CN111336828A - Heating furnace temperature controller based on FCM fuzzy time sequence - Google Patents
Heating furnace temperature controller based on FCM fuzzy time sequence Download PDFInfo
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- CN111336828A CN111336828A CN202010291344.8A CN202010291344A CN111336828A CN 111336828 A CN111336828 A CN 111336828A CN 202010291344 A CN202010291344 A CN 202010291344A CN 111336828 A CN111336828 A CN 111336828A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0003—Monitoring the temperature or a characteristic of the charge and using it as a controlling value
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Abstract
The invention discloses a furnace temperature controller of a heating furnace based on an FCM fuzzy time sequence, which comprises the following specific steps: and defining and dividing a domain of discourse, defining a fuzzy set and a fuzzy time sequence, establishing a fuzzy relation, predicting and defuzzifying and finishing the control of the furnace temperature of the controlled object. Aiming at the complex industrial process of the temperature change of the regenerative heating furnace, such as the measurement difficulty, the characteristics of multivariable, time variation, nonlinearity, coupling, large inertia, pure hysteresis and the like, the invention designs the furnace temperature controller of the heating furnace based on the FCM fuzzy time sequence, which is applied to the furnace temperature control of the heating furnace, and the invention realizes the rapid control response, small overshoot, no steady-state control error and good robustness, and obviously improves the ton steel energy consumption and the heating quality of the blank.
Description
Technical Field
The invention relates to the field of combustion control systems of rod and wire heating furnaces in the metallurgical industry, in particular to a heating furnace temperature controller based on an FCM fuzzy time sequence.
Background
The heating furnace is a complex controlled object, and has the factors of nonlinearity, time-varying property, pure hysteresis factor, uncertain random interference and the like, and the adjustment of the temperature of the heating furnace is mainly completed by controlling the gas flow, so that the establishment of a reasonable combustion control scheme is the key for realizing the combustion control of the heating furnace.
The combustion control scheme needs to improve the fuel utilization rate on the premise of meeting the quality requirement and the yield, realize the optimal temperature and combustion control, and reduce the energy consumption to the maximum extent, which is the task of controlling the heating furnace. The heating furnace is a closed combustion body, so that the parameters of a controlled object in the closed body cannot be directly detected, and therefore the conventional fuzzy PID controller has the following problems:
1. the fuzzy set universe cannot reflect the real furnace temperature response capability, and the system stability and efficiency are reduced after long-time operation;
2. the system response time is slow, and a steady-state error exists;
3. the control system has weak prediction capability, learning capability and self-adaption capability.
Through patent retrieval, the related public technologies of the needle heating furnace temperature controller at present are as follows:
patent application No.: 201510169298.3, according to the scheme, the kiln head output value is obtained by variable speed (fuzzy) PID operation according to the kiln head feedback temperature of the rotary kiln, the kiln head output value is output to a kiln head coal scale to control the coal injection amount of the kiln head coal within a certain range, and the set value of the variable speed (fuzzy) PID temperature, namely the actual set temperature of the decomposing furnace, is obtained by program operation of a decomposing furnace temperature control subsystem. And then calculating an error and an error change rate through the outlet feedback temperature of the decomposing furnace and the actual set temperature of the decomposing furnace, obtaining a PID (fuzzy) value of a variable speed PID (proportion integration differentiation) through the calculated error and error rate, applying the PID operation of the variable speed (fuzzy) according to the outlet feedback temperature of the decomposing furnace, calculating an output value of the decomposing furnace, outputting the output value of the decomposing furnace to a coal scale of the decomposing furnace, and controlling the coal injection amount of the decomposing furnace within a certain range. However, according to the scheme, only a single feedback temperature is used for PID operation, the system has the phenomena of slow response and serious lag, the temperature cannot be adjusted in time, the system is unstable, and the furnace temperature cannot be controlled in time.
