constant and time derivative constant of the correction parameters.
5. The fuzzy control method of a temperature control system of a high and low temperature test chamber according to claim 1, wherein the table look-up step is as follows:
obtaining a corresponding output control variable value in a matlab fuzzy tool box RuleViewer according to the error and the error change rate corresponding fuzzy sub-domains;
based on the fuzzy rule table, typing the data in the table into the shared data blocks DB6.DBD0-DB6.DBD584 in sequence from top to bottom and from left to right;
and calculating the address offset of the correction parameter, and determining the correction parameter corresponding to the error and the error change rate.
6. A fuzzy control device of a temperature control system of a high-low temperature test chamber is characterized by comprising:
the acquisition module is used for acquiring feedback temperature information in the high-low temperature test chamber;
the comparison module is used for comparing the feedback temperature information with a preset temperature threshold value to obtain an error and an error change rate;
the rule module is used for respectively carrying out fuzzy modeling on the errors and the error change rates in respective fuzzy kneeling domains according to preset fuzzy rules, and establishing a fuzzy rule table;
and the calculation module is used for correcting correction parameters comprising a proportional coefficient, a time integration constant and a time differentiation constant through table lookup based on the fuzzy rule table, and generating a control instruction according to the correction parameters.
7. The fuzzy control apparatus of the high and low temperature test chamber temperature control system of claim 6, wherein the calculation module comprises:
a first unit for determining a fuzzy universe expression based on the set error dynamic range;
a second unit for determining a fuzzy subset of the error and error rate, the correction parameter according to the fuzzy universe expression;
a third unit, for determining the value of the correction parameter according to the preset regulation rule of the error and the error change rate to the PID corresponding to the correction parameter by the even fish;
and a fourth unit for calculating the PID value according to the value of the correction parameter.
8. The fuzzy control apparatus of the high and low temperature test chamber temperature control system of claim 7, wherein the fuzzy discourse expression is as follows:
wherein [ e ] min ，e max ]、[ec min ，ec max ]、[ΔK min(m) ,ΔK max(m) ]A set temperature error dynamic range of (m=p, i, d); l (L) k (m), m= (p, i, d) is a scale factor; the e is the error and the ec is the error rate.
9. The fuzzy control apparatus of the high and low temperature test chamber temperature control system of claim 7, wherein the expression of the PID is as follows:
an inter-differential constant.
10. The fuzzy control apparatus of the high and low temperature test chamber temperature control system of claim 6, wherein the calculation module further comprises:
the control unit is used for obtaining corresponding output control variable values in the matlab fuzzy tool box rule viewer according to the corresponding fuzzy sub-domains of the errors and the error change rates;
a typing unit for sequentially typing the data in the table into the shared data blocks DB6.DBD0-DB6.DBD584 from top to bottom and from left to right based on the fuzzy rule table;
and the parameter unit is used for calculating the address offset of the correction parameter and determining the correction parameter corresponding to the error and the error change rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211505855.0A CN115993778A (en) | 2022-11-28 | 2022-11-28 | Fuzzy control method and device for temperature control system of high-low temperature test chamber |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211505855.0A CN115993778A (en) | 2022-11-28 | 2022-11-28 | Fuzzy control method and device for temperature control system of high-low temperature test chamber |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115993778A true CN115993778A (en) | 2023-04-21 |
Family
ID=85994522
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211505855.0A Pending CN115993778A (en) | 2022-11-28 | 2022-11-28 | Fuzzy control method and device for temperature control system of high-low temperature test chamber |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115993778A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117008527A (en) * | 2023-05-31 | 2023-11-07 | 济南鲁味斋食品有限责任公司 | Processing control method, system and medium for marinated food |
-
2022
- 2022-11-28 CN CN202211505855.0A patent/CN115993778A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117008527A (en) * | 2023-05-31 | 2023-11-07 | 济南鲁味斋食品有限责任公司 | Processing control method, system and medium for marinated food |
| CN117008527B (en) * | 2023-05-31 | 2024-03-05 | 济南鲁味斋食品有限责任公司 | Processing control method, system and medium for marinated food |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112817231B (en) | High-precision tracking control method for mechanical arm with high robustness | |
| CN111766777A (en) | PID controller and PID control method | |
| CN111596545B (en) | Self-adaptive fault-tolerant preset performance control method for multi-input multi-output mechanical system | |
| Zhang et al. | Output feedback control of a class of discrete MIMO nonlinear systems with triangular form inputs | |
| JPH10133703A (en) | Adaptive robust controller | |
| Li et al. | Adaptive neural network-based finite-time impedance control of constrained robotic manipulators with disturbance observer | |
| CN115993778A (en) | Fuzzy control method and device for temperature control system of high-low temperature test chamber | |
| CN111142404A (en) | Micro-positioning platform based on piezoelectric ceramic drive and modeling and control method thereof | |
| CN111856942B (en) | Time lag and error improvement method for remote intervention operation control system | |
| CN111352387B (en) | Intelligent compensation method for realizing nonlinear error of numerical control machine tool | |
| CN107490958A (en) | A kind of Fuzzy Adaptive Control Scheme of series parallel robot in five degrees of freedom | |
| CN110703718A (en) | Industrial process control method based on signal compensation | |
| Commuri et al. | CMAC neural network control of robot manipulators | |
| Rahali et al. | Fault tolerant control of robot manipulators based on adaptive fuzzy type-2 backstepping in attendance of payload variation | |
| Gerov et al. | Parameter estimation methods for the fopdt model, using the lambert w function | |
| CN114063445A (en) | Design method of fuzzy controller | |
| Yang et al. | Adaptive neural network control for flexible telerobotic systems with communication constraints | |
| Mrad et al. | Experimental comparative analysis of adaptive fuzzy logic controllers | |
| Lagrat et al. | Optimal adaptive fuzzy control for a class of unknown nonlinear systems | |
| CN115128951A (en) | Double-loop high-performance control method based on expected track limited optimization | |
| Rómoli et al. | Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor | |
| Kocian et al. | Implementation of fuzzy logic control based on plc | |
| Yin et al. | An Adaptive Control Method for Robot Constant Force Polishing Device | |
| Chittillapilly et al. | Tube model reference adaptive control for a cylindrical tank system | |
| CN111152213A (en) | Mechanical arm vibration compensation method and device based on hybrid control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |