Methods, devices, equipment and storage media for controlling outlet temperature of heating furnace

Abstract

This invention discloses a memory, a method, apparatus, and device for controlling the outlet temperature of a heating furnace. The nonlinear predictive control method includes: presetting a setpoint for the nonlinear predictive controller; constructing an object model group for the nonlinear control system through model identification; generating the dynamic equations of the object model group and calculating the disturbance characteristics of the object model group based on the dynamic equations; obtaining the actual output measurement value and state estimate value of the nonlinear control system at the current moment; calculating the optimal state estimate value of the nonlinear control system at the next moment using an extended Kalman filter; obtaining the corresponding switching function based on the optimal state estimate value; substituting the switching function into the dynamic equations of the object model group and obtaining the optimal solution using a sliding mode control algorithm; and using the optimal solution as the input to the object model group. This invention can effectively control the outlet temperature of a heating furnace, improving its robustness and timeliness.

Description

Technical Field

[0001] This invention relates to the field of process control, and in particular to a method, apparatus, equipment, and storage medium for controlling the outlet temperature of a heating furnace. Background Technology

[0002] In existing technologies, the outlet temperature control system of a heating furnace can maintain a stable outlet temperature by adjusting the opening of the fuel gas flow regulating valve to avoid production accidents. Traditional cascade control uses a PID controller, which determines the current control input based on the deviation between the current and past output measurements and the setpoint.

[0003] The inventors discovered through research that the existing technology has at least the following defects:

[0004] The cascade control system consisting of fuel gas flow rate and furnace outlet temperature is subject to complex disturbances and has very high control requirements. Existing nonlinear predictive control methods have poor timeliness and cannot effectively control in a timely manner when unmeasurable disturbances occur, which can easily lead to large fluctuations in the furnace outlet temperature in existing devices. Summary of the Invention

[0005] The main objective of this invention is to achieve timely and effective control of the furnace outlet temperature using a nonlinear predictive control method.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention discloses a method for controlling the outlet temperature of a heating furnace, comprising:

[0008] S11. The preset value of the nonlinear predictive controller in the nonlinear control system; the preset value includes a reasonable range of the furnace outlet temperature;

[0009] S12. Construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the regulating valve for adjusting the fuel gas flow rate; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0010] S13. Generate the dynamic equations of the object model group, and treat the changes in model parameters as disturbances, and calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve;

[0011] S14. Obtain the actual output measurement value and state estimate value of the nonlinear control system at the current moment, respectively; the method for obtaining the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment;

[0012] S15. Using an extended Kalman filter, the optimal state estimate of the nonlinear control system at the next moment is calculated recursively based on the actual output measurement value and the state estimate value.

[0013] S16. Obtain the corresponding switching function based on the optimal state estimate;

[0014] S17. Substitute the switching function into the dynamic equation of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0015] Preferably, in this invention, the disturbance further includes:

[0016] The composition of the fuel gas, the inlet flow rate of the heated material, and the temperature, or any combination thereof.

[0017] Preferably, in this invention, the mathematical model used to describe the object model group includes:

[0018] n-dimensional vector nonlinear function: X k =f[X k-1 u k-1 ,k-1,p k-1 ],as well as,

[0019] m-dimensional vector nonlinear function: Z k =h[X k u k ,k]

[0020] Among them, X k The state variable u represents the nonlinear function. k p represents the input variable of the nonlinear function. k Z represents the model parameter variable of a nonlinear function. k This represents the output variable of a nonlinear function.

[0021] Preferably, in this invention, calculating the perturbation characteristics of the object model group includes:

[0022] At preset time intervals, the first-order Taylor expansion of the state space model of the object model group is performed multiple times at the nominal model parameter points, with the model parameters as variables, to calculate the statistical characteristics of the disturbance.

[0023] Preferably, in this invention, the step of performing a first-order Taylor expansion of the state-space model of the object model group multiple times at the nominal model parameter points with model parameters as variables, according to a preset time interval, and calculating the statistical characteristics of the disturbance, includes:

[0024] Assuming that the parameter variables of the object model group follow a normal distribution, the changes in the model parameters of the object model group are equivalent to disturbances, and the statistical characteristics of the disturbances are calculated.

[0025] Preferably, in this invention, the step of calculating the optimal state estimate of the nonlinear control system at the next moment using an extended Kalman filter, based on the actual output measurement and the state estimate, in a recursive manner includes:

[0026] S21. Obtain the optimal state estimate of the nonlinear control system at time k-1.

[0027] S22. Obtain the actual output measurement value {y} of the nonlinear control system at time k by detection. k}, and at the same time through Obtain the state estimate of the nonlinear control system at time k.

[0028] S23, using the actual output measurement value {y} k} for the state estimate After correction, the optimal state estimate of the nonlinear control system at time k is obtained.

[0029] Preferably, in this invention, the switching function includes: in, is the optimal state estimate; C is a constant matrix.

[0030] Preferably, in this invention, the step of substituting the switching function into the dynamic equations of the object model group and obtaining the optimal solution according to the sliding mode control algorithm includes:

[0031] The dynamic equations of the object model group include:

[0032] X k =f[X k-1 u k-1 ,k-1,p k-1 ]+E[w k-1 ]

[0033] Z k =h[X k u k ,k]

[0034] The performance optimization index of the nonlinear control system is:

[0035] The constraints of the dynamic equations include:

[0036] X k ∈X, i = 0, 1, ..., N-1

[0037] u k ∈U, i=0,1,...,N-1

[0038]

[0039] ρ∈[0,1)

[0040] Where ρ∈[0,1) is the contraction rate parameter; the weighting matrix It is a positive definite symmetric matrix; the state constraint set X and the input constraint set U; a k For switching functions; a (0|k) Let a be the zeroth switching function at time k. (N|k) Let u(·) be the Nth switching function at time k, and u(·) be the control sequence.

