CN113848722B - Self-adaptive control method for circulating fluidized bed industrial boiler system - Google Patents
Self-adaptive control method for circulating fluidized bed industrial boiler system Download PDFInfo
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Abstract
An adaptive control method for an industrial circulating fluidized bed boiler system relates to an industrial boiler control method, and the industrial circulating fluidized bed boiler has the characteristics of large disturbance, multiple parameters, strong coupling, nonlinearity and the like, and a mathematical model is difficult to build by a conventional control algorithm. Aiming at the problem, the invention relates to the technical field of automatic control, in particular to a system optimization method by combining a generalized predictive control algorithm with on-line identification self-adaptive control, which aims to realize the self-adaptive control of complex industrial systems such as a circulating fluidized bed and the like, and the simulation effect of the system is obviously better than that of a classical control system. The invention utilizes the Matlab graphical user interface to design the man-machine interaction interface of the generalized predictive control algorithm for the industrial boiler, and the graphical user interface and the drawn generalized predictive control simulation diagram simplify the difficulty of using the generalized predictive control in the industrial system. The GUI application design of the generalized predictive control algorithm adopted by the invention has a certain practical significance in improving the automation level of a complex industrial system.
Description
Technical Field
The invention relates to an industrial boiler control method, in particular to a self-adaptive control method of a circulating fluidized bed industrial boiler system.
Background
In the thirty-four decades of the last century, from PID control to the continuous development of adaptive control strategies, industrial process control is continuously developed and is applied to various fields, and social productivity and convenience of daily life of people are improved. Expert scholars at home and abroad use PID control method to explore circulating fluidized bed boiler in a large quantity.
Such as PID algorithm-based boiler temperature control study (Wang Suiping, automation application, 2019 (05): 35-36+47) [ 1], fuzzy PID control algorithm application in electric boiler temperature control systems (Hu Xin New, information technology and informatization, 2019 (08): 118-120) [ 2 ]. PID is the most traditional and most routine control algorithm in distributed control systems, and PID control is mostly adopted in actual production and operation processes of factories.
However, because the accuracy requirement of the control object model is higher, when the working condition changes, the PID method is difficult to meet the system control. The circulating fluidized bed boiler has the characteristics of time varying, multiple parameters, strong coupling, nonlinearity and the like, the mathematical model of the circulating fluidized bed boiler is difficult to build, and the problem of the complex industrial system cannot reach the expected effect by using the conventional control theory. And most circulating fluidized bed boiler systems have low automation level, and the control mode needs to be improved. Therefore, the improvement of the control of the hearth temperature of the circulating fluidized bed boiler is of great significance to the actual operation of the boiler.
Generalized Predictive Control (GPC) is a type of predictive control that achieves adaptive control through on-line parameter identification. Such as "application of improved generalized predictive control in main steam temperature of thermal power generation boiler" (Wang Sheng, zhang Guyan, university of red peak, 2019,35 (12): 49-53) [ 3 ], improved generalized predictive control algorithm and application simulation study in boiler steam temperature control "(Liang Tao, ge Qun, shandong electric technology, 2017,44 (05): 54-57) [ 4 ]. In the control strategy, firstly, an online identification and estimation model is combined according to past control input, present input and output data and predicted output data. And then rolling and optimizing the predicted output and the expected output according to a certain performance index, and correcting the predicted output to correct and obtain the optimal control law.
Disclosure of Invention
The invention aims to provide a self-adaptive control method for a circulating fluidized bed industrial boiler system, which combines a generalized predictive control method with on-line identification self-adaptive control to optimize an industrial system and realize self-adaptive control of complex industrial systems such as a circulating fluidized bed, and has important significance for improving the automation level of the complex industrial systems.
