CN116880619A - Model prediction temperature control method and system for air cooling heating process - Google Patents
Model prediction temperature control method and system for air cooling heating process Download PDFInfo
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Abstract
The invention discloses a model predictive temperature control method and a system for an air cooling heating process, and relates to the field of model predictive temperature control, wherein the method predicts the inertia change of the future temperature in an integral incremental model mode by identifying a heating model of the heating process, so that the control quantity can be reduced in advance before the heating process reaches a set temperature; in the heating process, the control quantity and the control quantity change constraint are introduced simultaneously; in the design of the controller, a control quantity weight matrix and a control quantity change weight coefficient are introduced. The invention can ensure that the control quantity is always greater than or equal to 0 in the temperature regulation process, and the temperature regulation can reach the set value in a gentle way to realize stable temperature regulation without overshoot and oscillation. The invention can be widely applied to the field of temperature control in the air cooling and heating process, and has important practical value.
Description
Technical Field
The invention relates to the field of air cooling heating temperature control, in particular to a model prediction temperature control method and system for an air cooling heating process.
Background
Temperature control is a typical industrial control problem and is widely applied to the fields of thermal power generation, chemical processes, numerical control systems and the like. The temperature regulation needs to follow the principles of conservation of energy and heat exchange, and has the characteristics of large time lag, large inertia and the like. Because of the strict physical condition limitation, the performance of temperature control is often difficult to be simultaneously considered, such as rapidity, overshoot, stability and the like, and the quality and the efficiency of an industrial process are directly influenced, so that the temperature control becomes a difficult problem in the research of control theory.
The air cooling heating process is one of important links in temperature control, and the control method and technology are also continuously developed and perfected. PID is currently the most widely used type of temperature controller in industrial systems. The PID controller has stronger robustness, and can realize large-scale temperature control and adjustment without accurately establishing a model of a temperature process. Early researches provided frequency domain design and time domain design methods of PID controllers for typical first and second order temperature processes, and are applicable to control of temperature processes with time lag characteristics. In order to realize high-precision temperature control, the PID controller is further combined with various advanced control strategies to form typical temperature control schemes such as multi-degree-of-freedom PID control, smith pre-estimated control, internal model PID control and the like, wherein the schemes mainly take PID as a leading part and have profound effects on process control.
In recent years, with the development of advanced control theory, temperature control technology is gradually perfected. Some new temperature control systems use advanced sensors and control techniques to achieve more accurate temperature control. The theoretical design of advanced control strategies, while more complex than PID control, can improve control performance through time-varying and nonlinear feedback regulation and break through the limitations of linear control. However, the conventional temperature control strategy still has the problem that a good control effect is difficult to realize in the air cooling system.
Disclosure of Invention
The invention aims to provide a model predictive temperature control method and a system for an air cooling heating process, which can realize that temperature regulation reaches a set value in a gentle mode, overcome a great abrupt change of control quantity, realize that the temperature regulation is stable, has no overshoot and has no oscillation, and solve the problem that a conventional temperature control strategy is difficult to realize a good control effect in an air cooling system.
In order to achieve the above object, the present invention provides the following solutions:
a model predictive temperature control method for an air-cooled heating process, the method comprising:
identifying a heating model of a controlled object in an air cooling heating process, and determining steady-state gain and time constant in the heating model;
converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state;
determining a control amount constraint and a control amount variation constraint of the heating model; the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient;
introducing a control quantity change weight coefficient, and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the current heating model;
determining a solving objective function of control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient;
and solving the current control quantity change based on a solving objective function of the control quantity change and the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change, and controlling the temperature of the heating model according to the current control quantity.
Optionally, the expression of the heating model is:
wherein K is the steady-state gain; t is a time constant; ζ is a damping coefficient; y(s) is the temperature output; u(s) is a control input; s is the frequency domain operator.
