CN112748660A - Memory, heating furnace outlet temperature control method, device and equipment - Google Patents
Memory, heating furnace outlet temperature control method, device and equipment Download PDFInfo
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Abstract
The invention discloses a memory, a heating furnace outlet temperature control method, a device and equipment, wherein the nonlinear prediction control method comprises the following steps: presetting a set value of a nonlinear predictive controller; constructing an object model group of the nonlinear control system through model identification; generating a dynamic equation of the object model set, and calculating the disturbance characteristic of the object model set according to the dynamic equation; respectively acquiring an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; calculating the optimal state estimation value of the nonlinear control system at the next moment through an extended Kalman filter; substituting the optimal state estimation value of the dynamic equation of the object model group into a nonlinear predictive control algorithm of the nonlinear control system to obtain an optimal solution; the optimal solution is used for input of the set of object models. The invention can realize effective control on the outlet temperature of the heating furnace and reduce the control failure phenomenon.
Description
Technical Field
The invention relates to the field of process control, in particular to a memory, a heating furnace outlet temperature control method, a device and equipment.
Background
In the prior art, the outlet temperature control system of the heating furnace can keep stable outlet temperature by adjusting the opening of the fuel gas flow regulating valve so as to avoid production accidents. Conventional cascade control employs a PID controller to determine the current control input based on the deviation of the measured and set values of the current and past outputs of the process.
The inventor finds that the prior art has at least the following defects through research:
the disturbance factors of a cascade control system formed by the fuel gas flow and the outlet temperature of the heating furnace are complex, and the control requirement is very high, so that the outlet temperature of the heating furnace in the conventional device fluctuates greatly, and an ideal control effect cannot be achieved.
Disclosure of Invention
The invention mainly aims to realize effective control on the outlet temperature of the heating furnace and achieve the control index.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention discloses a heating furnace outlet temperature control method, which comprises the following steps:
s11, presetting a set value of a nonlinear predictive controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
s12, constructing an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
s13, generating a dynamic equation of the object model set, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model set according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
s14, respectively obtaining an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
s15, calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
s16, substituting the optimal state estimation value of the dynamic equation of the object model set into a nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution of the opening of the fuel gas flow regulating valve; the optimal solution is used for input of the set of object models.
Preferably, in the present invention, the disturbance further includes:
one of the composition of the fuel gas, the inlet flow rate and temperature of the material to be heated and any combination thereof.
Preferably, in the present invention, the mathematical model for describing the object model group includes:
n-dimensional vector nonlinear function: xk＝f[Xk-1，uk-1，k-1，pk-1]And, in addition,
m-dimensional vector nonlinear function:
wherein, XkState variables, u, representing non-linear functionskInput variables, p, representing non-linear functionskModel parameter variables, Z, representing non-linear functionskAn output variable representing a non-linear function.
And performing first-order Taylor expansion on the state space model of the object model group at the nominal model parameter point for multiple times according to a preset time interval by taking the model parameter as a variable, and calculating the statistical characteristic of disturbance.
Preferably, in the present invention, the performing a first-order taylor expansion on the state space model of the object model group at the nominal model parameter point multiple times according to a preset time interval with the model parameter as a variable to calculate the statistical characteristic of the disturbance includes:
and (3) assuming that the parameter variables of the object model group obey normal distribution, equating the change of the model parameters of the object model group into disturbance, and calculating the statistical property of the disturbance.
Preferably, in the present invention, the calculating, by using an extended kalman filter, an optimal state estimation value at the next time of the nonlinear control system in a recursive manner according to the actual output measurement value and the state estimation value includes:
s21, obtaining the optimal state estimation value of the nonlinear control system at the moment k-1
S22, obtaining the actual output measured value { y at the moment k of the nonlinear control system through detectionkAt the same time pass
Obtaining the state estimation value of the nonlinear control system at the k moment
S23, passing the actual output measurement value ykFor the state estimation value
Correcting to obtain the optimal state estimation value of the nonlinear control system at the k moment
In another aspect of the present invention, there is also provided a heating furnace outlet temperature control apparatus, including:
the device comprises a presetting unit, a control unit and a control unit, wherein the presetting unit is used for presetting a set value of a nonlinear prediction controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
the building unit is used for building an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
the characteristic calculation unit is used for generating a dynamic equation of the object model group, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model group according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
the numerical value acquisition unit is used for respectively acquiring an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
the correction unit is used for calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
the result generating unit is used for substituting the optimal state estimation value of the dynamic equation of the object model set into the nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution for adjusting the opening of the fuel gas flow regulating valve; the optimal solution is used for input of the set of object models.