The invention aims at realizing the following technical scheme:
an adaptive control method for a circulating fluidized bed industrial boiler system, the method comprises the steps of establishing a model, wherein a prediction model of GPC adopts a controlled autoregressive integral moving average (CARIMA) model; in the control strategy, firstly, an online identification and estimation model is combined according to past control input, present input and output data and predicted output data; and then rolling and optimizing the predicted output and the expected output according to a certain performance index, correcting the predicted output to correct and obtain an optimal control law, and specifically comprising the following steps:
1) The system adopts a controlled autoregressive integral moving average (CARIMA) model as a prediction model, and is written as:
A(z -1 )y(k)＝B(z -1 )u(k-1)+C(z -1 )ξ(k)/Δ
wherein z is -1 Is a backward operator; y (k) and u (k) represent the output and input at time k, respectively; ζ (k) represents a white noise sequence with zero mean; delta = 1-z -1 Is a difference operator;
in the above, n a 、n b 、n c Respectively polynomial a (z -1 )、B(z -1 )、C(z -1 ) Is the order of (2); if the system time lag is greater than zero, B (z -1 ) The coefficient of one or several items of the polynomial head is equal to zero; when the generalized predictive control is pushed, C (z -1 )＝1；
2) For system robustness enhancement, consider the effect of the current time input u (k) on future time, using the following objective function:
wherein n is the maximum predicted length; m represents the control length, and m is less than or equal to n in total; y (k+j) is the j-th output of the system prediction; w is an expected value output by an object, and a signal w (k+j) is an output reference sequence; λ (j) is a control weighting coefficient, taking a constant value greater than zero, if λ (j) =0 means that the control increment is not constrained;
for the purpose of softening control, the control is to track the reference trajectory instead of directly tracking the output to the set point, as follows:
w(k+j)＝α j y(k)+(1-α j )y r (j＝1,2,…,n)
wherein: y is r Y (k) and w (k) are respectively the set value of the system, the actually measured output value of the system and the reference track, alpha is the softening coefficient, 0<α<1；
The Diophantine equation was introduced:
1＝A(z -1 )ΔE j (z -1 )+z -j F j (z -1 )
wherein,,
and (3) sorting a forward generalized forecast equation of the controllable object:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)+E j (z -1 )ξ(k+j)
in the method, in the process of the invention,
G j (z -1 )＝B(z -1 )E j (z -1 )＝g 0 +g 1 z -1 +…+g j-1 z -j+1
neglecting the noise effect, the output predicted value at the k moment can be obtained:
the optimal output predicted value obtainable according to the above equation is represented by a vector:
in the middle of
Optimal control rate
Adopting a Recursive Least Squares (RLS) parameter identification algorithm with forgetting factors:
wherein lambda is forgetting factor, 0.95<λ<1, a step of; k (K) is a weight factor, and P (K) is a positive covariance matrix; for recursive operations, it is necessary to giveAn initial value of P (k); according to the recursive least square method, the element G in the matrix G and the vector F can be obtained 0 ，g 1 … and f (k+m).
According to the self-adaptive control method of the circulating fluidized bed industrial boiler system, a model of the circulating fluidized bed boiler control system is combined with a mechanism model, operation parameters and data of a circulating fluidized bed and site disturbance through Matlab, and the gas quantity is selected as input u (k), and the fluidized bed temperature is output y (k); for a single-input, single-output (SISO) discrete system, a circulating fluidized bed temperature CARIMA controlled model is established:
y(k)＝-a 1 y(k-1)-a 2 y(k-2)+b 0 u(k-1)+b 1 u(k-2)+ξ(k)
collecting x groups of fluidized bed temperature data actually operated on site, preprocessing the previous y groups of data such as removing abnormal values, and then establishing a fluidized bed temperature mathematical model as input data and output data, and leaving z groups of data for model accuracy verification, wherein x=y+z;
parameter identification is carried out by adopting RLS, and the initial value of the RLS parameter is as follows: g n-1 =1, f (k+m) =1; forgetting factor μ=0.99; a softening coefficient α=0.5; xi (k) is [ -0.1,0.1)]White noise is uniformly distributed.