Patent application No.: 201010521573.0, the proposal is a method for controlling the temperature of an open-fire heating furnace, comprising the following steps: monitoring the furnace temperature, obtaining a furnace temperature feedback value, calculating the difference between the furnace temperature set value and the furnace temperature feedback value according to the furnace temperature feedback value and the furnace temperature set value as a deviation value DV1, calculating the difference between the furnace temperature set value and the furnace temperature feedback value in unit time, namely the slope of the furnace temperature change value as a deviation value DV2, obtaining the speed V of the open-fire heating furnace unit from a speed regulator of the open-fire heating furnace unit, obtaining a first multi-feedforward output component FFV according to the speed V of the unit, and obtaining a second multi-feedforward output component FFT according to the difference between the furnace temperature set value and the furnace temperature feedback value and the deviation value DV 1. And searching PID control parameters based on a fuzzy control rule according to the deviation values DV1 and DV2, generating a regulation control parameter OP1 according to the PID control parameters, and controlling the regulation control parameter OP1 to a gas flow regulating valve and an air flow regulating valve by combining the first multi-feedforward output component FFV and the second multi-feedforward output component FFT as final control output values. The control process of the application scheme is only to utilize the feedback value to carry out PID value selection through a fuzzy logic rule, the conditions in the furnace, learning ability and self-adaptive ability cannot be pre-judged in advance, the furnace temperature required by the field conditions cannot be timely reached, and the production rhythm is influenced.
Disclosure of Invention
The invention discloses a furnace temperature controller of a heating furnace based on an FCM fuzzy time sequence, which is mainly used for overcoming the defects and shortcomings in the prior art, designing the furnace temperature controller of the heating furnace based on the FCM fuzzy time sequence and applying the furnace temperature controller to a heat accumulating type heating furnace temperature control system, solving the long-term instability in the heating furnace temperature control and ensuring the good stability and robustness of the system.
The technical scheme adopted by the invention is as follows:
a heating furnace temperature controller based on an FCM fuzzy time sequence is disclosed, and the control mode of the controller comprises the following specific steps:
the method comprises the following steps: definition and partitioning of discourse domain: let U be a given universe of discourse and divide the universe of discourse into n subintervals
；
Step two: defining a fuzzy set and a fuzzy time series: the fuzzy set A defined in the domain of discourse U is represented as follows:
wherein the content of the first and second substances,
is uiFor the membership functions of the fuzzy set a,
is uiFor the degree of membership of the fuzzy set a,
；
step three: establishing a fuzzy relation: collecting a furnace temperature set value and a furnace temperature feedback value as a two-dimensional time sequence, calculating a difference value between the set value and a feedback value at the next moment, collecting the difference value at each moment, when the sequence data quantity n > =30, performing fuzzy division by using the time sequence of the FCM difference value, determining a domain to which a control quantity belongs according to errors and error variation, and then entering a fourth step, or entering a fifth step;
step four: prediction and defuzzification: determining an accurate output value according to a domain to which the controlled variable belongs, and finishing defuzzification processing;
step five: and finishing the control of the furnace temperature of the controlled object.
Furthermore, in the third step, fuzzy partition is performed by using the FCM difference time series, and the iterative computation steps are as follows:
A. setting an initial membership matrix: setting classification number c, initializing membership matrix U by random number between [0,1] to satisfy constraint condition of following formula (1),
wherein n is the number of attributes in the data set, and in the furnace temperature control of the heating furnace, the attributes are the difference value between the set value and the feedback value and the change value of the difference value, so that n = 2;
B. calculating a membership clustering center: c cluster centers are calculated using the following formula (2),
C. calculating a value function and solving the change quantity of the membership degree: the cost function is calculated using the following equation (3),
then comparing the cost function with a given threshold value, stopping iteration when the cost function value is smaller than the given threshold value or the change amount of the cost function value relative to the last time is smaller than the given threshold value, and turning to the step E, or turning to the step D;
D. a new U matrix is calculated using the following equation (4),
then returning to the step B;
E. output matrix U, UijIs namely XiMembership for the jth partition.
Further, the number of classifications c in step A has a value of 7.
Through the description of the above technical solution, compared with the prior art, the advantage of this solution is:
because the discourse domain of the traditional fuzzy PID controller is divided into equal parts and the fuzzy relation is a time-invariant system, the control effect of the traditional fuzzy PID controller cannot achieve the ideal control effect after the furnace condition and the working condition are changed along with the change of time in the actual control of the heating furnace. According to the scheme, a gradient descent optimization algorithm is adopted to search the clustering center and the membership matrix thereof which enable the value of the objective function to be small based on the FCM fuzzy time sequence, fuzzy intervals can be divided according to the distribution characteristics of furnace temperature data of the heating furnace, and the discrimination between different distributed data can be obviously increased by the result of fuzzy division performed by adopting the FCM fuzzy time sequence, so that a fuzzy association rule with higher quality can be obtained. And finally, obtaining the accurate value of the PID parameter through defuzzification. The result shows that the method can more comprehensively reflect the characteristics of the system and is beneficial to improving the prediction accuracy of the PID parameters in a complex environment.