Preferably, in the present invention, the disturbance further includes:
one of the composition of the fuel gas, the inlet flow rate and temperature of the material to be heated and any combination thereof.
In another aspect of the embodiment of the present invention, there is also provided a memory including a software program adapted to execute the steps of the above-mentioned nonlinear control method for the outlet temperature of the heating furnace by a processor.
In another aspect of the embodiments of the present invention, there is also provided a heating furnace outlet temperature control apparatus, including a computer program stored on a memory, the computer program including program instructions that, when executed by a computer, cause the computer to perform the method of the above aspects, and achieve the same technical effects.
Advantageous effects
Because the disturbance factors of a cascade control system formed by the fuel gas flow and the outlet temperature of the heating furnace are complex and the control requirement is very high, in order to enable the outlet temperature of the heating furnace to achieve an ideal control effect, the invention adopts a nonlinear control technology, and specifically comprises the following steps:
the method comprises the steps of firstly presetting a reasonable interval of the outlet temperature of a heating furnace as a set value of a nonlinear predictive controller in a nonlinear control system; in addition, the invention also defines the actuator sub-object model and each controlled object sub-object model as an object model group, and then calculates and obtains the disturbance characteristic of the object model group; then, obtaining the optimal state estimation value of the object model group by using an extended Kalman filtering algorithm; in the invention, the optimal state estimation value is obtained by the following steps: taking a nonlinear control system at a certain time (k time) as an example, on one hand, a state estimation value of the nonlinear control system at the k time is obtained at the last time (k-1) of the time; on the other hand, the actual output measurement value of the nonlinear control system at the time k is also acquired; and then, correcting the state estimation value according to the actual output measurement value through an extended Kalman filter to generate the optimal state estimation value of the nonlinear control system.
Compared with the nonlinear control system in the prior art, the nonlinear control system in the invention adds the step of correcting the state estimation value (obtaining the optimal state estimation value) by the extended Kalman filter, so that the stability of the system can be still improved when the nonlinear control system is greatly disturbed, and the potential safety hazard problem caused by unstable temperature of the outlet of the heating furnace can be effectively reduced.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical means of the present application more clearly understood and to make the same based on the content of the description, one or more preferred embodiments are listed below and described in detail below with reference to the accompanying drawings in order to make the above and other objects, technical features, and advantages of the present application more comprehensible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view showing the steps of a method for controlling the outlet temperature of a heating furnace according to the present invention;
FIG. 2 is a schematic structural view of an outlet temperature control system of the heating furnace according to the present invention;
FIG. 3 is a graph of probability distribution of model parameter variables in the furnace exit temperature control system of the present invention;
FIG. 4 is a schematic structural view of an outlet temperature control device of the heating furnace according to the present invention;
FIG. 5 is a schematic structural view of the outlet temperature control apparatus of the heating furnace according to the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Because disturbance factors borne by a cascade control system formed by fuel gas flow and outlet temperature of a heating furnace are complex and control requirements are very high, in order to enable the outlet temperature of the heating furnace to achieve an ideal control effect, referring to fig. 1 and 2, an embodiment of the invention provides a heating furnace outlet temperature control method, which comprises the following steps:
s11, presetting a set value of a nonlinear predictive controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
the embodiment of the invention aims to effectively control the temperature of the outlet of the heating furnace, so that a reasonable interval of the temperature of the outlet of the heating furnace is preset; therefore, when the temperature of the outlet of the heating furnace is in a reasonable interval, the temperature of the outlet of the heating furnace is considered to be in a safe temperature area, and potential safety hazards in production cannot be brought.
In practical applications, the range value of the reasonable interval of the temperature at the outlet of the heating furnace can be set by those skilled in the art according to actual needs, and is not limited in particular.