The model designs a GUI user interface based on MATLAB; designing an interactive interface by using buttons, radio buttons, editable text, static text, coordinate areas and panel elements; a radio button can change the category of a desired curve, set the desired curve as a step signal and input parameters into an editable text box;
N a 、N b respectively A (z) -1 )、B(z -1 )；U 0 And Y 0 Is the initial value of the horizontal and vertical coordinates;P(0)＝10 6 I；
the operation control panel is selected to perform 'operation', the system is simulated, and a tracking curve of generalized predictive control under the action of different signals (square wave signals, sine wave signals and step signals) can be obtained after the operation.
The interface comprises a CARIMA parameter model module, a GPC parameter module, an expected curve module, an operation control interface module and a simulation image module.
According to the self-adaptive control method for the circulating fluidized bed industrial boiler system, the CARIMA model parameter module and the GPC parameter module are used for completing the setting of generalized predictive control parameters, the expected curve module can adjust and set curve signals, and the simulation image module comprises a generalized predictive control algorithm control effect and a delta U change condition curve.
The invention has the advantages and effects that:
1. the invention adopts the combination of a generalized predictive control method and on-line identification self-adaptive control to optimize the system, and aims to realize the self-adaptive control of complex industrial systems such as a circulating fluidized bed and the like. The invention utilizes the Matlab graphical user interface to design the man-machine interaction interface for carrying out generalized predictive control on the industrial boiler, and the graphical user interface and the drawn generalized predictive control simulation diagram simplify the difficulty of using the generalized predictive control in the industrial system. The GUI application design with generalized predictive control has important implications for increasing the automation level of complex industrial systems.
2. The invention provides a GUI application design of a generalized predictive control in a circulating fluidized bed industrial boiler system. The predictive control self-corrector is based on a CARIMA model, adopts long-period optimization performance indexes, combines identification and self-correction mechanisms, has the characteristics of stronger robustness, low model requirement and the like, and has a wide application range. The method can overcome the defects in self-adaption such as generalized minimum variance, pole allocation and the like.
Drawings
FIG. 1 is a block diagram of a circulating fluidized bed boiler system;
FIG. 2 is a generalized predictive control block diagram;
FIG. 3 is a schematic diagram of an implicit generalized predictive controller Simulink model;
FIG. 4 is a control diagram of implicit generalized predictive control under a step signal;
FIG. 5 is a GPC control effect of an identification model under square wave signals;
FIG. 6 is a diagram of a generalized predictive control simulation GUI operational interface;
FIG. 7 is a graph of a generalized predictive control square wave signal trace;
FIG. 8 is a graph of generalized predictive control sine wave signal tracking;
FIG. 9 is a generalized predictive control step signal tracking graph.
Detailed Description
The present invention will be described in detail with reference to the embodiments shown in the drawings.
In the predictive control theory, the invention needs a basic model for describing the dynamic behavior of the system, and becomes a predictive model. It should have predictive functionality, i.e. the ability to predict future output values of the system based on historical data and future inputs of the system.
1. The system adopts a CARIMA model as a prediction model, wherein the model CARIMA is an abbreviation of "Controller Auto-Regressive Integrated Moving-Average" and can be translated into a "controlled autoregressive integral moving Average model". This model can be written as:
A(z -1 )y(k)＝B(z -1 )u(k-1)+C(z -1 )ξ(k)/Δ (1)
wherein z is -1 Is a backward operator; y (k) and u (k) represent the output and input at time k, respectively; ζ (k) represents a white noise sequence with zero mean; delta = 1-z -1 Is a differential operator.
In the above, n a 、n b 、n c Respectively polynomial a (z -1 )、B(z -1 )、C(z -1 ) Is a function of the order of (2). If the system time lag is greater than zero, the coefficient or coefficients of the beginning of the B (z-1) polynomial are equal to zero. Clarke et al, when pushing to generalized predictive control, let C (z -1 )＝1。
2. For system robustness enhancement, consider the effect of the current time input u (k) on future time, using the following objective function:
wherein n is the maximum predicted length; m represents the control length, and m is less than or equal to n in total; y (k+j) is the j-th output of the system prediction; w is an expected value output by an object, and a signal w (k+j) is an output reference sequence; λ (j) is a control weighting coefficient, taking a constant value greater than zero, if λ (j) =0 means that the control increment is not constrained.