[0041] Preferably, in this invention, the step of substituting the switching function into the dynamic equations of the object model group and obtaining the optimal solution according to the sliding mode control algorithm includes the following steps:

[0042] S31. Given an initial state x(0), N is the prediction time domain, the shrinkage rate ρ∈[0,1), a state constraint set X, and an input constraint set U; furthermore, Q>0, R>0,

[0043] S32. Let k = 0;

[0044] S33. Based on the dynamic equations of the object model group, the optimal state estimate at the current time k. Obtain the corresponding switching function a k ;

[0045] S34. Change the switching function a at time k. k Substituting the dynamic equations of the object model group; and using the performance optimization index and the constraints, the optimal control sequence u is obtained according to the sliding mode predictive control algorithm. * (·)={u * (0|k), ..., u * (N-1|k)};Determine the control action u from the optimal control sequence. * (0|k) is the optimal solution at time k;

[0046] S35. When k is less than the preset value, let k = k + h, and return to step S33; where h is the number of time intervals, and 1 ≤ h ≤ N;

[0047] S36. When k reaches the preset value, the calculation for finding the optimal solution ends.

[0048] Preferably, in this invention, the method further includes: determining the optimal value of the interval time number h, including the step of:

[0049] S41 Based on the optimal state estimate at the current moment Let h = N, and then use the performance optimization index of the nonlinear control system. Solve the optimization problem;

[0050] When S42 cannot obtain the corresponding optimal performance indicators When h = h-1, return to step S41;

[0051] S43 can achieve the corresponding optimal performance indicators When the current h value is determined, it is set as the optimal value for the interval time h.

[0052] In another aspect of the invention, a furnace outlet temperature control device is also provided, comprising:

[0053] A preset unit is used to preset the setpoint of the nonlinear predictive controller in the nonlinear control system; the setpoint includes a reasonable range of the furnace outlet temperature.

[0054] The grouping unit is used to construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the regulating valve for adjusting the fuel gas flow rate; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0055] The characteristic calculation unit is used to generate the dynamic equations of the object model group, and to treat the changes in model parameters as disturbances, and to calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve;

[0056] The numerical acquisition unit is used to acquire the actual output measurement value and the state estimate value of the nonlinear control system at the current moment, respectively; the method for acquiring the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment;

[0057] The correction unit is used to calculate the optimal state estimate of the nonlinear control system at the next moment in a recursive manner based on the actual output measurement value and the state estimate value through an extended Kalman filter.

[0058] A switching function generation unit is used to obtain the corresponding switching function based on the optimal state estimate.

[0059] The result generation unit is used to input the switching function into the dynamic equations of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0060] Preferably, in this invention, the disturbance further includes:

[0061] The composition of the fuel gas, the inlet flow rate of the heated material, and the temperature, or any combination thereof.

[0062] In another aspect of this invention, a heating furnace outlet temperature control device is also provided, comprising:

[0063] Memory, used to store computer programs;

[0064] A processor is used to invoke and execute the computer program to implement the various steps of the furnace outlet temperature control method as described in any of the preceding claims.

[0065] In another aspect of the present invention, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the various steps of the furnace outlet temperature control method as described in any of the preceding claims.

[0066] Beneficial effects

[0067] Because the cascade control system formed by the fuel gas flow rate and the furnace outlet temperature is subject to complex disturbances and has very high control requirements, this invention adopts a furnace outlet temperature control method based on nonlinear control technology to achieve the ideal control effect for the furnace outlet temperature. Specifically:

[0068] This invention first presets a reasonable range of furnace outlet temperature as the setpoint for the nonlinear predictive controller in the nonlinear control system. Furthermore, this invention 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 characteristics of the object model group. Next, it uses the extended Kalman filter algorithm to obtain the optimal state estimate of the object model group. In this invention, the optimal state estimate is obtained as follows: taking a nonlinear control system at a certain time (time k) as an example, on the one hand, the state estimate of the nonlinear control system at time k is obtained at the previous time (k-1); on the other hand, the actual output measurement value of the nonlinear control system at time k is also obtained. Then, the extended Kalman filter is used to correct the state estimate based on the actual output measurement value to generate the optimal state estimate of the nonlinear control system.

[0069] Furthermore, this invention combines sliding mode control with predictive control, using a switching function as a new variable in the sliding mode predictive control for optimization. The objective function uses the switching function and control input as optimization variables, with added constraints on the switching function. By minimizing the designed objective function, the optimal control action can be obtained. This invention uses the switching function in sliding mode control as a new variable and penalizes the deviation of the switching function and control input from the equivalent control in the objective function. Therefore, a zero objective function signifies that the system state has reached the sliding surface, and the control law is equal to the equivalent control. Because this invention combines the advantages of both sliding mode control and predictive control, it designs a sliding mode predictive controller with stability guarantees. This controller maintains stability even under large unmeasurable disturbances in the nonlinear control system, effectively reducing the impact of unmeasurable disturbances on the predictive control effect of the nonlinear control system and improving its robustness and timeliness. Thus, even when the nonlinear control system is subjected to large disturbances, its stability can still be improved, effectively reducing safety hazards caused by unstable furnace outlet temperatures.