S12, constructing an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the pair of actuator sub-object models are used for describing and adjusting the opening of the fuel gas flow adjusting valve; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
in the embodiment of the present invention, an object model group of the nonlinear control system is defined, and several object models of the PID21, the actuator 22, the first controlled object 23, and the second controlled object 24 in the nonlinear control system are used as sub-object models of the object model group. The nonlinear predictive controller 01 may generate the input variables u of the object model group.
The mathematical model for describing the object model group in the embodiment of the present invention may be specifically expressed as:
n-dimensional vector nonlinear function: xk＝f[Xk-1，uk-1，k-1，pk-1]And, an m-dimensional vector nonlinear function: zk＝h[Xk，uk，k]。
Wherein, XkState variables, u, representing non-linear functionskInput variables, p, representing non-linear functionskModel parameter variables, Z, representing non-linear functionskAn output variable representing a non-linear function.
S13, generating a dynamic equation of the object model set, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model set according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
then, a corresponding dynamic equation is generated by using the object model group (including the PID sub-object model, the actuator sub-object model and each controlled object sub-object model) as a whole, and the disturbance characteristic of the object model group is calculated according to the dynamic equation.
In practical application, the method for calculating the disturbance characteristic of the object model group may specifically be that the first-order taylor expansion is performed on the state space model of the object model group at the nominal model parameter point for multiple times according to a preset time interval by using the model parameter as a variable, so as to calculate the statistical characteristic of the disturbance. Preferably, it may be assumed that the parameter variables of the object model group follow a normal distribution, the change of the parameters of the object model group is equivalent to a disturbance, and the statistical characteristic of the disturbance is calculated.
In practical applications, the model parameters may be the outlet temperature of the furnace and the fuel gas flow rate measured or collected under a nonlinear control system.
In practical application, the disturbance may be a fuel gas pre-valve pressure value measured or collected under a nonlinear control system, and may further include one of a composition of the fuel gas, an inlet flow rate and a temperature of the heated material, any combination thereof, and the like.
In the embodiment of the present invention, the specific manner of equating the change of the model parameter as disturbance may be:
assuming that the model parameter variables (i.e., furnace exit temperature, fuel gas flow) in fig. 3 follow a normal distribution, the model parameter variables are represented by a vector P, and their mean and variance are as follows:
E[P]＝P0
E[(P-P0)(P-P0)T]＝QP
in the formula, P0Representing nominal model parameter values, QPCovariance matrix representing model parameters, assuming the actual parameters after change to be Pi，plantThe model parameter distribution is shown in fig. 3.
The dashed envelope in fig. 3 represents the nominal model parameter Pi，0Probability distribution curve of (1), actual model parameter Pi，plantWith nominal model parameters Pi，0Can be represented as Pi，plant＝Pi，0+δiI.e. the variation is deltai. The abscissa in fig. 3 is the value of the model parameter and the ordinate is the probability density of the corresponding value of the model parameter.
In order to estimate the state of the system after the model parameters change, the change of the model parameters can be equivalent to disturbance, and the state space model of the object model group is subjected to first-order Taylor expansion at the nominal model parameter point by taking the model parameters as variables. In order to make the output of the prediction model approach the output of the actual system, the statistical characteristics of the disturbance need to be calculated according to the uncertain information of the model parameters.
At nominal model parameters Pi，0The first-order taylor expansion is performed on a discrete state space model (i.e., a mathematical model of an object model set used for describing a nonlinear control system), and a dynamic system equation is obtained as follows:
the above formula can be simplified into
Wherein, wk-1、vkIs a perturbation equivalent by uncertainty in the model parameters.
S14, respectively obtaining an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
in the embodiment of the invention, not only the state estimation value of the nonlinear control system needs to be calculated, but also the actual output measurement value at the same moment needs to be acquired, specifically, for the moment k, on one hand, the state estimation value of the nonlinear control system at the moment k can be generated at the moment k-1, and on the other hand, the actual output measurement value of the controlled object (namely, the outlet temperature of the heating furnace) at the moment k needs to be acquired; the method for acquiring the state estimation value at the time k may include: and at the moment k-1 (namely the moment before the current moment), calculating and generating a state estimation value for estimating the nonlinear control system at the moment k by taking the input u applied to the object model group as a parameter according to the dynamic equation and the disturbance characteristic thereof.