For the purpose of softening control, the control is to track the reference trajectory instead of directly tracking the output to the set point, as follows:
w(k+j)＝α j y(k)+(1-α j )y r (j＝1,2,…,n) (4)
wherein: y is r Y (k) and w (k) are respectively the set value of the system, the actually measured output value of the system and the reference track, alpha is the softening coefficient, 0<α<1。
To predict the output y (k+j) of step j by rolling optimization, a self-correction algorithm is used for Generalized Minimum Variance Control (GMVC) to obtain an optimal prediction. The Diophantine equation is introduced herein:
1＝A(z -1 )ΔE j (z -1 )+z -j F j (z -1 ) (5)
wherein,,
multiplying both sides of (1) by E j (z -1 )z j And (3) combining the method with the method (4), and finishing a forward generalized forecast equation of the controlled object:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)+E j (z -1 )ξ(k+j) (7)
in the method, in the process of the invention,
G j (z -1 )＝B(z -1 )E j (z -1 )＝g 0 +g 1 z -1 +…+g j-1 z -j+1 (8)
neglecting the noise effect, the output predicted value at the k moment can be obtained:
the optimal output predicted value obtainable according to the above equation is represented by a vector:
in the middle of
3. Optimal control rate
If order
W＝[w(k+1),w(k+2),…,w(k+n)] T (13)
Then equation (3) can be represented as a matrix vector:
J＝(Y-W) T (Y-W)+λΔU T ΔU (14)
will optimize the predicted valueInstead of Y, formula (9) is substituted into (13) and +.>The method can obtain:
ΔU＝(G T G+λI) -1 G T (W-F) (15)
Δ U, W, F is the control increment, tracking the reference trajectory, and predicting the vector, respectively. The control amount input at the next time is:
u(k)＝u(k-1)+g T (W-F) (16)
in the formula g T Is (G) T G+λI) -1 G T Is a first row of (c). To solve the optimal control rate deltau, the matrix G and the prediction vector F need to be identified through input and output by using an implicit self-correction method according to a prediction equation.
The m parallel predictors available according to equation (9) are:
y(k+1)＝g 0 Δu(k)+f(k+1)+E 1 ξ(k+1)
y(k+2)＝g 1 Δu(k)+g 0 Δu(k+1)+f(k+2)+E 2 ξ(k+2)
…
y(k+m)＝g m-1 Δu(k)+…+g 0 Δu(k+m-1)+f(k+m)+E m ξ(k+m) (17)
from the above equation, all elements in the matrix G appear in the mth equation, and thus the matrix G can be found by identifying them.
Wherein the model parameters can be vector-codedAnd data parameters->The representation is:
all output expressions can be expressed in matrix form as follows:
or (b)
Adopting a Recursive Least Squares (RLS) parameter identification algorithm with forgetting factors:
where λ is a forgetting factor, with 0.95< λ <1.K (K) is a weight and P (K) is a positive covariance matrix.
In general, for recursive operations, it is necessary to giveInitial value of P (k). According to the recursive least square method, the element G in the matrix G and the vector F can be obtained 0 ，g 1 … and f (k+m).
4. Identification of parameters
When designing the optimization control system of the circulating fluidized bed boiler, firstly, establishing a mathematical model of the industrial boiler. However, the parameters are difficult to determine, and the subsystems of the control system are mutually coupled, so that the mathematical relationship between the input and the output cannot be accurately obtained. For control systems with excessively complex operation mechanisms, experimental modeling can record the output response of the system by applying a certain pulse signal to the system as input and control the change of the input to enable the output to reach a set value. The method for determining the black box model of the system by using the input and output data of the system is called system identification.