[0070] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it according to the contents of the specification, and to make the above and other objectives, technical features and advantages of this application easier to understand, one or more preferred embodiments are listed below and described in detail with reference to the accompanying drawings. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a schematic diagram of the steps of the furnace outlet temperature control method described in this invention;

[0073] Figure 2 This is a schematic diagram of the structure of the heating furnace outlet temperature control system described in this invention;

[0074] Figure 3 This is a probability distribution curve of the model parameter variables in the heating furnace outlet temperature control system described in this invention;

[0075] Figure 4 This is a schematic diagram of the structure of the furnace outlet temperature control device described in this invention;

[0076] Figure 5 This is a schematic diagram of the structure of the heating furnace outlet temperature control device described in this invention. Detailed Implementation

[0077] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Example 1

[0079] Because the cascade control system formed by the fuel gas flow rate and the furnace outlet temperature is subject to complex disturbances and has very high control requirements, in order to effectively reduce the impact of unmeasurable disturbances on the predictive control performance of the nonlinear control system and improve its robustness and timeliness, reference is made to... Figure 1 and Figure 2 This invention provides a method for controlling the outlet temperature of a heating furnace, comprising:

[0080] S11. The preset value of the nonlinear predictive controller in the nonlinear control system; the preset value includes a reasonable range of the furnace outlet temperature;

[0081] The purpose of this invention is to effectively control the temperature at the outlet of the heating furnace. Therefore, a reasonable range for the temperature at the outlet of the heating furnace must first be preset. When the temperature at the outlet of the heating furnace is within the reasonable range, it is considered that the temperature at the outlet of the heating furnace is in a safe temperature range and will not cause any production safety hazards.

[0082] In practical applications, the reasonable range of temperature at the outlet of the heating furnace can be set by those skilled in the art according to actual needs, and no specific limitation is made here.

[0083] S12. Construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the fuel gas flow regulating valve; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0084] In this embodiment of the invention, an object model group for the nonlinear control system is defined, and the object models PID 21, actuator 22, first controlled object 23, and 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 can generate the input variable u of the object model group.

[0085] The mathematical model used to describe the object model group in this embodiment of the invention can be specifically represented as follows:

[0086] n-dimensional vector nonlinear function: X k =f[X k-1 u k-1 ,k-1,p k-1 ], and, the m-dimensional vector nonlinear function: Z k =h[X k u k ,k].

[0087] Among them, X k The state variable u represents the nonlinear function. k p represents the input variable of the nonlinear function. k Z represents the model parameter variable of a nonlinear function. k This represents the output variable of a nonlinear function.

[0088] S13. Generate the dynamic equations of the object model group, and treat the changes in model parameters as disturbances, and calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve;

[0089] Then, taking the object model group (including PID sub-object model, actuator sub-object model and each controlled object sub-object model) as a whole, the corresponding dynamic equation is generated, and the disturbance characteristics of the object model group are calculated based on the dynamic equation.

[0090] In practical applications, the specific method for calculating the perturbation characteristics of the object model group can be to perform a first-order Taylor expansion of the state-space model of the object model group multiple times at the nominal model parameter points, using the model parameters as variables, to calculate the statistical characteristics of the perturbation. Preferably, it can be assumed that the parameter variables of the object model group follow a normal distribution, and the changes in the parameters of the object model group can be equated to perturbations, and the statistical characteristics of the perturbations can be calculated.

[0091] In practical applications, the model parameters can be the furnace outlet temperature and fuel gas flow rate measured or collected under a nonlinear control system.

[0092] In practical applications, disturbances can be the pressure value before the fuel gas valve measured or collected under a nonlinear control system, or they can include one or any combination of the components of the fuel gas, the inlet flow rate and temperature of the heated material.

[0093] In this embodiment of the invention, the specific way to equate the change in model parameters to a disturbance can be:

[0094] Assumption Figure 3 The model parameter variables (i.e., furnace outlet temperature and fuel gas flow rate) follow a normal distribution. The model parameter variables are represented by a vector P, and their mean and variance are as follows:

[0095] E[P]=P0

[0096] E[(P-P0)(P-P0) T ] = Q P

[0097] In the formula, P0 represents the nominal model parameter value, Q P Let P represent the covariance matrix of the model parameters. i，plant The model parameter distribution is as follows Figure 3 As shown.

[0098] Figure 3 The dashed envelope represents the nominal model parameter P. i，0 The probability distribution curve, the actual model parameter P i，plant With nominal model parameter P i，0 The relationship can be represented as P i，plant =P i，0 +δ i That is, the change is δ i . Figure 3The horizontal axis represents the model parameter values, and the vertical axis represents the probability density of the corresponding model parameter values.

[0099] To estimate the system state after changes in model parameters, these changes can be treated as a disturbance. A first-order Taylor expansion is then performed on the state-space model of the object model group at the nominal model parameter points, using the model parameters as variables. To ensure the output of the predictive model approximates the output of the actual system, the statistical properties of the disturbance need to be calculated based on the uncertainty information of the model parameters.

[0100] In the nominal model parameter P i，0 A first-order Taylor expansion of the discrete state-space model (i.e., the mathematical model of the object model group used to describe the nonlinear control system) yields the following dynamic system equations:

[0101]

[0102]

[0103] The above formula can be simplified to

[0104] X k =f[X k-1 u k-1 ,k-1,p k-1 ]+w k-1

[0105] Z k =h[X k u k ,k]+v k ;

[0106] Among them, w k-1 v k It is a disturbance equivalent to the uncertainty of the model parameters.

[0107]

[0108]

[0109] S14. Obtain the actual output measurement value and state estimate value of the nonlinear control system at the current moment, respectively; the method for obtaining the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment;

[0110] In this embodiment of the invention, not only is it necessary to calculate the state estimate of the nonlinear control system, but also to collect the actual output measurement value at the same time. Specifically, for time k, on the one hand, the state estimate of the nonlinear control system at time k can be generated at time k-1, and on the other hand, it is also necessary to collect the actual output measurement value of the controlled object (i.e., the outlet temperature of the heating furnace) at time k. The method for obtaining the state estimate at time k may include: at time k-1 (i.e., the time before the current time), using the input u acting on the object model group as a parameter, calculating and generating the state estimate of the nonlinear control system at time k based on the dynamic equation and its disturbance characteristics.