Optionally, the expression of the integral incremental model is:
wherein ,Am ，B m ，C m A system matrix after converting the heating model G(s) into a discrete state space model; Δx m (k) A change in state variable after converting the heating model G(s) into a discrete state space model for time k; a, B, C is a system matrix after converting a discrete state space model into an integral incremental model; x (k) is a state variable after converting the discrete state space model into an integral increment model; deltau (k) represents a control amount change at time k; o (o) m Is a zero matrix; y (k) represents the output temperature of the k-time integral delta model.
Optionally, the predictive expression for predicting the heating state is:
wherein x (k+N) p ) Nth predicted for current heating state variable x (k) p A heating state; n (N) p For a given prediction horizon; n (N) c For a given control time domain, N c <N p The control amount change outside the control time domain is set to zero; deltau (k), deltau (k+1), … Deltau (k+N) c -1) for future N to be calculated c The control amount varies.
Optionally, the expression of the predicted temperature output of the integral incremental model is:
Y＝Fx(k)+φΔU
wherein y= [ Y (k+1), Y (k+2), … Y (k+n) p )] T ；
ΔU＝[Δu(k),Δu(k+1),…Δu(k+N c -1)] T ；
Wherein y (k+1), y (k+2), … y (k+N) p ) Representing predicted N p And a temperature output.
Optionally, a calculation formula of the control amount change upper limit value in the control amount change constraint is:
Δu max ＝k*T/K
wherein ,Δumax An upper limit value for the control amount variation; k (k) * >And 0 is the upper bound coefficient of control quantity variation.
Optionally, determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the heating model at present specifically includes:
the control quantity change weight coefficient and the control quantity change upper bound coefficient are adjusted by taking a unit array or a preset numerical value as a reference;
when the overshoot degree is larger than a first preset value or the oscillation degree is larger than a second preset value, increasing the current control quantity change weight coefficient and reducing the current control quantity change upper bound coefficient; otherwise, the current control quantity change weight coefficient is reduced, and the current control quantity change upper bound coefficient is increased.
Optionally, a calculation formula of the reference temperature input of the heating model is:
wherein ,ys Is input for a reference temperature; y is 0 Is the initial temperature; y is * Is the desired temperature; omega c Representing excessive speed.
Optionally, the expression of the solving objective function of the control quantity change is:
wherein ,a weight coefficient matrix for controlling the quantity change; the first element of the solved Δu is used for the current control quantity update, Δu (k) = [ 10 … 0]ΔU；y s The resulting temperature sequence is designated->
The invention provides a model predictive temperature control system for an air cooling heating process, which comprises:
the heating model identification module is used for identifying a heating model of the controlled object in the air cooling heating process and determining steady-state gain and time constant in the heating model;
the model prediction module is used for converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state;
a constraint condition construction module for determining a control amount constraint and a control amount variation constraint of the heating model; the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient;
the weight coefficient determining module is used for introducing a control quantity change weight coefficient and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the current heating model;
the objective function construction module is used for determining a solving objective function of control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient;
and the temperature control module is used for solving the current control quantity change based on the solving objective function of the control quantity change, the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change and controlling the temperature of the heating model according to the current control quantity.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a model prediction temperature control method and a model prediction temperature control system for an air cooling heating process. The control quantity and the constraint of the control quantity change are introduced, and the severe temperature change caused by the control quantity mutation is avoided in the heating process, so that the stability and the precision of the control process are ensured. The conditions of the incremental model, the control quantity constraint, the control quantity change constraint and the like are incorporated into the design of the controller, so that the precise control of the heating process is realized. The control quantity weight coefficient and the control quantity change upper limit coefficient are introduced, and the temperature of the heating process can be controlled more accurately by adjusting the two types of coefficients. The invention can realize temperature regulation to reach a set value in a gentle mode, overcome a great abrupt change of control quantity, realize stable temperature regulation, no overshoot and no oscillation, and solve the problem that a conventional temperature control strategy is difficult to realize a good control effect in an air cooling system.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a temperature prediction control structure for an air cooling heating process according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for controlling model predictive temperature in an air cooling and heating process according to an embodiment of the invention;
