CN113848722A - 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
A self-adaptive control method for an industrial boiler system of a circulating fluidized bed relates to an industrial boiler control method, the industrial circulating fluidized bed boiler has the characteristics of large disturbance, multiple parameters, strong coupling, nonlinearity and the like, and a mathematical model of the industrial circulating fluidized bed boiler is difficult to establish by a conventional control algorithm. Aiming at the problem, the invention relates to the technical field of automatic control, in particular to a method for optimizing a system by combining a generalized predictive control algorithm and online identification adaptive control, which aims to realize the adaptive control of complex industrial systems such as a circulating fluidized bed and the like, and the simulation effect of the method is obviously superior to 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 generalized predictive control in an industrial system. The GUI application design of the generalized predictive control algorithm adopted by the invention has certain practical significance in improving the automation level of a complex industrial system.
Description
Technical Field
The invention relates to a control method of an industrial boiler, in particular to a self-adaptive control method of a circulating fluidized bed industrial boiler system.
Background
In the three and four decades of the last century, from PID control to the continuous development of adaptive control strategies, industrial process control is continuously developed and strengthened, and the method is applied to numerous fields and improves social productivity and the convenience degree of daily life of people. Experts and scholars at home and abroad use a PID control method to explore a large amount of circulating fluidized bed boilers.
For example, boiler temperature control research based on PID algorithm (Wansui, automated application, 2019(05):35-36+ 47) [ (1) ], and application of fuzzy PID control algorithm in electric boiler temperature control system (New, information technology and informatization, 2019(08): 118-120) [ (2) ]. PID is the most traditional and conventional control algorithm in a distributed control system, and PID control is mostly adopted in the actual production operation process of a factory.
However, the accuracy requirement of the controlled object model is high, but when the working condition changes, the PID method is difficult to meet the requirement of system control. The circulating fluidized bed boiler has the characteristics of time variation, multiple parameters, strong coupling, nonlinearity and the like, a mathematical model of the circulating fluidized bed boiler is difficult to establish, and the conventional control theory is used for solving the problem that the complex industrial system cannot achieve the expected effect. And most circulating fluidized bed boiler systems have low automation level, and the control mode needs to be improved. Therefore, the improvement of the bed temperature control of the circulating fluidized bed boiler is significant to the actual operation of the boiler.
Generalized Predictive Control (GPC) is a type of predictive control that implements adaptive control through online parameter identification. For example, the application of the generalized predictive control in the steam temperature of a thermal power generation boiler based on improvement (Wangsheng, chapter, rock, Chifeng academy, 2019,35(12): 49-53) [ 3 ], and the simulation research of the generalized predictive control algorithm and the application thereof in the steam temperature control of the boiler (Liang Tao, Ku, Shandong power technology, 2017,44(05): 54-57) [ 4 ]. In the control strategy, firstly, an online identification and estimation model is combined according to the past control input, the current input and output data and the predicted output data. And then, the predicted output and the expected output are optimized in a rolling mode according to certain performance indexes, and the predicted output is corrected so as to correct and obtain an optimal control law.
Disclosure of Invention
The invention aims to provide a self-adaptive control method of a circulating fluidized bed industrial boiler system, which combines a generalized predictive control method and online identification self-adaptive control to optimize an industrial system, realizes self-adaptive control of complex industrial systems such as a circulating fluidized bed and the like, and has important significance for improving the automation level of the complex industrial system.