[0111] S15. Using an extended Kalman filter, the optimal state estimate of the nonlinear control system at the next moment is calculated recursively based on the actual output measurement value and the state estimate value.

[0112] In this embodiment of the invention, an extended Kalman filter 03 is also provided to obtain the state estimate of the object model group. The state estimate is corrected by the actual output measurement value y to obtain the optimal state estimate, thereby effectively avoiding excessive error between the state estimate and the actual state of the nonlinear control system. In practical applications, the optimal state estimate of the nonlinear control system at the next moment is calculated recursively, which may specifically include the following steps:

[0113] S21. Obtain the optimal state estimate of the nonlinear control system at time k-1.

[0114] S22. Obtain the actual output measurement value {y} of the nonlinear control system at time k by detection. k}, and at the same time through Obtain the state estimate of the nonlinear control system at time k.

[0115] S23, using the actual output measurement value {y} k} for the state estimate After correction, the optimal state estimate of the nonlinear control system at time k is obtained.

[0116] In this embodiment of the invention, the specific method for calculating the optimal state estimate of the nonlinear control system at the next moment may include:

[0117] After equating the changes in model parameters to disturbances, the disturbances are further equating to Gaussian white noise, and an extended Kalman filter is designed based on this, specifically including:

[0118] 1. Find w k-1 vk Statistical characteristics

[0119] w k-1 The mean is E[w k-1 ], and E[w k-1 ]≠0, w k-1 The variance is:

[0120] E[(w k-1 -E[w k-1 ])·(w j-1 -E[w j-1 ]) T ] = Q k-1

[0121] v k The mean is E[v k ], and E[v k ] = 0, v k The variance is:

[0122] E[(v k -E[v k ])·(v j -E[v j ]) T ] = R k

[0123] and

[0124] The above formula can be simplified to:

[0125] w k-1 ~(E[w k-1 ], Q k-1 )

[0126] v k ~(0,0)

[0127] 2. W k-1 Equivalent to Gaussian white noise with zero mean, including:

[0128] make For equivalent zero-mean Gaussian white noise, the original nonlinear control system equations can be equivalently represented as:

[0129]

[0130] Z k =h[X k u k ,k]+v k

[0131] in, The mean and variance are as follows:

[0132]

[0133]

[0134] v k The mean and variance are as follows:

[0135] E[v k ] = 0,

[0136] E[(v k -E[v k ])·(v j -E[v j ]) T ] = R k =0

[0137] and

[0138] State estimation is performed using an extended Kalman filter, which transforms the nonlinear function f[X] k-1 u k-1 ,k-1,p k-1 ] and h[X k u k ,k] are respectively approximated as and The nearby first-order Taylor polynomial is:

[0139]

[0140]

[0141] Among them, F k-1 and H k For the Jacobian matrix:

[0142]

[0143]

[0144] We obtain a linear state-space model approximated by a first-order Taylor polynomial:

[0145]

[0146]

[0147] The Extended Kalman Filter (EKF) algorithm is used:

[0148] initialization:

[0149]

[0150]

[0151] Predicted status:

[0152]

[0153] State prediction error covariance matrix:

[0154]

[0155] in,

[0156]

[0157] Kalman gain:

[0158]

[0159]

[0160] State estimation:

[0161]

[0162] State estimation error covariance matrix:

[0163] P k =(IK k H k )P k|k-1

[0164] The optimal estimated state value can be obtained by solving the problem.

[0165] In this invention, the optimal state estimate is obtained as follows: taking a nonlinear control system at a certain time (time k) as an example, on the one hand, the state estimate of the nonlinear control system at time k is obtained at the previous time (k-1); on the other hand, the actual output measurement value y of the controlled object at time k is also obtained; then, the optimal state estimate of the nonlinear control system is generated by correcting the state estimate based on the actual output measurement value using an extended Kalman filter. The application scenario of the nonlinear sliding mode predictive control system in this embodiment is a system for controlling the outlet temperature of a heating furnace, and the controlled object can be the outlet temperature of the heating furnace.

[0166] S16. Obtain the corresponding switching function based on the optimal state estimate;

[0167] The inventive concept in this invention includes: utilizing the advantages of sliding mode control and predictive control methods to design a sliding mode predictive controller with stability guarantees. In other words, the nonlinear control system in this invention can also be called a nonlinear sliding mode predictive control system. The switching function in sliding mode control is selected as a new variable, and the deviation of the switching function and control input from the equivalent control is penalized in the objective function. Thus, a zero objective function signifies that the system state has reached the sliding surface, and the control law is equal to the equivalent control. By minimizing the designed objective function, the numerical solution of the optimal control action can be obtained.

[0168] For predictive control methods of nonlinear control systems, stability assurance is a prerequisite for their practical application. If infinite time-domain performance indices are used, the optimization problem would have an infinite number of optimization variables, making it unsolvable. Therefore, in this embodiment of the invention, an optimization problem using finite time-domain performance indices is employed.

[0169] In the sliding control algorithm, the first step is to select appropriate switching function parameters to ensure the system exhibits asymptotically stable sliding dynamics on the sliding surface. Then, an explicit state feedback control law is derived based on the arrival conditions. By implementing the derived state feedback control law, the system state will reach the sliding surface within a finite time and initiate sliding mode motion. Since the arrival conditions are met, once the system state reaches the sliding surface, it will not leave the sliding surface but will instead asymptotically stabilize along the sliding surface to the equilibrium point.

[0170] In this embodiment of the invention, the switching function (linear switching function) can be: in, Let A be the optimal state estimate; C is a constant matrix, then the corresponding sliding surface is a = Cx = 0. Define the null space as follows: A: N(C) = {x|||a|| = ||Cx|| ≤ Δ, C ∈ constant matrix}.