FIG. 3 shows different control increment constraint control effects of a model predictive gentle temperature control method for a second-order system hollow cold heating process according to an embodiment of the present invention;
FIG. 4 shows the control effects of different control weights of the model predictive gentle temperature control method for the air-cooling and heating process in the second-order system according to the first embodiment of the invention;
FIG. 5 shows the comparison effect of model predictive control and PID control according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a model predictive temperature control method and a system for an air cooling heating process, which can realize that temperature adjustment reaches a set value in a gentle mode, overcome a great abrupt change of control quantity, realize that the temperature adjustment is stable, has no overshoot and has no oscillation, and solve the temperature control problem in the air cooling heating process.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
The embodiment provides a model predictive temperature control method for an air cooling heating process, as shown in fig. 1, which shows a principle structure of temperature predictive control with constrained control quantity and control increment in a heating stage; the control principle structure comprises air cooling gentle temperature generation, integral incremental model temperature prediction, real-time constraint optimization of control quantity, coefficient adjustment of control quantity and change thereof and the like, and the structure comprises four parts which can realize gentle temperature adjustment from different layers, are combined according to the mode of fig. 1, realize gentle temperature adjustment in the air cooling heating process, and are mutually matched to achieve gentle temperature adjustment. The air cooling gentle temperature is generated to generate a proper temperature set value, so that the set value is ensured not to jump; the integral incremental model temperature prediction is used for predicting inertial change of temperature (heating or air cooling) in a future period of time, so that oscillation and overshoot of the current control quantity system are not generated in the future period of time; the control quantity real-time constraint optimization is used for constraining the jump of the control quantity and the jump of the control quantity increment; the control amount and its changing coefficients adjust a quantization adjustment mechanism for introducing new control amount constraints. Through the design of this structure, realize mild temperature control to avoid super and the emergence of phenomenon such as vibration, reduce the influence to the environment.
The temperature control system of the extrusion head of the 3D printer is a typical air cooling heating process, and the quality of the printer product is determined by the temperature control effect of the extrusion head. The printer adopts a single-head electrothermal tube, the model is 12V,40W, the temperature measurement adopts a thermistor, and the model is NTC-R25=100deg.C+ -1%B25/50=3950. Thermistors contain three important parameters: a reference temperature of 25 ℃; resistance at reference temperature 100K; b=3950. Taking an air cooling heating process of a temperature control system of a 3D printer extrusion head as an example, the temperature control process of the present embodiment is described as follows:
specifically, as shown in fig. 2, the method includes:
s1: and identifying a heating model of the controlled object in the air cooling heating process, and determining steady-state gain and time constant in the heating model.
By identifying the second order model of the heating process, the following model (transfer function) is obtained:
where K is the steady state gain, e.g., k=59; t is a time constant, such as t=61 seconds; ζ is a damping coefficient; y(s) is the temperature output; u(s) is a control input; s is the frequency domain operator (the frequency domain method is commonly used in control theory).
The transfer function describes the heating dynamics when u >0, with a large inertia time from the change in resistance wire voltage to the change in temperature. When u=0, the temperature is reduced under the air cooling effect, and is maintained under the self inertia effect, and the dynamic process of reducing the temperature is determined by the inertia of the system and the current environment temperature. The temperature reduction process system is in an open loop state, and to realize accurate temperature control, the control quantity needs to be properly restrained in the heating process, so that the temperature can reach the set temperature under the condition of no overshoot/tiny overshoot.
S2: and converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state.
According to the actual requirement of the air cooling heating process, G(s) predicts the inertia change of the future temperature by using an integral incremental model so as to further optimize the temperature control. First, G(s) is converted into a discrete state space model whose system matrix is (A) m ，B m ，C m ) State x m (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite Then, to eliminate steady state errors, the discrete model is converted to an incremental model, and a new one is formedThe state variable x (k) and the system matrix (A, B, C). By introducing integration, steady-state errors of the system can be further eliminated, and the accuracy and stability of temperature control are improved. This approach may allow the amount of control of the heating process to be reduced in advance without oscillation and overshoot of the system for a future period of time.