The purpose of the invention is realized by the following technical scheme:
a circulating fluidized bed industrial boiler system adaptive control method, the method comprises establishing a model, wherein a GPC prediction model adopts a controlled autoregressive integrated moving average (CARIMA) model; in the control strategy, firstly, an online identification and estimation model is combined according to the past control input, the current input and output data and the predicted output data; and then the predicted output and the expected output are optimized according to a certain performance index in a rolling way, and the predicted output is corrected so as to correct and obtain the optimal control law, wherein the method specifically comprises the following steps:
1) the system uses a controlled autoregressive integrated moving average (CARIMA) model as a prediction model, written as:
wherein z-1 is a backward shift operator; y (k), u (k) represent the output and input at time k, respectively; ξ (k) represents a white noise sequence with a mean of zero; Δ =1-z-1 is a difference operator;
in the above formula, na, nb and nc are respectively the order of polynomial A (z-1), B (z-1) and C (z-1); if the system skew is greater than zero, then one or more coefficients at the beginning of the B (z-1) polynomial are equal to zero; when generalized predictive control is inferred, for convenience of calculation, C (z-1) = 1;
2) for system robustness enhancement, taking into account the effect of the current time input u (k) on the future time, the following objective function is employed:
wherein n is the maximum prediction length; m represents the control length, and m is always less than or equal to n; y (k + j) is the jth step output of the system prediction; w is the expected value of the object output, and the signal w (k + j) is the reference sequence of the output; λ (j) is a control weighting coefficient, and takes a constant value larger than zero, if λ (j) =0 means that the control increment is not constrained;
for the purpose of the soft control, the control is not to make the output directly follow the set value, but follow the reference trajectory as follows:
in the formula: yr, y (k) and w (k) are respectively a set value of the system, a measured output value of the system and a reference track, alpha is a softening coefficient, and 0< alpha < 1;
introduce the expression of drop pattern (Diphantine):
wherein the content of the first and second substances,
the forward generalized forecast equation of the controlled object can be obtained by sorting:
in the formula (I), the compound is shown in the specification,
neglecting the influence of noise, the output predicted value at the k moment can be obtained:
the optimal output prediction value obtained according to the above formula is represented by a vector:
in the formula
Optimum control rate
Adopting a recursive least square method (RLS) parameter identification algorithm with forgetting factors:
wherein λ is forgetting factor, and has a value of 0.95<λ<1; k (k) is a weight factor, and P (k) is a positive definite covariance matrix; in order to perform the recursion operation, it is necessary to give
Initial value of P (k); according to the recursive least squares method, the elements G0, G1, …, and F (k + m) in matrix G and vector F can be obtained.
According to the self-adaptive control method of the circulating fluidized bed industrial boiler system, the model of the fluidized bed boiler control system selects the coal gas amount as an input u (k) and the fluidized bed temperature as an output y (k) by combining Matlab with a mechanism model, operation parameters, data and field disturbance of a circulating fluidized bed; for a single-input single-output (SISO) discrete system, establishing a CARIMA controlled model of the temperature of a circulating fluidized bed:
acquiring x groups of fluidized bed temperature data actually operated on site, performing pretreatment such as removing abnormal values on the previous y groups of data, establishing a fluidized bed temperature mathematical model as input data and output data, and using the remaining z groups of data for model accuracy verification, wherein x = y + z;
RLS is adopted for parameter identification, and the initial value of the RLS parameter is as follows: gn-1=1, f (k + m) = 1; forgetting factor μ = 0.99; softening coefficient α = 0.5; ξ (k) is [ -0.1,0.1] evenly distributed white noise.
In the self-adaptive control method of the circulating fluidized bed industrial boiler system, the model design is based on a GUI user interface of MATLAB; designing an interactive interface by using buttons, radio buttons, editable texts, static texts, coordinate areas and panel elements; the radio button can change the category of the expected curve, set the expected curve as a step signal and input the parameters into the editable text box;
na and Nb are respectively A (z-1) and B (z-1); u0 and Y0 are initial values of horizontal and vertical coordinates;
；
the 'operation' is selected on the operation control panel, the system is simulated, and the tracking curve of the 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 self-adaptive control method of the circulating fluidized bed industrial boiler system comprises a CARIMA parameter model module, a GPC parameter module, a desired curve module, an operation control interface module and a simulation image module.
The CARIMA model parameter module and the GPC parameter module complete the setting of generalized predictive control parameters and expect a desired curveThe line module can adjust and set curve signals, and the simulation image module comprises generalized predictive control algorithm control effects and deltaUCurve of change.
The invention has the advantages and effects that:
1. the invention adopts the combination of a generalized predictive control method and on-line identification adaptive control to optimize the system and aims to realize the 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 the generalized predictive control of 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 great significance for improving the automation level of a complex industrial system.
2. The invention provides a GUI application design of generalized predictive control in a circulating fluidized bed industrial boiler system. The prediction control self-corrector is based on a CARIMA model, adopts long-time optimized performance indexes, combines an identification and self-correction mechanism, has the characteristics of strong robustness, low model requirement and the like, and has wide application range. The defects in self-adaptation such as generalized minimum variance, pole allocation and the like can be overcome.
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 diagram of an implicit generalized predictive controller Simulink model;
FIG. 4 is a control diagram of implicit generalized prediction under a step signal;
FIG. 5 is a GPC control effect of the recognition model under a square wave signal;
FIG. 6 is a generalized predictive control simulation GUI operational interface diagram;
FIG. 7 is a generalized predictive control square wave signal tracking graph;
FIG. 8 is a generalized predictive control sine wave signal tracking graph;
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 prediction control theory, a basic model for describing the dynamic behavior of the system is needed to become a prediction model. It should have a predictive function, i.e. be able to predict the future output value of the system based on historical data and future inputs to the system.