Considering the operating state of a general autonomous system, most industrial nonlinear objects can be described by using a linear model in a control range. And selecting the gas quantity as input u (k) and the fluidized bed temperature as output y (k) by combining Matlab with a mechanism model, operation parameters and data of the circulating fluidized bed and site disturbance. For a single-input, single-output (SISO) discrete system, a circulating fluidized bed temperature CARIMA controlled model can be built as:
y(k)＝-a 1 y(k-1)-a 2 y(k-2)+b 0 u(k-1)+b 1 u(k-2)+ξ(k)
and collecting x groups of fluidized bed temperature data of actual operation on site. After preprocessing such as removing outliers and the like on the previous y groups of data, a fluid bed temperature mathematical model is established as input data and output data, and the remaining z groups of data are used for model accuracy verification (x=y+z).
Parameter identification is carried out by adopting RLS, and the initial value of the RLS parameter is as follows: gn-1=1, f (k+m) =1; forgetting factor μ=0.99; a softening coefficient α=0.5; and xi (k) is white noise uniformly distributed in [ -0.1,0.1 ].
5. GUI user interface design based on MATLAB
The interactive interface shown in the following diagram is designed by using elements such as buttons, radio buttons, editable text, static text, coordinate areas, panels and the like. The GUI will generate a ". Fig" file of the user interface and a ". M" file storing the desired function for each saved user window file. The interface mainly comprises a CARIMA parameter model module, a GPC parameter module, an expected curve module, an operation control interface module and a simulation image module. The CARIMA model parameter module and the GPC parameter module mainly complete the setting of generalized predictive control parameters, the expected curve module can adjust and set curve signals, and the simulation image module comprises generalized predictive control algorithm control effects and delta U change condition curves.
Example 1
Taking a circulating fluidized bed boiler control system in a thermal power plant as an example, a specific application of the invention is given. The circulating fluidized bed boiler had a capacity of 135MW and a thermal efficiency of about 91.28%. The working flow is as follows: the coal in the coal bunker is transported to a coal feeder by a lifter through a series of processes. The coal feeder conveys coal into the circulating fluidized bed boiler furnace through the crawler belt. For the limestone system, the limestone is subjected to primary crushing and secondary crushing, and large limestone is changed into limestone powder and is conveyed into a hearth. And primary air is introduced to rapidly raise the temperature in the hearth. And secondary air is introduced to fully perform the reaction of the hearth and improve the thermal efficiency. The flue gas generated by the circulating fluidized bed boiler reaction is separated into large particles and small particles by a cyclone separator. And the large-particle flue gas is returned to the hearth through the material returning mechanism. The small-particle flue gas passes through a superheater, an economizer and an air preheater. The high temperature flue gas temperature is reduced. Enters a dust remover to separate dust from steam. And finally, the flue gas is discharged to the atmosphere from the chimney. The structure of the circulating fluidized bed boiler system is shown in fig. 1.
The system adopts a CARIMA model as a prediction model, and is written as follows:
A(z -1 )y(k)＝B(z -1 )u(k-1)+C(z -1 )ξ(k)/Δ
wherein z is -1 Is a backward operator; y (k) and u (k) represent the output and input at time k, respectively; ζ (k) represents a white noise sequence with zero mean; delta = 1-z -1 Is a differential operator. The generalized predictive control structure is shown in fig. 2.
For a single-input, single-output (SISO) discrete system, a circulating fluidized bed temperature CARIMA controlled model can be built as:
y(k)＝-a 1 y(k-1)-a 2 y(k-2)+b 0 u(k-1)+b 1 u(k-2)+ξ(k)
and collecting x groups of fluidized bed temperature data of actual operation on site. After preprocessing such as removing outliers and the like on the previous y groups of data, a fluid bed temperature mathematical model is established as input data and output data, and the remaining z groups of data are used for model accuracy verification, wherein x=y+z.
Parameter identification is carried out by adopting RLS, and the initial value of the RLS parameter is as follows: g n-1 =1, f (k+m) =1; forgetting factor μ=0.99; a softening coefficient α=0.5; xi (k) is [ -0.1,0.1)]White noise is uniformly distributed.