S15, calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
in the embodiment of the invention, an extended Kalman filter 03 is further arranged to obtain the state estimation value of the object model group, and the state estimation value is corrected through actually outputting the measurement value y to obtain the optimal state estimation value, so that the overlarge error between the state estimation value and the actual state of the nonlinear control system can be effectively avoided; in practical application, the calculating the optimal state estimation value of the nonlinear control system at the next time in a recursive manner may specifically include the following steps:
s21, obtaining the optimal state estimation value of the nonlinear control system at the moment k-1
S22, obtaining the actual output measured value { y at the moment k of the nonlinear control system through detectionkAt the same time pass
Obtaining the state estimation value of the nonlinear control system at the k moment
S23, passing the actual output measurement value ykFor the state estimation value
Correcting to obtain the optimal state estimation value of the nonlinear control system at the k moment
In the embodiment of the present invention, the specific way of calculating the optimal state estimation value at the next time of the nonlinear control system may include:
after the change of the model parameters is equivalent to disturbance, the disturbance is further equivalent to Gaussian white noise, and an extended Kalman filter is designed according to the Gaussian white noise, and the method specifically comprises the following steps:
1, finding wk-1，vkStatistical properties of
wk-1Has a mean value of E [ w ]k-1]And E [ w ]k-1]≠0，wk-1The variance of (c) is:
E[(wk-1-E[Wk-1])·[Wj-1])T]＝Qk-1
vkhas a mean value of E [ v ]k]And E [ v ]k]＝0，vkThe variance of (c) is:
E[(vk-E[vk])·(vj-E[vj])T]＝Rk
and is
The above formula can be simplified into:
wk-1～(E[wk-1]，Qk-1)
vk～(0，0)
2, mixing wk-1Equivalent to white gaussian noise with mean zero, comprising:
order to
For equivalent zero mean gaussian white noise, the original nonlinear control system equation can be equivalent to:
Zk＝h[Xk，uk，k]+vk
wherein the content of the first and second substances,
the mean and variance of (a) are as follows:
vkthe mean and variance of (a) are as follows:
E[vk]＝0，
E[(vk-E[vk])·(vj-E[vj])T]-Rk-0
and is
Performing state estimation by using an extended Kalman filter to obtain a nonlinear function f [ X ]k1，uk1，k-1，pk1]And h [ X ]k，uk，k]Are respectively approximated to
And
the nearby first order taylor polynomial is:
wherein, Fk-1And HkIs a Jacobian matrix:
a linear state space model approximated by a first order taylor polynomial is obtained:
using extended kalman filter algorithm (EKF):
initialization:
and (3) predicting the state:
state prediction error covariance matrix:
wherein the content of the first and second substances,
kalman gain:
and (3) state estimation:
state estimation error covariance matrix:
Pk＝(I-KkHk)Pk|k-1
the optimal estimation state can be obtained by solving
S16, substituting the optimal state estimation value of the dynamic equation of the object model set into a nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution of the opening of the fuel gas flow regulating valve; the optimal solution is used for input of the set of object models.
The optimal state estimation value in the embodiment of the present invention is used as the input of the nonlinear predictive controller 01, and is the state estimation value after being corrected, so that a more reasonable optimal solution (the optimal solution is the input of the object model group) can be obtained by the nonlinear predictive control algorithm in the nonlinear predictive controller 01.
In practical applications, the control model of the non-linear predictive controller 01 may be set to:
Xk＝f[Xk-1，uk-1，k-1，pk-1]+E[wk-1]
Zk＝h[Xk，uk，k]
for the nonlinear control system, a hierarchical prediction iterative nonlinear prediction control method is adopted, and first, u is assumed0＝0，X 00 is an equilibrium point of the nonlinear control system, i.e. f (0, 0) is 0, h (0) is 0, f and h are continuously differentiable, the system is linearized at the equilibrium point (0, 0), and the original nonlinear prediction model is equivalent to:
Xk＝AXk-1+Buk-1+D(Xk-1，uk-1，k-1，pk-1)
Zk＝CXk+G[Xk，uk，k]
wherein the content of the first and second substances,
G[Xk，uk，k]＝h[Xk，uk，k]-CXk
from time k, it can be
Xk+i＝AXk+i-1+Buk+i-1+D(Xk+i-1，uk+i-1，k+i-1，pk+i-1)
Zk+i＝CXk+i+G[Xk+i，uk+1，k+i]
Wherein i is 1, 2.