1. The system adopts a CARIMA model as a prediction model, wherein the CARIMA model 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:
wherein z-1 is a backward shift operator; y (k), u (k) represent the output and input at time k, respectively; ξ (k) represents a white noise sequence with a mean of zero; Δ =1-z-1 is the difference operator.
In the above formula, na, nb and nc are the order of polynomials A (z-1), B (z-1) and C (z-1), respectively. If the system skew is greater than zero, then one or more coefficients at the beginning of the B (z-1) polynomial are equal to zero. Clarke et al, when we come to generalized predictive control, let C (z-1) =1 for ease of calculation.
2. For system robustness enhancement, taking into account the effect of the current time input u (k) on the future time, the following objective function is employed:
wherein n is the maximum prediction length; m represents the control length, and m is always less than or equal to n; y (k + j) is the jth step output of the system prediction; w is the expected value of the object output, and the signal w (k + j) is the reference sequence of the output; λ (j) is a control weighting coefficient, and takes a constant value greater than zero, if λ (j) =0 means that the control increment is unconstrained.
For the purpose of the soft control, the control is not to make the output directly follow the set value, but follow the reference trajectory as follows:
in the formula: yr, y (k) and w (k) are respectively a set value of the system, a measured output value of the system and a reference track, alpha is a softening coefficient, and 0< alpha < 1.
When the output y (k + j) of the j step is predicted by rolling optimization, a self-correcting algorithm of Generalized Minimum Variance Control (GMVC) is used to obtain the optimal prediction. The expression "is introduced here:
wherein the content of the first and second substances,
and (4) combining two sides of the formula (1) with Ej (z-1) zj, and sorting to obtain a forward generalized forecast equation of the controlled object:
in the formula (I), the compound is shown in the specification,
neglecting the influence of noise, the output predicted value at the k moment can be obtained:
the optimal output prediction value obtained according to the above formula is represented by a vector:
in the formula
3. Optimum control rate
If order
Equation (3) can be expressed as a matrix vector:
will optimize the predicted value
Substituting formula (9) into (13) instead of Y, and reacting
The following can be obtained:
Δ U, W, F are control increments, tracking reference trajectories, and prediction vectors, respectively. The control quantity input at the next moment is:
in the formula, gT is the first row of (GTG + λ I) -1 GT. To solve the optimal control rate Δ U, a matrix G and a prediction vector F need to be identified by input and output according to a prediction equation and by using an implicit self-correction method.
M parallel predictors can be obtained according to equation (9):
Wherein the model parameters can be represented by vectors
And data parameters
Represents:
all output expressions can be expressed in matrix form as follows:
or
Adopting a recursive least square method (RLS) parameter identification algorithm with forgetting factors:
wherein, λ is forgetting factor, and 0.95< λ < 1. K (k) is a weight factor, and P (k) is a positive covariance matrix.
Generally, in order to perform a recursion operation, it is necessary to give
And initial value of P (k). According to the recursive least squares method, the elements G0, G1, …, and F (k + m) in matrix G and vector F can be obtained.
4. Identification of parameters
When designing the optimization control system of the circulating fluidized bed boiler, firstly, a mathematical model of the industrial boiler is established. However, the parameters are difficult to determine, and the subsystems of the control system are coupled with each other, so that the mathematical relationship between the input and the output cannot be accurately obtained. For these control systems with excessively complex operation mechanisms, experimental modeling may be performed by applying a certain pulse signal to the system as an input, recording the output response of the system, and controlling the change in the input to bring the output to 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 working state of a general automatic control system, most industrial nonlinear objects can be described by using a linear model in a control range. By combining Matlab with a mechanism model, operation parameters and data of the circulating fluidized bed and field disturbance, the amount of the coal gas is selected as input u (k), and the bed temperature of the fluidized bed is selected as output y (k). For a single input, single output (SISO) discrete system, the circulating fluidized bed temperature CARIMA controlled model can be established as:
and acquiring x groups of fluidized bed temperature data of actual operation on site. And (3) after preprocessing such as eliminating abnormal values and the like is carried out on the front y groups of data, establishing a bed temperature mathematical model of the fluidized bed as input data and output data, and remaining z groups of data for model accuracy verification (x = y + z).
RLS is adopted for parameter identification, and the initial value of the RLS parameter is as follows: gn-1=1, f (k + m) = 1; forgetting factor μ = 0.99; softening coefficient α = 0.5; ξ (k) is [ -0.1,0.1] evenly distributed white noise.