[0171] Among them, the positive constant Δ determines the bandwidth of the quasi-sliding mode.

[0172] For discrete-time nonlinear control systems, the design steps for sliding mode controllers can be as follows:

[0173] First, select the switching function a. k This makes the corresponding sliding mode asymptotically stable; the switching function a in this embodiment of the invention k It can be chosen to be in linear form with respect to state x, i.e. For linear sliding surfaces under such linear switching functions, the parameter C is very easy to obtain. Furthermore, the switching function in the embodiments of this invention can also be selected as a nonlinear form; those skilled in the art can set it themselves according to their expertise, and further explanation is not provided here.

[0174] S17. Substitute the switching function into the dynamic equation of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0175] Specifically, the switching function a at the current time k can be... k Substitute the dynamic equations of the object model group; then, using the performance optimization index and the constraints, employ the sliding mode predictive control algorithm to obtain the corresponding control action (i.e., state feedback control action) based on the arrival conditions. In practical applications, the arrival conditions can be: ||a(k+1)||<||a(k)||.

[0176] The optimization problem of sliding mode predictive control for nonlinear control systems based on contraction constraints can be specifically described as follows:

[0177] Let the dynamic equations of the object model group be:

[0178] X k =f[X k-1 u k-1 ,k-1,p k-1 ]+E[w k-1 ]

[0179] Z k =h[X k u k ,k]

[0180] The performance optimization index of the nonlinear control system is

[0181] In this embodiment of the invention, a special switching function constraint is added to the optimization problem of the traditional sliding mode predictive control method to ensure the stability of the closed-loop system.

[0182] The constraints include:

[0183] X k ∈X, i = 0, 1, ..., N-1

[0184] u k ∈U, i=0,1,...,N-1

[0185]

[0186] ρ∈[0,1)

[0187] Where ρ∈[0,1) is the contraction rate parameter, which is a fixed value set by those skilled in the art based on actual needs and / or experience; weighting matrix It is a positive definite symmetric matrix; the state constraint set X and the input constraint set U; a k For switching functions; a (0|k)Let a be the zeroth switching function at time k. (N|k) Let ρ be the Nth switching function at time k. Introducing the contraction rate parameter ρ as a new optimization variable into the optimization problem can yield better control performance and increase the feasible region of the algorithm.

[0188] Preferably, to achieve better control performance and expand the feasible region of the algorithm, this embodiment of the invention may further include a step of determining the optimal shrinkage rate parameter ρ. Specifically, when setting the value of the shrinkage rate parameter ρ, the value of ρ is adjusted based on the deviation between the prediction result and the actual result at the previous moment (or several previous moments) of the prediction system. In practical applications, when the deviation between the prediction result and the actual result is too large, the value of ρ needs to be increased, even if its value is closer to 1, thereby improving the subsequent prediction accuracy; conversely, when the deviation between the prediction result and the actual result is too small, the value of ρ needs to be decreased, even if its value is closer to 0, thereby reducing unnecessary computation. In other words, by determining the optimal shrinkage rate parameter, a balance can be achieved between accuracy and computational load, thereby improving the real-time performance of prediction by reducing computational costs while ensuring a certain level of prediction accuracy. It should be noted that those skilled in the art can set a reasonable threshold range for the deviation according to actual needs, and determine whether the deviation is too large or too small by comparing it with the deviation between the prediction result and the actual result.

[0189] In this embodiment of the invention, the specific steps for obtaining the optimal solution using the sliding mode control algorithm are as follows:

[0190] S31. Given an initial state x(0), N is the prediction time domain, the shrinkage rate ρ∈[0,1), a state constraint set X, and an input constraint set U; furthermore, Q>0, R>0,

[0191] S32. Let k = 0;

[0192] S33. Based on the dynamic equations of the object model group, the optimal state estimate at the current time k. Obtain the corresponding switching function a k ;

[0193] S34. Change the switching function a at time k. k Substituting the dynamic equations of the object model group; and using the performance optimization index and the constraints, the optimal control sequence u is obtained according to the sliding mode predictive control algorithm. * (·)={u * (0|k), ..., u * (N-1|k)};Determine the control action u from the optimal control sequence. * (0|k) is the optimal solution at time k;

[0194] S35. When k is less than the preset value, let k = k + h, and return to step S33; where h is the number of time intervals, and 1 ≤ h ≤ N; it should be noted that the value of h can be set by those skilled in the art according to actual needs. The larger the value of h, the more time intervals there are in each calculation process, and therefore the smaller the overall calculation workload; the smaller the value of h, the fewer time intervals there are in each calculation process, and the higher the accuracy of the nonlinear sliding mode prediction system in this embodiment of the invention.

[0195] S36. When k reaches the preset value, the calculation for finding the optimal solution ends.

[0196] In summary, the embodiments of the present invention first preset a reasonable range of the furnace outlet temperature as the setpoint for the nonlinear predictive controller in the nonlinear control system; furthermore, the present invention 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 characteristics of the object model group; next, the extended Kalman filter algorithm is used to obtain the optimal state estimate of the object model group; in the embodiments of the present invention, the optimal state estimate is obtained as follows: taking a nonlinear control system at a certain time (time k) as an example, on the one hand, the state estimate of the nonlinear control system at time k is obtained at the previous time (k-1); on the other hand, the actual output measurement value of the nonlinear control system at time k is also obtained; then, the state estimate is corrected by the extended Kalman filter according to the actual output measurement value to generate the optimal state estimate of the nonlinear control system.