Thus, model (1) is converted into an integral delta model, expressed as:
wherein ,Am ，B m ，C m A system matrix after converting the heating model G(s) into a discrete state space model; Δx m (k) A change of state variables after converting the heating model G(s) into a discrete state space model for the current k moment; a, B, C is a system matrix after converting a discrete state space model into an integral incremental model; x (k) is a state variable after converting the discrete state space model into an integral increment model; deltau (k) represents a control amount change at time k; o (o) m A zero matrix of proper dimension; y (k) represents the output temperature of the k-time integral delta model.
The system sampling period is 0.5 seconds, and the obtained system matrix is
C＝0 0 1。
The current time is k, and the given control time domain is N c (calculate future N c Δu), prediction time domain N p (current state variable x (k) predicts future N p Individual state quantity), and typically has N c <N p . Assuming that the control amount changes to zero outside the control time domain, there are:
Δu(k+i)＝0,i＝N c ,N c +1,…,N p -1
then, using the delta model described above, the state variables of the system can be predicted, with the state variable approximation result being as follows:
wherein x (k+N) p ) Nth predicted for current heating state variable x (k) p A heating state; n (N) p For a given prediction horizon; n (N) c For a given control time domain, N c <N p The control amount change outside the control time domain is set to zero; deltau (k), deltau (k+1), … Deltau (k+N) c -1) for future N to be calculated c The control amount varies.
The model (2) and the state prediction (3) are combined, the system prediction state is converted into the prediction output of the system, namely the expression of the prediction temperature output of the integral type incremental model is as follows:
writing the output into a matrix form, and finally predicting the output of the integral incremental model as follows:
Y＝Fx(k)+φΔU (4)
wherein y= [ Y (k+1), Y (k+2), … Y (k+n) p )] T ；
ΔU＝[Δu(k),Δu(k+1),…Δu(k+N c -1)] T ；
Wherein y (k+1), y (k+2), … y (k+N) p ) Representing predicted N p And a temperature output.
After the heating process model (1) is identified, the heating process model is converted into an integral increment model (2), and a state prediction (3) and a system output prediction (4) of the system are obtained by using the integral increment model (2). The inertia change of the future temperature is predicted in an integral type incremental model mode, so that the control quantity can be reduced in advance before the heating process reaches the set temperature. The model predictive control can be optimized on line according to real-time measurement results, and control accuracy and stability are further improved.
S3: determining a control amount constraint and a control amount variation constraint of the heating model; and the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient.
The control quantity and its variation constraints are designed such that the upper bound of the control quantity variation is inversely proportional to the system steady-state gain K and proportional to the time constant T. By introducing the constraint of the control quantity u and the control quantity change delta u at the same time, the severe temperature change caused by the abrupt change of the control quantity in the heating process is avoided, and the cooling process can only rely on control cooling and slow automatic heat dissipation due to the absence of a refrigerating device. To better control the overshoot, the control amount variation deltau is constrained. Wherein the upper limit Deltau of the control quantity variation max Inversely proportional to the steady-state gain K of the system, proportional to the time constant T, satisfying the formula:
Δu max ＝k * T/K， (5)
wherein ,Δumax An upper limit value for the control amount variation; k (k) * >And 0 is the upper bound coefficient of control quantity variation.
The control amount constraint is considered as follows:
u(k)＝u(k-1)+Δu(k)≤u max
wherein ,umax Is the maximum control input, determined by the system physical conditions. When u (k) =0, the system is in an air-cooled state; u (k)>The heating state is at 0.
Will control the time domain N c The constraints within are written in vector form, with:
M 1 ΔU≤N 1
wherein ,M1 ，N 1 As constraint matrix, deltaU is N c A deltau vector within.