In the formula, A, B and C are constants, D and G are nonlinear terms, and additional prediction is introduced to convert an original nonlinear prediction model into a linear model, so that the problem of difficulty in solving nonlinear prediction control optimization is solved;
wherein u is0As initial estimate, X can be arbitrarily given0Can be at XkAnd u0Recursion under known conditions yields:
Xk+i-AXk+i-1+Buk+i-1+li
Zk+i＝CXk+i+hi，i＝1，...，P
uk+i-1＝uk+M-1，i＞＞M
wherein:
when X is knownkAnd when the estimated X and u are known constants, the above equation is a linear model, and at this time, the optimal solution of the nonlinear control system in the embodiment of the present invention may be performed by using a predictive control method of a linear system, specifically:
the predicted output of a linear system is expressed as:
order to
The predicted output of the linear system is then:
wherein the content of the first and second substances,
outputting the prediction of the linear system
And a set value Zr,kSubstituting into an equation of optimization solution, the optimization performance index of the control system is as follows:
wherein Z isr，kFor the output reference trajectory, P is the prediction horizon, M is the control horizon, Q ═ diag { Q (1), Q (2),. the next, Q (P) }, R ═ diag { R (1), R (2),. the next, R (M-1) } are the output and input weights, respectively, assuming M ≦ P, when i ≧ M,
while solving the linear system optimization performance index, the constraint condition of the system is also considered, and the input and output constraint conditions are expressed as follows:
through the optimization solution, an optimal solution Δ u, which is an optimal solution of the nonlinear control system in the embodiment of the present invention (i.e., an optimal solution of the opening degree of the fuel gas flow regulating valve), can be obtained.
In summary, in order to improve the control effect of the outlet temperature of the heating furnace in the cascade control system formed by the fuel gas flow and the outlet temperature of the heating furnace, the embodiment of the invention adopts a nonlinear control technology, specifically:
the embodiment of the invention firstly presets a reasonable interval of the outlet temperature of the heating furnace as a set value of a nonlinear predictive controller in a nonlinear control system; in addition, the invention also defines the actuator sub-object model and each controlled object sub-object model as an object model group, and then calculates and obtains the disturbance characteristic of the object model group; then, obtaining the optimal state estimation value of the object model group by using an extended Kalman filtering algorithm; in the invention, the optimal state estimation value is obtained by the following steps: taking a nonlinear control system at a certain time (k time) as an example, on one hand, a state estimation value of the nonlinear control system at the k time is obtained at the last time (k-1) of the time; on the other hand, the actual output measurement value of the nonlinear control system at the time k is also acquired; and then, correcting the state estimation value according to the actual output measurement value through an extended Kalman filter to generate the optimal state estimation value of the nonlinear control system.
If the state of the nonlinear control system at the current moment is obtained by adopting a traditional nonlinear predictive control model in a recursion mode, the error of the whole prediction process is likely to be amplified continuously, and then the model is easy to cause serious model mismatch frequently, so that the control effect on the outlet temperature of the heating furnace is poor, the outlet temperature of the heating furnace is easy to lose effective control, and the hidden danger of production accidents is caused.
The optimal state estimation value obtained in the invention is closer to the actual state of the nonlinear control system, so that the frequency and degree of model mismatch can be effectively reduced, the influence caused by the model mismatch is effectively reduced, the outlet temperature of the heating furnace can be timely and effectively controlled, and the hidden danger of production accidents is eliminated. And the stability of the system can still be ensured when the nonlinear control system is greatly disturbed.