[0197] Next, this embodiment of the invention further combines sliding mode control and predictive control, using a switching function as a new variable to optimize the sliding mode predictive control solution. The objective function uses the switching function and control input as optimization variables, and incorporates switching function constraints. By minimizing the designed objective function, the optimal control action can be obtained. This invention uses the switching function in sliding mode control as a new variable and penalizes the deviation of the switching function and control input from the equivalent control in the objective function. Therefore, a zero objective function signifies that the system state has reached the sliding surface, and the control law is equal to the equivalent control. Since this invention combines the advantages of both sliding mode control and predictive control methods, it designs a sliding mode predictive controller with stability guarantees. This controller maintains stability even under large unmeasurable disturbances in the nonlinear control system, effectively reducing the impact of unmeasurable disturbances on the predictive control effect of the nonlinear control system and improving the robustness and timeliness of the nonlinear control system. Thus, even when the nonlinear control system is subjected to large disturbances, the system stability can still be improved, effectively reducing safety hazards caused by unstable furnace outlet temperature.

[0198] Example 2

[0199] To further improve the timeliness of the nonlinear control system in this embodiment of the invention and thus enhance the control effect, this embodiment may further include determining the optimal value of the time interval h, specifically including the following steps:

[0200] S41 Based on the optimal state estimate at the current moment Let h = N, and then use the performance optimization index of the nonlinear control system. Solve the optimization problem;

[0201] When S42 cannot obtain the corresponding optimal performance indicators When h = h-1, return to step S41;

[0202] S43 can achieve the corresponding optimal performance indicators When the current h value is determined, it is set as the optimal value for the interval time h.

[0203] In this embodiment of the invention, the time interval h for each calculation can also be set to determine how many time intervals are needed before a calculation is performed. Generally, the larger the value of h, the more time intervals are between each calculation, thus reducing the overall computational load and accelerating the generation of control results. In other words, increasing the value of h can improve the timeliness of the nonlinear control system. However, an excessively large value of h will affect the accuracy of the prediction results of the nonlinear control system. Generally, if the optimal performance index can be obtained... (Non-suboptimal performance indicators) can ensure the accuracy of the prediction effect of the nonlinear control system; therefore, in this embodiment of the invention, the maximum value of h is obtained under the premise of ensuring the accuracy of the prediction effect of the nonlinear control system, so as to effectively reduce the computational load of the nonlinear control system and thereby improve the timeliness of effective control of the outlet temperature of the heating furnace.

[0204] Example 3

[0205] In another aspect of this invention, a heating furnace outlet temperature control device is also provided. Figure 4 This diagram illustrates the structure of a furnace outlet temperature control device provided in an embodiment of the present invention. The furnace outlet temperature control device is... Figure 1 The device corresponding to the furnace outlet temperature control method described in the corresponding embodiment is implemented through a virtual device. Figure 1The furnace outlet temperature control method in the corresponding embodiment allows each virtual module constituting the furnace outlet temperature control device to be executed by an electronic device, such as a network device, terminal device, or server. The furnace outlet temperature control device in this embodiment can achieve nonlinear predictive control required for industrial control. Specifically, the furnace outlet temperature control device in this embodiment includes:

[0206] The preset unit 101 is used to preset the set value of the nonlinear predictive controller in the nonlinear control system; the set value includes a reasonable range of the furnace outlet temperature.

[0207] The grouping unit 102 is used to construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the regulating valve for adjusting the fuel gas flow rate; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0208] The characteristic calculation unit 103 is used to generate the dynamic equations of the object model group, and to treat the changes in model parameters as disturbances, and to calculate the disturbance characteristics of the object model group according to the dynamic equations; the disturbances include the pressure in front of the fuel gas valve;

[0209] The numerical acquisition unit 104 is used to acquire the actual output measurement value and the state estimate value of the nonlinear control system at the current moment, respectively; the method for acquiring the state estimate value includes: using the input acting on the object model group as parameters, the state estimate value for estimating the state of the nonlinear control system at the current moment is calculated based on the dynamic equation and its disturbance characteristics at the moment before the current moment.

[0210] The correction unit 105 is used to calculate the optimal state estimate of the nonlinear control system at the next moment in a recursive manner based on the actual output measurement value and the state estimate value through an extended Kalman filter.

[0211] The switching function generation unit 106 is used to obtain the corresponding switching function based on the optimal state estimate.

[0212] The result generation unit 107 is used to input the switching function into the dynamic equation of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0213] Preferably, in this invention, the disturbance further includes one or any combination of the components of the fuel gas, the inlet flow rate of the heated material, and the temperature.

[0214] Since the working principle and beneficial effects of the furnace outlet temperature control device in the embodiments of the present invention have already been demonstrated, Figure 1 The corresponding furnace outlet temperature control methods are described and explained, so they can be referenced together and will not be repeated here.

[0215] Example 4

[0216] Corresponding to the method embodiments, this application also provides a heating furnace outlet temperature control device, such as a terminal, server, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these.

[0217] An example diagram of the hardware structure block diagram of the heating furnace outlet temperature control device provided in this embodiment of the invention is shown below. Figure 5 As shown, it may include:

[0218] Processor 1, communication interface 2, memory 3, and communication bus 4;

[0219] The processor 1, communication interface 2, and memory 3 communicate with each other via communication bus 4.

[0220] Optionally, the communication interface 2 can be an interface of a communication module, such as the interface of a GSM module; the processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0221] Memory 3 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0222] Specifically, processor 1 is used to execute the computer program stored in memory 3 to perform the following steps:

[0223] S11. The preset value of the nonlinear predictive controller in the nonlinear control system; the preset value includes a reasonable range of the furnace outlet temperature;

[0224] S12. Construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the regulating valve for adjusting the fuel gas flow rate; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0225] S13. Generate the dynamic equations of the object model group, and treat the changes in model parameters as disturbances, and calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve;

[0226] S14. Obtain the actual output measurement value and state estimate value of the nonlinear control system at the current moment, respectively; the method for obtaining the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment;

[0227] S15. Using an extended Kalman filter, the optimal state estimate of the nonlinear control system at the next moment is calculated recursively based on the actual output measurement value and the state estimate value.