As can be seen from the figure, the system is basically stable in each parameter estimation value when l=250 or so. The identification result is as follows: a1 = -0.8251, a2= 0.0554, b0= 0.1254, b1= 0.2213. The CARIMA model can be obtained according to the established circulating fluidized bed boiler system, and the CARIMA model comprises the following steps:
y(k)-0.8251y(k-1)+0.0554y(k-2)＝0.1254u(k-1)+0.2213u(k-2)+ξ(k)
the model is established by using input and output data in a systematic identification method, an actual model and a modeled model can be different, and in addition, the model can be changed under the condition of disturbance in the production process, and can cause mismatching of the model, so that the accuracy of the identified model is verified.
The model is simulated in a Simulink environment. Selecting control parameters: n1=1; prediction and control time domain length n=4, nu=2; the weighting coefficient λ=0.3 is controlled. Setting the simulation step length as 100, sampling time as T=0.1s, and the discrete equation is as follows:
the simulation model and the result are shown in fig. 3 and 4.
The model is simulated under Matlab 2018a version, and control parameters are selected: n (N) 1 =1; predicting and controlling time domain length n=4, N u =2; a softening coefficient α=0.5; the weighting coefficient λ=0.3 is controlled. The simulation step k=300 is set, and the simulation result thereof is shown in fig. 5.
The invention designs the interactive interface shown in the lower graph by using elements such as buttons, radio buttons, editable text, static text, coordinate areas, panels and the like. The GUI will generate a ". Fig" file of the user interface and a ". M" file storing the desired function for each saved user window file. The interface mainly comprises a CARIMA parameter model module, a GPC parameter module, an expected curve module, an operation control interface module and a simulation image module. The CARIMA model parameter module and the GPC parameter module mainly complete the setting of generalized predictive control parameters, the expected curve module can adjust and set curve signals, and the simulation image module comprises generalized predictive control algorithm control effects and delta U change condition curves. FIG. 6 is a diagram of a generalized predictive control simulation GUI operator interface drawn by a Matlab graphical user interface.
The radio button may change the desired curve category, set the desired curve to a step signal, and input parameters into the editable text box. N (N) a 、N b Respectively A (z) -1 )、B(z -1 )；U 0 And Y 0 Is the initial value of the horizontal and vertical coordinates;P(0)＝10 6 I。
and selecting 'running' on the running control panel, simulating the system, and obtaining the generalized predictive control square wave signal tracking curve shown in fig. 7 after running.
The expected curve is set as a sine wave signal, the system is simulated, and the generalized predictive control sine wave signal tracking curve shown in fig. 8 can be obtained after the system is operated.
The expected curve is set as a step signal, the system is simulated, and the generalized predictive control step signal tracking curve shown in fig. 9 can be obtained after the system is operated.
Claims (5)
1. A method for adaptively controlling a circulating fluidized bed industrial boiler system, the method comprising modeling, wherein a predictive model of GPC employs a controlled autoregressive integral moving average (CARIMA) model; in the control strategy, firstly, an online identification and estimation model is combined according to past control input, present input and output data and predicted output data; and then rolling and optimizing the predicted output and the expected output according to a certain performance index, correcting the predicted output to correct and obtain an optimal control law, and specifically comprising the following steps:
1) The system adopts a controlled autoregressive integral moving average (CARIMA) model as a prediction model, and is written as:
A(z -1 )y(k)＝B(z -1 )u(k-1)+C(z -1 )ξ(k)/Δ
wherein z is -1 Is a backward operator; y (k) and u (k) represent the output and input at time k, respectively; ζ (k) represents a white noise sequence with zero mean; delta = 1-z -1 Is a difference operator;
in the above, n a 、n b 、n c Respectively polynomial a (z -1 )、B(z -1 )、C(z -1 ) Is the order of (2); if the system time lag is greater than zero, B (z -1 ) The coefficient of one or several items of the polynomial head is equal to zero; when the generalized predictive control is pushed, C (z -1 )＝1；
2) For system robustness enhancement, consider the effect of the current time input u (k) on future time, using the following objective function:
wherein n is the maximum predicted length; m represents the control length, and m is less than or equal to n in total; y (k+j) is the j-th output of the system prediction; w is an expected value output by an object, and a signal w (k+j) is an output reference sequence; λ (j) is a control weighting coefficient, taking a constant value greater than zero, if λ (j) =0 means that the control increment is not constrained;
for the purpose of softening control, the control is to track the reference trajectory instead of directly tracking the output to the set point, as follows:
w(k+j)＝α j y(k)+(1-α j )y r (j＝1,2,…,n)
wherein: y is r Y (k) and w (k) are respectively the set value of the system, the actually measured output value of the system and the reference track, alpha is the softening coefficient, 0<α<1；
The Diophantine equation was introduced:
1＝A(z -1 )ΔE j (z -1 )+z -j F j (z -1 )
wherein,,
and (3) sorting a forward generalized forecast equation of the controllable object:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)+E j (z -1 )ξ(k+j)
in the method, in the process of the invention,
G j (z -1 )＝B(z -1 )E j (z -1 )＝g 0 +g 1 z -1 +…+g j-1 z -j+1
neglecting the noise effect, the output predicted value at the k moment can be obtained:
the optimal output predicted value obtainable according to the above equation is represented by a vector:
in the middle of
Optimal control rate
Adopting a Recursive Least Squares (RLS) parameter identification algorithm with forgetting factors:
wherein lambda is forgetting factor, 0.95<λ<1, a step of; k (K) is a weight factor, and P (K) is a positive covariance matrix; for recursive operations, it is necessary to giveAn initial value of P (k); according to the recursive least square method, the element G in the matrix G and the vector F can be obtained 0 ，g 1 … and f (k+m).
2. The self-adaptive control method of the circulating fluidized bed industrial boiler system according to claim 1, wherein the model of the fluidized bed boiler control system is combined with a mechanism model, operation parameters and data of a circulating fluidized bed and site disturbance through Matlab, and the gas amount is selected as input u (k), and the fluidized bed temperature is output y (k); for a single-input, single-output (SISO) discrete system, a circulating fluidized bed temperature CARIMA controlled model is established:
y(k)＝-a 1 y(k-1)-a 2 y(k-2)+b 0 u(k-1)+b 1 u(k-2)+ξ(k)
collecting x groups of fluidized bed temperature data actually operated on site, performing outlier rejection pretreatment on the previous y groups of data, and then establishing a fluidized bed temperature mathematical model as input data and output data, wherein z groups of data are left for model accuracy verification, and x=y+z;
parameter identification is carried out by adopting RLS, and the initial value of the RLS parameter is as follows: g n-1 =1, f (k+m) =1; forgetting factor μ=0.99; a softening coefficient α=0.5; xi (k) is [ -0.1,0.1)]White noise is uniformly distributed.
3. The method for adaptively controlling a circulating fluidized bed industrial boiler system according to claim 1, wherein the model designs a GUI user interface based on MATLAB; designing an interactive interface by using buttons, radio buttons, editable text, static text, coordinate areas and panel elements; a radio button can change the category of a desired curve, set the desired curve as a step signal and input parameters into an editable text box;
N a 、N b respectively A (z) -1 )、B(z -1 )；U 0 And Y 0 Is the initial value of the horizontal and vertical coordinates;P(0)＝10 6 I；
the operation control panel is selected to perform 'operation', the system is simulated, and a tracking curve which is controlled by generalized prediction under the actions of different square wave signals, sine wave signals and step signals can be obtained after the operation.
4. A method of adaptively controlling a circulating fluidized bed industrial boiler system according to claim 3, wherein the interface comprises a CARIMA parameter model module, a GPC parameter module, a desired curve module, an operation control interface module, and a simulation image module.
5. The adaptive control method of a circulating fluidized bed industrial boiler system according to claim 4, wherein the CARIMA model parameter module and the GPC parameter module complete setting of generalized predictive control parameters, the expected curve module can adjust setting curve signals, and the simulated image module includes generalized predictive control algorithm control effects and Δu change condition curves.
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