In another aspect of the embodiment of the present invention, a heating furnace outlet temperature control device is further provided, and fig. 4 shows a schematic structural diagram of the heating furnace outlet temperature control device provided in the embodiment of the present invention, where the heating furnace outlet temperature control device is a device corresponding to the heating furnace outlet temperature control method in the embodiment corresponding to fig. 1, that is, the heating furnace outlet temperature control method in the embodiment corresponding to fig. 1 is implemented by using a virtual device, and each virtual module constituting the heating furnace outlet temperature control device may be executed by an electronic device, such as a network device, a terminal device, or a server. The heating furnace outlet temperature control device in the embodiment of the invention can realize the nonlinear predictive control required by industrial control. Specifically, the heating furnace outlet temperature control device in the embodiment of the present invention includes:
a presetting unit 101, configured to preset a setting value of a nonlinear predictive controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
the building unit 102 is used for building an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
the characteristic calculation unit 103 is configured to generate a dynamic equation of the object model set, equate changes of model parameters to disturbances, and calculate disturbance characteristics of the object model set according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
a numerical value obtaining unit 104, configured to obtain an actual output measurement value and a state estimation value of the nonlinear control system at the current time, respectively; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
a correcting unit 105, configured to calculate, through an extended kalman filter, an optimal state estimation value at the next time of the nonlinear control system in a recursive manner according to the actual output measurement value and the state estimation value;
a result generating unit 106, configured to substitute the optimal state estimation value of the dynamic equation of the object model set into a nonlinear predictive control algorithm of the nonlinear control system to obtain an optimal solution of the opening of the flow valve of the regulated fuel gas; the optimal solution is used for input of the set of object models.
Preferably, in the present invention, the disturbance further comprises one of a composition of fuel gas, an inlet flow rate and a temperature of the material to be heated, and any combination thereof.
Since the working principle and the beneficial effects of the heating furnace outlet temperature control device in the embodiment of the present invention have been described and illustrated in the heating furnace outlet temperature control method corresponding to fig. 1, they can be referred to each other and are not described herein again.
In an embodiment of the present invention, a memory is further provided, where the memory includes a software program, and the software program is adapted to enable the processor to execute each step in the heating furnace outlet temperature control method corresponding to fig. 1.
The embodiment of the present invention may be implemented by a software program, that is, by writing a software program (and an instruction set) for implementing each step in the nonlinear predictive control method corresponding to fig. 1, where the software program is stored in a storage device, and the storage device is disposed in a computer device, so that the software program can be called by a processor of the computer device to implement the purpose of the embodiment of the present invention.
In an embodiment of the present invention, a heating furnace outlet temperature control device is further provided, where a memory included in the heating furnace outlet temperature control device includes a corresponding computer program product, and when a program instruction included in the computer program product is executed by a computer, the computer may execute the heating furnace outlet temperature control method in the above aspects, and achieve the same technical effect.
Fig. 5 is a schematic diagram of a hardware configuration of a device of a method for controlling the outlet temperature of a heating furnace as an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the device includes one or more processors 610, a bus 630, and a memory 620. Taking one processor 610 as an example, the apparatus may further include: input device 640, output device 650.
The processor 610, the memory 620, the input device 640, and the output device 650 may be connected by a bus or other means, such as the bus connection in fig. 5.
The memory 620, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device, i.e., the processing method of the above-described method embodiment, by executing the non-transitory software programs, instructions and modules stored in the memory 620.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 620 optionally includes memory located remotely from the processor 610, which may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 640 may receive input numeric or character information and generate a signal input. The output device 650 may include a display device such as a display screen.
The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform:
s11, presetting a set value of a nonlinear predictive controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
s12, constructing an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
s13, generating a dynamic equation of the object model set, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model set according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
s14, respectively obtaining an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
s15, calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
s16, substituting the optimal state estimation value of the dynamic equation of the object model set into a nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution of the opening of the fuel gas flow regulating valve; the optimal solution is used for input of the set of object models.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage device and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage device includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a ReRAM, an MRAM, a PCM, a NAND Flash, a NOR Flash, a Memory, a magnetic disk, an optical disk, or other various media that can store program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for controlling the outlet temperature of a heating furnace is characterized by comprising the following steps:
s11, presetting a set value of a nonlinear predictive controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
s12, constructing an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
s13, generating a dynamic equation of the object model set, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model set according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
s14, respectively obtaining an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
s15, calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
s16, substituting the optimal state estimation value of the dynamic equation of the object model set into a nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution of the opening of the fuel gas flow regulating valve; the optimal solution is used for input of the set of object models.
2. The method of controlling the outlet temperature of the heating furnace according to claim 1, wherein the disturbing further comprises:
one of the composition of the fuel gas, the inlet flow rate and temperature of the material to be heated and any combination thereof.