[0228] S16. Obtain the corresponding switching function based on the optimal state estimate;

[0229] S17. Substitute the switching function into the dynamic equation of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0230] The furnace outlet temperature control device in this embodiment of the invention, when the program instructions included in its computer program product are executed by a computer, can enable the computer to execute the furnace outlet temperature control method described in the above aspects and achieve the same technical effect.

[0231] Example 5

[0232] In this embodiment of the invention, a storage medium is also provided, which can store a program suitable for execution by a processor, the program being used for:

[0233] S11. The preset value of the nonlinear predictive controller in the nonlinear control system; the preset value includes a reasonable range of the furnace outlet temperature;

[0234] S12. Construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the regulating valve for adjusting the fuel gas flow rate; the first controlled object sub-object model is used to describe the furnace outlet temperature; the second controlled object sub-object model is used to describe the fuel gas flow rate; the object model group is expressed in the form of a state-space model;

[0235] S13. Generate the dynamic equations of the object model group, and treat the changes in model parameters as disturbances, and calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve;

[0236] S14. Obtain the actual output measurement value and state estimate value of the nonlinear control system at the current moment, respectively; the method for obtaining the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment;

[0237] S15. Using an extended Kalman filter, the optimal state estimate of the nonlinear control system at the next moment is calculated recursively based on the actual output measurement value and the state estimate value.

[0238] S16. Obtain the corresponding switching function based on the optimal state estimate;

[0239] S17. Substitute the switching function into the dynamic equation of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group.

[0240] Optionally, the refined and extended functions of the program can be found in the description above.

[0241] The above-described product can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0242] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0243] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0244] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0245] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0246] It should be understood that in the embodiments of this application, the claims, various embodiments, and features can be combined with each other to solve the aforementioned technical problems.

[0247] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0248] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the outlet temperature of a heating furnace, characterized in that, include: S11. The preset value of the nonlinear predictive controller in the nonlinear control system; the preset value includes a reasonable range of the furnace outlet temperature; S12. Construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the fuel gas flow regulating valve. The first controlled object sub-object model is used to describe the furnace outlet temperature; The second controlled object sub-object model is used to describe the fuel gas flow rate; The object model group is expressed in the form of a state-space model; Mathematical models used to describe object model groups include: n-dimensional vector nonlinear function: X k =f[X k-1 u k-1 ,k-1,p k-1 ],as well as, m-dimensional vector nonlinear function: Z k =h[X k ,u k ,k] Among them, X k The state variable u represents the nonlinear function. k p represents the input variable of the nonlinear function. k Z represents the model parameter variable of a nonlinear function. k Represents the output variable of a nonlinear function; S13. Generate the dynamic equations of the object model group, and equate the changes in model parameters to disturbances, and calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve; the calculation of the disturbance characteristics of the object model group includes: performing a first-order Taylor expansion on the state space model of the object model group multiple times at the nominal model parameter points at preset time intervals, using the model parameters as variables, and calculating the statistical characteristics of the disturbances; S14. Obtain the actual output measurement value and state estimate value of the nonlinear control system at the current moment, respectively; the method for obtaining the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment; S15. Using an extended Kalman filter, based on the actual output measurement and the state estimate, calculate the optimal state estimate of the nonlinear control system at the next moment in a recursive manner, including: S21. Obtain the optimal state estimate of the nonlinear control system at time k-1. S22. Obtain the actual output measurement value {y} of the nonlinear control system at time k by detection. k }, and at the same time through Obtain the state estimate of the nonlinear control system at time k. S23, using the actual output measurement value {y} k } for the state estimate After correction, the optimal state estimate of the nonlinear control system at time k is obtained. S16. Obtain the corresponding switching function based on the optimal state estimate; the switching function includes: in, C is the optimal state estimate; C is a constant matrix; S17. Substitute the switching function into the dynamic equations of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as the input of the object model group. In this step: The dynamic equations of the object model group include: X k =f[X k-1 ,u k-1 ,k-1,p k-1 ]+E[w k-1 ] Z k =h[X k ,u k ,k] The performance optimization index of the nonlinear control system is: The constraints of the dynamic equations include: X k ∈X,i=0,1,...,N-1 u k ∈U,i=0,1,...,N-1 ρ∈[0,1) Where ρ∈[0,1) is the contraction rate parameter; the weighting matrix It is a positive definite symmetric matrix; the state constraint set X and the input constraint set U; a k For switching functions; a (0|k) Let a be the zeroth switching function at time k. (N|k) Let u(·) be the Nth switching function at time k, and u(·) be the control sequence. This step includes sub-steps: S31. Given an initial state x(0), N is the prediction time domain, the shrinkage rate ρ∈[0,1), a state constraint set X, and an input constraint set U; furthermore, Q>0, R>0, S32. Let k = 0; S33. Based on the dynamic equations of the object model group, the optimal state estimate at the current time k. Obtain the corresponding switching function a k ; S34. Change the switching function a at time k. k Substituting the dynamic equations of the object model group; and using the performance optimization index and the constraints, the optimal control sequence u is obtained according to the sliding mode predictive control algorithm. * (·)={u * (0|k),...,u * (N-1|k)};Determine the control action u from the optimal control sequence. * (0|k) is the optimal solution at time k; S35. When k is less than the preset value, let k = k + h, and return to step S33; where h is the number of time intervals, and 1 ≤ h ≤ N; S36. When k reaches the preset value, the calculation for finding the optimal solution ends.

2. The method for controlling the outlet temperature of a heating furnace according to claim 1, characterized in that, The disturbance also includes: The composition of the fuel gas, the inlet flow rate of the heated material, and the temperature, or any combination thereof.