5. MATLAB-based GUI user interface design
And designing an interactive interface shown in the drawing by using elements such as buttons, radio buttons, editable texts, static texts, coordinate areas, panels and the like. The GUI will generate a ". fig" file for the user interface and a ". m" file that stores the required functions for each saved user window file. The interface mainly comprises a CARIMA parameter model module, a GPC parameter module, a desired curve module, an operation control interface module and a simulation image module. The CARIMA model parameter module and the GPC parameter module are mainly used for setting 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.
Example 1
A specific application of the present invention is given by taking a circulating fluidized bed boiler control system in a thermal power plant as an example. The circulating fluidized bed boiler has a capacity of 135MW and a thermal efficiency of about 91.28%. The working process comprises the following steps: the coal from the bunker is processed in a series and transported to the coal feeder by an elevator. The coal feeder conveys coal into a hearth of the circulating fluidized bed boiler through a crawler belt. For the limestone system, limestone is subjected to primary crushing and secondary crushing, and large limestone is changed into limestone powder and conveyed into a hearth. And introducing primary air to rapidly raise the temperature in the hearth. Secondary air is introduced to fully carry out the reaction in the hearth and improve the heat efficiency. The smoke generated by the reaction of the circulating fluidized bed boiler 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 temperature of the high-temperature flue gas is reduced. And the dust enters a dust remover to separate dust from steam. And finally, discharging the flue gas from the chimney to the atmosphere. The circulating fluidized bed boiler system is constructed as shown in fig. 1.
The system adopts a CARIMA model as a prediction model and is written as follows:
wherein z-1 is a backward shift operator; y (k), u (k) represent the output and input at time k, respectively; ξ (k) represents a white noise sequence with a mean of zero; Δ =1-z-1 is the difference operator. The generalized predictive control architecture is shown in fig. 2.
For a single input, single output (SISO) discrete system, the circulating fluidized bed temperature CARIMA controlled model can be established as:
and acquiring x groups of fluidized bed temperature data of actual operation on site. And (3) after preprocessing the previous y groups of data such as rejecting abnormal values and the like, establishing a bed temperature mathematical model of the fluidized bed as input data and output data, and remaining z groups of data for model accuracy verification, wherein x = y + z.
RLS is adopted for parameter identification, and the initial value of the RLS parameter is as follows: gn-1=1, f (k + m) = 1; forgetting factor μ = 0.99; softening coefficient α = 0.5; ξ (k) is [ -0.1,0.1] evenly distributed white noise.
As can be seen from the figure, the estimated values of the parameters of the system are almost stable when L =250 or so. The identification result is: a1= -0.8251, a2=0.0554, b0=0.1254, b1= 0.2213. The CARIMA model can be obtained from the established circulating fluidized bed boiler system as follows:
the model is established by a system identification method by using input and output data, an actual model and the established model may be different, and the model may change under the condition of disturbance in the production process, which may cause model mismatching, so that the accuracy of the identified model needs to be verified.
The model was simulated in a Simulink environment. Selecting control parameters: n1= 1; prediction and control time domain lengths N =4, Nu = 2; the weighting coefficient λ =0.3 is controlled. Setting the simulation step length as 100, the sampling time as T =0.1s, and the discrete equation is as follows:
the simulation model and the results are shown in fig. 3 and 4.
The model is simulated under the Matlab 2018a version, and control parameters are selected: n1= 1; prediction and control time domain lengths N =4, Nu = 2; softening coefficient α = 0.5; the weighting coefficient λ =0.3 is controlled. The simulation step k =300 is set, and the simulation result is shown in fig. 5.
The invention designs the interactive interface shown in the next drawing by using elements such as buttons, radio buttons, editable texts, static texts, coordinate areas, panels and the like. The GUI will generate a ". fig" file for the user interface and a ". m" file that stores the required functions for each saved user window file. The interface mainly comprises a CARIMA parameter model module, a GPC parameter module, a desired curve module, an operation control interface module and a simulation image module. The CARIMA model parameter module and the GPC parameter module are mainly used for setting 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. Fig. 6 is a generalized predictive control simulation GUI operation interface diagram drawn by Matlab graphical user interface.
The radio button may change the desired curve category, set the desired curve to a step signal, and enter the parameter into the editable text box. Na and Nb are respectively A (z-1) and B (z-1); u0 and Y0 are initial values of horizontal and vertical coordinates;
。
and selecting 'operation' on the operation control panel, simulating the system, and obtaining the generalized predictive control square wave signal tracking curve shown in fig. 7 after operation.
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 operation.
And setting the expected curve as a step signal, simulating the system, and obtaining the generalized predictive control step signal tracking curve shown in fig. 9 after operation.