Compared with a fuzzy PID, the furnace temperature controller of the heating furnace based on the FCM fuzzy time sequence has the advantages that the discourse domain is subjected to rolling optimization through the difference time sequence of furnace temperature control, and the length of the discourse domain is divided into non-linear and non-equal parts in a non-mean clustering mode, so that the actual combustion control condition of the heating furnace can be reflected better. The scheme can realize rapid control response, small overshoot, no steady-state control error and good robustness, and obviously improves the ton steel energy consumption and the heating quality of the blank.
Drawings
FIG. 1 is a schematic diagram of the fuzzy time series controller of the present invention.
FIG. 2 is a graph of a membership matrix according to the present invention.
Fig. 3 is a control flow diagram of the present invention.
Detailed Description
Embodiments of the present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1 to 3, a furnace temperature controller of a heating furnace based on an FCM fuzzy time series, the control method of the controller comprises the following specific steps:
the method comprises the following steps: definition and partitioning of discourse domain: let U be a given universe of discourse and divide the universe of discourse into n subintervals
；
Step two: defining a fuzzy set and a fuzzy time series: the fuzzy set A defined in the domain of discourse U is represented as follows:
wherein the content of the first and second substances,
is uiFor the membership functions of the fuzzy set a,
is uiFor the degree of membership of the fuzzy set a,
；
step three: establishing a fuzzy relation: collecting a furnace temperature set value and a furnace temperature feedback value as a two-dimensional time sequence, calculating a difference value between the set value and a feedback value at the next moment, collecting the difference value at each moment, when the sequence data quantity n > =30, performing fuzzy division by using the time sequence of the FCM difference value, determining a domain to which a control quantity belongs according to errors and error variation, and then entering a fourth step, or entering a fifth step;
step four: prediction and defuzzification: determining an accurate output value according to a domain to which the controlled variable belongs, and finishing defuzzification processing;
step five: and finishing the control of the furnace temperature of the controlled object.
Furthermore, in the third step, fuzzy partition is performed by using the FCM difference time series, and the iterative computation steps are as follows:
A. setting an initial membership matrix: setting classification number c, initializing membership matrix U by random number between [0,1] to satisfy constraint condition of following formula (1),
wherein n is the number of attributes in the data set, and in the furnace temperature control of the heating furnace, the attributes are the difference value between the set value and the feedback value and the change value of the difference value, so that n = 2;
B. calculating a membership clustering center: c cluster centers are calculated using the following formula (2),
C. calculating a value function and solving the change quantity of the membership degree: the cost function is calculated using the following equation (3),
then comparing the cost function with a given threshold value, stopping iteration when the cost function value is smaller than the given threshold value or the change amount of the cost function value relative to the last time is smaller than the given threshold value, and turning to the step E, or turning to the step D;
D. a new U matrix is calculated using the following equation (4),
then returning to the step B;
E. output matrix U, UijIs namely XiMembership for the jth partition.
Further, the number of classifications c in step A has a value of 7.
In the analysis process, compared with the fuzzy PID, the furnace temperature controller of the heating furnace based on the FCM fuzzy time sequence has the advantages that the discourse domain is subjected to rolling optimization through the difference time sequence of furnace temperature control, and the length of the discourse domain is subjected to nonlinear and non-equal division through a non-mean clustering mode, so that the actual combustion control condition of the heating furnace can be reflected better.