3. The method for controlling the outlet temperature of a heating furnace according to claim 2, characterized in that, The step of performing a first-order Taylor expansion of the state-space model of the object model group multiple times at the nominal model parameter points, using the model parameters as variables, according to a preset time interval, and calculating the statistical characteristics of the disturbance, includes: Assuming that the parameter variables of the object model group follow a normal distribution, the changes in the model parameters of the object model group are equivalent to disturbances, and the statistical characteristics of the disturbances are calculated.

4. The method for controlling the outlet temperature of a heating furnace according to claim 3, characterized in that, Also includes: Determining the optimal value of the time interval h includes the following steps: S41 Based on the optimal state estimate at the current moment Let h = N, and then use the performance optimization index of the nonlinear control system. Solve the optimization problem; When S42 cannot obtain the corresponding optimal performance indicators When h = h-1, return to step S41; When S43 can obtain the corresponding optimal performance index When the current h value is determined, it is set as the optimal value for the interval time h.

5. A heating furnace outlet temperature control device, characterized in that, include: A preset unit is used to preset the setpoint of the nonlinear predictive controller in the nonlinear control system; the setpoint includes a reasonable range of the furnace outlet temperature. The grouping unit is used to construct an object model group through model identification, including 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; the actuator sub-object model is used to describe the opening degree of the fuel gas flow regulating valve. The first controlled object sub-object model is used to describe the furnace outlet temperature; The second controlled object sub-object model is used to describe the fuel gas flow rate; The object model group is expressed in the form of a state-space model; Mathematical models used to describe object model groups include: n-dimensional vector nonlinear function: X k =f[X k-1 ,u k-1 ,k-1,p k-1 ],as well as, m-dimensional vector nonlinear function: Z k =h[X k ,u k ,k] Among them, X k The state variable u represents the nonlinear function. k p represents the input variable of the nonlinear function. k Z represents the model parameter variable of a nonlinear function. k Represents the output variable of a nonlinear function; The characteristic calculation unit is used to generate the dynamic equations of the object model group, and to equate the changes in model parameters to disturbances, and to calculate the disturbance characteristics of the object model group based on the dynamic equations; the disturbances include the pressure before the fuel gas valve; the calculation of the disturbance characteristics of the object model group includes: performing a first-order Taylor expansion on the state space model of the object model group multiple times at the nominal model parameter points at preset time intervals, with the model parameters as variables, and calculating the statistical characteristics of the disturbances; The numerical acquisition unit is used to acquire the actual output measurement value and the state estimate value of the nonlinear control system at the current moment, respectively; the method for acquiring the state estimate value includes: using the input acting on the object model group as parameters, calculating and generating a state estimate value for estimating the state of the nonlinear control system at the current moment based on the dynamic equation and its disturbance characteristics at the moment before the current moment; The correction unit is used to recursively calculate the optimal state estimate of the nonlinear control system at the next moment using an extended Kalman filter, based on the actual output measurement and the state estimate. This includes: Obtain the optimal state estimate of the nonlinear control system at time k-1. The actual output measurement value {y} of the nonlinear control system at time k is obtained by detection. k }, and at the same time through Obtain the state estimate of the nonlinear control system at time k. Through the actual output measurement value {y k } for the state estimate After correction, the optimal state estimate of the nonlinear control system at time k is obtained. A switching function generation unit is used to obtain a corresponding switching function based on the optimal state estimate; the switching function includes: in, C is the optimal state estimate; C is a constant matrix; The result generation unit is used to input the switching function into the dynamic equations of the object model group and obtain the optimal solution according to the sliding mode control algorithm; the optimal solution is used as input to the object model group, including: The dynamic equations of the object model group include: X k =f[X k-1 ,u k-1 ,k-1,p k-1 ]+E[w k-1 ] Z k =h[X k ,u k ,k] The performance optimization index of the nonlinear control system is: The constraints of the dynamic equations include: X k ∈X,i=0,1,...,N-1 u k ∈U,i=0,1,...,N-1 ρ∈[0,1) Where ρ∈[0,1) is the contraction rate parameter; the weighting matrix It is a positive definite symmetric matrix; the state constraint set X and the input constraint set U; a k For switching functions; a (0|k) Let a be the zeroth switching function at time k. (N|k) Let u(·) be the Nth switching function at time k, and u(·) be the control sequence. This unit specifically includes: Given an initial state x(0), N is the prediction time domain, the shrinkage rate ρ∈[0,1), a state constraint set X, and an input constraint set U; furthermore, Q>0, R>0, Let k = 0; Based on the dynamic equations of the object model group, the optimal state estimate at time k is... Obtain the corresponding switching function a k ; The switching function a at time k. k Substituting the dynamic equations of the object model group; and using the performance optimization index and the constraints, the optimal control sequence u is obtained according to the sliding mode predictive control algorithm. * (·)={u * (0|k),...,u * (N-1|k)};Determine the control action u from the optimal control sequence. * (0|k) is the optimal solution at time k; When k is less than the preset value, let k = k + h, and return to the switching function a at the current time k. k Substitute the dynamic equations of the object model group into the equations; where h is the number of time intervals, and 1≤h≤N; When k reaches the preset value, the calculation for finding the optimal solution ends.

6. The furnace outlet temperature control device according to claim 5, characterized in that, The disturbance also includes: The composition of the fuel gas, the inlet flow rate of the heated material, and the temperature, or any combination thereof.

7. A heating furnace outlet temperature control device, comprising: Memory, used to store computer programs; A processor is configured to invoke and execute the computer program to implement the various steps of the furnace outlet temperature control method as described in any one of claims 1-4.

8. A storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the furnace outlet temperature control method as described in any one of claims 1-4.

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