Consider the constraint of the control amount variation au:
Δu(k)≤Δu max
will control the time domain N c The constraints within are written in vector form, with:
M 2 ΔU≤N 2
wherein M2 ，N 2 Is a constraint matrix. Let M= [ M ] 1 ,M 2 ] T ，N＝[N 1 ,N 2 ] T The method comprises the following steps:
MΔU≤N (6)
and simultaneously introducing a control quantity u and a control quantity change delta u constraint, wherein the upper limit of the control quantity change is inversely proportional to a system steady-state gain K, and the upper limit proportional to a time constant T meets the formula (5), and the air cooling process is slower than the heating process, so that the delta u (K) does not need to be provided with a lower limit, and the cooling process can be started rapidly. And (3) carrying out matrixing description on the constraint condition to obtain a formula (6), namely the constraint condition finally obtained.
And setting the upper limit of the change rate of the control quantity to limit the change speed of the control quantity, and selecting proper control parameters according to the response speed of the controller and the inertia characteristic of the system so as to further smooth the heating process and improve the accuracy and stability of temperature control. The advanced reduction of the control quantity can prevent excessive fluctuation and overshoot, so that the heating process is smoother and more stable, and the temperature regulation precision is improved.
As shown in fig. 3, deltau is selected max = infinity i.e. unconstrained, Δu max ＝0.5、Δu max ＝1、Δu max Temperature control experiments were performed =1.5. As the control amount changes, the constraint condition becomes more relaxed, and the system approaches the unconstrained condition. When Deltau max At=1.5, the oscillation and overshoot are completely consistent with the unconstrained case. While when Deltau max At=0.5, overshoot of the temperature is almost eliminated, the oscillation process thereof also becomes very small, and the temperature fluctuates slightly below the target value at all times. In order to give consideration to the performances of steady-state precision, oscillation, overshoot and the like of temperature control, deltau is selected max ＝1(k * =0.96) as a control amount variation constraint.
S4: introducing a control quantity change weight coefficient, and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper boundary coefficient according to the overshoot degree and the oscillation degree of the current heating model.
During the heating process, a control quantity weight matrix (control quantity change weight coefficient) is introducedControl amount variation upper limit weight coefficient k * The performance of the controller can be optimized by adjusting the two weight values, the large abrupt change of the control quantity is overcome, and the stable temperature adjustment, no overshoot and no oscillation are realized.
Weight matrixSum weight coefficient k * The newly introduced normalized adjustable parameter can be adjusted according to the following rules in the implementation of the temperature gentle control of the air cooling heating process:
1) Weight matrixSum weight coefficient k * The adjustment is performed based on the unit array or 1.
2) If the system oscillates or overshoot is greater, the weight matrix is increasedIntroducing a larger control quantity cost into the objective function J; otherwise, the weight matrix can be appropriately reduced>And the response speed of the system is quickened.
3) If the system oscillations or overshoot are large, k is reduced * The upper bound of the control quantity change is reduced, and the control quantity is limited to jump greatly; otherwise, properly increase k * And the response speed of the system is accelerated.
S5: and determining a solving objective function of the control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient.
Given an initial system temperature of y 0 The desired temperature is y * The gentle temperature reference track can be defined by
wherein ,ωc Representing excessive speed, y s Is written as a sequence of (2)(7) The equation achieves a gentle temperature generation.
In combination with constraint (6), the DeltaU sequence can be determined by solving the following optimization problem
s.t.MΔU≤N
This step fuses the calculation results of the previous steps for updating the control amount change Δu (k). In the objective function J, the first term (Y s -Y) T (Y s -Y) represents the prediction error between the input and output, represented by the reference trajectory Y s And S2, performing difference formation with the system prediction output Y obtained in the step; second itemThe constraint of the control signal is used for preventing the control signal from being excessively acted. For the quadratic programming problem, the Lagrangian multiplier method, the effective set method, the original-dual method, the interior point method and the like can be used for real-time optimization solution.