Claims (5)
1. A circulating fluidized bed industrial boiler system adaptive control method, the method comprising modeling, characterized in that a prediction model of GPC employs a controlled autoregressive integrated moving average (CARIMA) model; in the control strategy, firstly, an online identification and estimation model is combined according to the past control input, the current input and output data and the predicted output data; and then the predicted output and the expected output are optimized according to a certain performance index in a rolling way, and the predicted output is corrected so as to correct and obtain the optimal control law, wherein the method specifically comprises the following steps:
1) the system uses a controlled autoregressive integrated moving average (CARIMA) model as a prediction model, written as:
wherein z-1 is a backward shift operator; y (k), u (k) represent the output and input at time k, respectively; ξ (k) represents a white noise sequence with a mean of zero; Δ =1-z-1 is a difference operator;
in the above formula, na, nb and nc are respectively the order of polynomial A (z-1), B (z-1) and C (z-1); if the system skew is greater than zero, then one or more coefficients at the beginning of the B (z-1) polynomial are equal to zero; when generalized predictive control is inferred, for convenience of calculation, C (z-1) = 1;
2) for system robustness enhancement, taking into account the effect of the current time input u (k) on the future time, the following objective function is employed:
wherein n is the maximum prediction length; m represents the control length, and m is always less than or equal to n; y (k + j) is the jth step output of the system prediction; w is the expected value of the object output, and the signal w (k + j) is the reference sequence of the output; λ (j) is a control weighting coefficient, and takes a constant value larger than zero, if λ (j) =0 means that the control increment is not constrained;
for the purpose of the soft control, the control is not to make the output directly follow the set value, but follow the reference trajectory as follows:
in the formula: yr, y (k) and w (k) are respectively a set value of the system, a measured output value of the system and a reference track, alpha is a softening coefficient, and 0< alpha < 1;
introduce the expression of drop pattern (Diphantine):
wherein the content of the first and second substances,
the forward generalized forecast equation of the controlled object can be obtained by sorting:
in the formula (I), the compound is shown in the specification,
neglecting the influence of noise, the output predicted value at the k moment can be obtained:
the optimal output prediction value obtained according to the above formula is represented by a vector:
in the formula
Optimum control rate
Adopting a recursive least square method (RLS) parameter identification algorithm with forgetting factors:
wherein λ is forgetting factor, and has a value of 0.95<λ<1; k (k) is a weight factor, and P (k) is a positive definite covariance matrix; in order to perform the recursion operation, it is necessary to give
Initial value of P (k); according to the recursive least squares method, the elements G0, G1, …, and F (k + m) in matrix G and vector F can be obtained.
2. The adaptive control method of a circulating fluidized bed industrial boiler system according to claim 1, wherein the model of the fluidized bed boiler control system selects the amount of coal gas as input u (k) and the bed temperature of the fluidized bed as output y (k) by Matlab in combination with the mechanism model, operation parameters and data of the circulating fluidized bed and field disturbance; for a single-input single-output (SISO) discrete system, establishing a CARIMA controlled model of the temperature of a circulating fluidized bed:
acquiring x groups of fluidized bed temperature data actually operated on site, performing pretreatment such as removing abnormal values on the previous y groups of data, establishing a fluidized bed temperature mathematical model as input data and output data, and using the remaining z groups of data for model accuracy verification, wherein x = y + z;
RLS is adopted for parameter identification, and the initial value of the RLS parameter is as follows: gn-1=1, f (k + m) = 1; forgetting factor μ = 0.99; softening coefficient α = 0.5; ξ (k) is [ -0.1,0.1] evenly distributed white noise.
3. The adaptive control method for a circulating fluidized bed industrial boiler system according to claim 1, wherein the model design is based on a GUI user interface of MATLAB; designing an interactive interface by using buttons, radio buttons, editable texts, static texts, coordinate areas and panel elements; the radio button can change the category of the expected curve, set the expected curve as a step signal and input the parameters into the editable text box;
na and Nb are respectively A (z-1) and B (z-1); u0 and Y0 are initial values of horizontal and vertical coordinates;
；
the 'operation' is selected on the operation control panel, the system is simulated, and the tracking curve of the generalized predictive control under the action of different signals (square wave signals, sine wave signals and step signals) can be obtained after the operation.
4. The adaptive control method for 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 operational 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,the CARIMA model parameter module and the GPC parameter module complete the setting of the generalized predictive control parameters, the expected curve module can adjust and set curve signals, and the simulation image module comprises the control effect and delta of the generalized predictive control algorithmUCurve of change.
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Citations (4)
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| CN102494336A (en) * | 2011-12-16 | 2012-06-13 | 浙江大学 | Combustion process multivariable control method for CFBB (circulating fluidized bed boiler) |
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