3. The furnace exit temperature control method of claim 1, wherein the mathematical model describing the set of object models comprises:
n-dimensional vector nonlinear function:
and the number of the first and second groups,
m-dimensional vector nonlinear function: zk＝h[Xk，uk，k]
Wherein, XkState variables, u, representing non-linear functionskInput variables, p, representing non-linear functionskModel parameter variables, Z, representing non-linear functionskAn output variable representing a non-linear function.
4. The method according to claim 1, wherein said calculating the disturbance characteristic of the object model group comprises:
and performing first-order Taylor expansion on the state space model of the object model group at the nominal model parameter point for multiple times according to a preset time interval by taking the model parameter as a variable, and calculating the statistical characteristic of disturbance.
5. The method of claim 3, wherein the calculating the statistical properties of the disturbance by performing a first-order Taylor expansion on the state space model of the object model set at the nominal model parameter points with the model parameters as variables a plurality of times at preset time intervals comprises:
and (3) assuming that the parameter variables of the object model group obey normal distribution, equating the change of the model parameters of the object model group into disturbance, and calculating the statistical property of the disturbance.
6. The method of claim 1, wherein the calculating an optimal state estimation value of the nonlinear control system at the next moment in time in a recursive manner according to the actual output measurement value and the state estimation value through an extended kalman filter comprises:
s21, obtaining the optimal state estimation value of the nonlinear control system at the moment k-1
S22, obtaining the actual output measured value { y at the moment k of the nonlinear control system through detectionkAt the same time pass
Obtaining the state estimation value of the nonlinear control system at the k moment
S23, passing the actual output measurement value ykFor the state estimation value
Making a correction to obtainThe optimal state estimation value of the nonlinear control system at the k moment
7. A heating furnace outlet temperature control device is characterized by comprising:
the device comprises a presetting unit, a control unit and a control unit, wherein the presetting unit is used for presetting a set value of a nonlinear prediction controller in a nonlinear control system; the set value comprises a reasonable interval of the outlet temperature of the heating furnace;
the building unit is used for building an object model group comprising a PID sub-object model, an actuator sub-object model, a first controlled object sub-object model and a second controlled object sub-object model through model identification; the actuator sub-object model is used for describing the opening of the regulating valve for regulating the fuel gas flow; the first controlled object sub-object model is used for describing the outlet temperature of the heating furnace; the second controlled object sub-object model is used for describing fuel gas flow; the object model group is expressed in the form of a state space model;
the characteristic calculation unit is used for generating a dynamic equation of the object model group, equating the change of model parameters to disturbance, and calculating the disturbance characteristic of the object model group according to the dynamic equation; the disturbance comprises a fuel gas pre-valve pressure;
the numerical value acquisition unit is used for respectively acquiring an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; the method for acquiring the state estimation value comprises the following steps: calculating and generating a state estimation value for estimating the current time state of the nonlinear control system according to the dynamic equation and the disturbance characteristic thereof by taking the input acting on the object model group as a parameter at the moment before the current time;
the correction unit is used for calculating the optimal state estimation value of the nonlinear control system at the next moment in a recursion mode according to the actual output measurement value and the state estimation value through an extended Kalman filter;
the result generating unit is used for substituting the optimal state estimation value of the dynamic equation of the object model group into the nonlinear predictive control algorithm of the nonlinear control system to obtain the optimal solution of the opening of the regulating fuel gas flow valve; the optimal solution is used for input of the set of object models.
8. The furnace exit temperature control device according to claim 7, wherein said disturbance further comprises:
one of the composition of the fuel gas, the inlet flow rate and temperature of the material to be heated and any combination thereof.
9. Memory, characterized in that it comprises a software program adapted to carry out the steps of the furnace outlet temperature control method according to any one of claims 1 to 6 by a processor.
10. A furnace exit temperature control apparatus comprising a bus, a processor and a memory as claimed in claim 9;
the bus is used for connecting the memory and the processor;
the processor is configured to execute a set of instructions in the memory.
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| CN113534661A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Resistance furnace temperature control method based on Kalman filtering and non-minimum state space |
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| CN113534661A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Resistance furnace temperature control method based on Kalman filtering and non-minimum state space |
| CN113534661B (en) * | 2021-06-03 | 2023-03-17 | 太原理工大学 | Resistance furnace temperature control method based on Kalman filtering and non-minimum state space |
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