According to the predictive control principle, the first element of Δu is used for the current control amount update, Δu (k) = [ 10 … 0] Δu. The control constraint (6) obtained in the step S3 constitutes a constraint condition M delta U less than or equal to N of the optimization problem. Solving the optimization problem of the formula (8), carrying out real-time constraint optimization solution on the control quantity change at each moment, and calculating a real-time adjustment value delta u (k) of the control quantity change to realize rolling optimization on the control quantity change delta u (k) at each moment.
Taking outSetting Deltau max =1, prediction time domain N p Control time domain N =100 c =2. Since a control cost is introduced, r w Is to suppress temperature oscillationHas important function. Can select proper weight r through experiments w . As shown in FIG. 4, r is taken in the experimental test w =0 (i.e. not weighted), r w＝10 and rw Three cases =20.
The overshoot and oscillation of the temperature control curve after the weighting is introduced are obviously reduced. r is (r) w The larger the control amount is, the more cautious the change of the control amount is, namely, the smaller the deltau is, so that the steady state of the temperature process is more stable; on the other hand, r w The larger the temperature response speed of the heating process is also slowed down, which is mainly reflected in the dynamic process of temperature change. It can be seen that increasing the weight of the control quantity constraint overcomes overshoot and oscillation existing in the system in a control manner at the cost of the response speed of the system. For the air cooling heating process of steady-state work, the method has good practicability. For this purpose, r is selected w =20 as controller parameters.
In order to verify the effectiveness of the method, a temperature regulation experiment is carried out by adopting a designed temperature prediction controller with constraint, wherein r is w ＝20，Δu max =1, the target temperature was set to 200 ℃. Meanwhile, under the condition of consistent response speed, a PID controller is designed for comparison. As shown in fig. 5, it can be seen that even if the response speeds of the two types of controllers are identical, the overshoot of the PID control is significantly greater than the method proposed by the present invention, because the PID control can only react after the deviation occurs, and when the temperature approaches the target value, although the differential action can produce a certain deceleration effect, the PID cannot produce an accurate reverse regulation effect due to the lack of the refrigeration process, so that the temperature continuously increases. In addition, the conventional PID controller cannot be integrated into the dynamic characteristics of the system and lacks a constraint mechanism of control quantity, so that overshoot and oscillation of a large-inertia system cannot be overcome well. The design method fully considers the heating characteristic under the air cooling regulation, and specifically introduces the control quantity constraint to make the temperature enter the steady state as stably as possible.
S6: and solving the current control quantity change based on a solving objective function of the control quantity change and the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change, and controlling the temperature of the heating model according to the current control quantity.
In this embodiment, (1) by identifying the second order model of the heating process and predicting the inertial change of the future temperature in the form of the incremental model, the control amount can be reduced in advance before the heating process reaches the set temperature, thereby controlling the heating process more accurately. (2) The control quantity and the constraint of the control quantity change are introduced, and the severe temperature change caused by the control quantity mutation is avoided in the heating process, so that the stability and the precision of the control process are ensured. The conditions of the incremental model, the control quantity constraint, the control quantity change constraint and the like are incorporated into the design of the controller, so that the precise control of the heating process is realized. (3) The control quantity weight coefficient and the control quantity change upper limit coefficient are introduced, and the temperature of the heating process can be controlled more accurately by adjusting the two types of coefficients.
Example two
The embodiment provides a model predictive temperature control system for an air cooling heating process, the system comprising:
and the heating model identification module is used for identifying a heating model of the controlled object in the air cooling heating process and determining steady-state gain and time constant in the heating model.
The model prediction module is used for converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state.
A constraint condition construction module for determining a control amount constraint and a control amount variation constraint of the heating model; and the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient.
The weight coefficient determining module is used for introducing a control quantity change weight coefficient and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the current heating model.
And the objective function construction module is used for determining a solving objective function of the control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient.