Through the description of the above technical solution, compared with the prior art, the advantage of this solution is:
because the discourse domain of the traditional fuzzy PID controller is divided into equal parts and the fuzzy relation is a time-invariant system, the control effect of the traditional fuzzy PID controller cannot achieve the ideal control effect after the furnace condition and the working condition are changed along with the change of time in the actual control of the heating furnace. According to the scheme, a gradient descent optimization algorithm is adopted to search the clustering center and the membership matrix thereof which enable the value of the objective function to be small based on the FCM fuzzy time sequence, fuzzy intervals can be divided according to the distribution characteristics of furnace temperature data of the heating furnace, and the discrimination between different distributed data can be obviously increased by the result of fuzzy division performed by adopting the FCM fuzzy time sequence, so that a fuzzy association rule with higher quality can be obtained. And finally, obtaining the accurate value of the PID parameter through defuzzification. The result shows that the method can more comprehensively reflect the characteristics of the system and is beneficial to improving the prediction accuracy of the PID parameters in a complex environment.
Compared with a fuzzy PID (proportion integration differentiation), the furnace temperature controller of the heating furnace based on the FCM fuzzy time sequence has the advantages that the discourse domain is subjected to rolling optimization through the difference time sequence of furnace temperature control, and the length of the discourse domain is divided in a non-linear and non-equal division mode through non-mean value clustering, so that the actual combustion control condition of the heating furnace can be reflected better.
The controller based on the FCM fuzzy time sequence has the following characteristics:
(1) adopting current time historical data to divide domains;
(2) based on the FCM mode, a non-mean clustering algorithm is adopted to divide a domain of discourse into non-equal divisions and non-linear divisions, so that the performance of controlling parameter mutation is effectively avoided;
⑶ the change of furnace condition and working condition is fully considered by dividing the discourse domain, and the control characteristics under the current condition can be reflected.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications of the present invention using this concept shall fall within the scope of infringing the present invention.
Claims (3)
1. The utility model provides a heating furnace temperature controller based on FCM fuzzy time series which characterized in that: the control mode of the controller comprises the following specific steps:
the method comprises the following steps: definition and partitioning of discourse domain: let U be a given universe of discourse and divide the universe of discourse into n subintervals
；
Step two: defining a fuzzy set and a fuzzy time series: the fuzzy set A defined in the domain of discourse U is represented as follows:
wherein the content of the first and second substances,
is uiFor the membership functions of the fuzzy set a,
is uiFor the degree of membership of the fuzzy set a,
；
step three: establishing a fuzzy relation: collecting a furnace temperature set value and a furnace temperature feedback value as a two-dimensional time sequence, calculating a difference value between the set value and a feedback value at the next moment, collecting the difference value at each moment, when the sequence data quantity n > =30, performing fuzzy division by using the time sequence of the FCM difference value, determining a domain to which a control quantity belongs according to errors and error variation, and then entering a fourth step, or entering a fifth step;
step four: prediction and defuzzification: determining an accurate output value according to a domain to which the controlled variable belongs, and finishing defuzzification processing;
step five: and finishing the control of the furnace temperature of the controlled object.
2. The FCM fuzzy time series based furnace temperature controller of claim 1, wherein: in the third step, fuzzy partition is performed by using the FCM difference time sequence, and the iterative computation steps are as follows:
A. setting an initial membership matrix: setting classification number c, initializing membership matrix U by random number between [0,1] to satisfy constraint condition of following formula (1),
wherein n is the number of attributes in the data set, and in the furnace temperature control of the heating furnace, the attributes are the difference value between the set value and the feedback value and the change value of the difference value, so that n = 2;
B. calculating a membership clustering center: c cluster centers are calculated using the following formula (2),
C. calculating a value function and solving the change quantity of the membership degree: the cost function is calculated using the following equation (3),
then comparing the cost function with a given threshold value, stopping iteration when the cost function value is smaller than the given threshold value or the change amount of the cost function value relative to the last time is smaller than the given threshold value, and turning to the step E, or turning to the step D;
D. a new U matrix is calculated using the following equation (4),
then returning to the step B;
E. output matrix U, UijIs namely XiMembership for the jth partition.
3. The FCM fuzzy time series based furnace temperature controller of claim 2, wherein: the number of classifications c in step A has a value of 7.
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Cited By (2)
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| CN112666834A (en) * | 2021-01-20 | 2021-04-16 | 福建三钢闽光股份有限公司 | Heating furnace temperature control method adaptive to severe fluctuation of fuel gas heat value |
| CN114198914A (en) * | 2021-11-18 | 2022-03-18 | 邯郸钢铁集团有限责任公司 | Automatic combustion control method of hot blast stove based on idea of dynamically tracking slope |
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