And the temperature control module is used for solving the current control quantity change based on the solving objective function of the control quantity change, the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change and controlling the temperature of the heating model according to the current control quantity.
Example III
The embodiment provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to run the computer program to cause the electronic device to execute the model predictive temperature control method for the air cooling heating process according to the first embodiment.
Alternatively, the electronic device may be a server.
In addition, an embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the model predictive temperature control method of the air-cooling heating process of the first embodiment.
Embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. A model predictive temperature control method for an air cooling heating process, the method comprising:
identifying a heating model of a controlled object in an air cooling heating process, and determining steady-state gain and time constant in the heating model;
converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state;
determining a control amount constraint and a control amount variation constraint of the heating model; the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient;
introducing a control quantity change weight coefficient, and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the current heating model;
determining a solving objective function of control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient;
and solving the current control quantity change based on a solving objective function of the control quantity change and the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change, and controlling the temperature of the heating model according to the current control quantity.
2. The method of claim 1, wherein the expression of the heating model is:
wherein K is the steady-state gain; t is a time constant; ζ is a damping coefficient; y(s) is the temperature output; u(s) is a control input; s is the frequency domain operator.
3. The method of claim 2, wherein the expression of the integrated delta model is:
wherein ,Am ，B m ，C m A system matrix after converting the heating model G(s) into a discrete state space model; Δx m (k) A change in state variable after converting the heating model G(s) into a discrete state space model for time k; a, B, C is a system matrix after converting a discrete state space model into an integral incremental model; x (k) is a state variable after converting the discrete state space model into an integral increment model; deltau (k) represents a control amount change at time k; o (o) m Is a zero matrix; y (k) represents the output temperature of the k-time integral delta model.
4. A method according to claim 3, wherein the predictive expression for predicting the heating state is:
wherein x (k+N) p ) Nth predicted for current heating state variable x (k) p A heating state; n (N) p For a given prediction horizon; n (N) c For a given control time domain, N c <N p The control amount change outside the control time domain is set to zero; deltau (k), deltau (k+1), … Deltau (k+N) c -1) for future N to be calculated c The control amount varies.
5. The method of claim 4, wherein the expression of the predicted temperature output of the integrated delta model is:
Y＝Fx(k)+φΔU
wherein y= [ Y (k+1), Y (k+2), … Y (k+n) p )] T ；
ΔU＝[Δu(k),Δu(k+1),…Δu(k+N c -1)] T ；
Wherein y (k+1), y (k+2), … y (k+N) p ) Representing predicted N p And a temperature output.
6. The method according to claim 5, wherein a calculation formula of the control amount variation upper limit value in the control amount variation constraint is:
Δu max ＝k*T/K
wherein ,Δumax An upper limit value for the control amount variation; k (k) * >And 0 is the upper bound coefficient of control quantity variation.
7. The method according to claim 1, wherein determining the value of the control amount change weight coefficient and the value of the control amount change upper bound coefficient according to the overshoot degree and the oscillation degree of the heating model at present specifically comprises:
the control quantity change weight coefficient and the control quantity change upper bound coefficient are adjusted by taking a unit array or a preset numerical value as a reference;
when the overshoot degree is larger than a first preset value or the oscillation degree is larger than a second preset value, increasing the current control quantity change weight coefficient and reducing the current control quantity change upper bound coefficient; otherwise, the current control quantity change weight coefficient is reduced, and the current control quantity change upper bound coefficient is increased.
8. The method of claim 6, wherein the calculation formula for the reference temperature input of the heating model is:
wherein ,ys Is input for a reference temperature; y is 0 Is the initial temperature; y is * Is the desired temperature; omega c Representing excessive speed.
9. The method of claim 8, wherein the expression of the solution objective function for the control quantity change is:
wherein ,a weight coefficient matrix for controlling the quantity change; the first element of the solved Δu is used for the current control quantity update, Δu (k) = [ 10 … 0]ΔU；y s The resulting temperature sequence is designated->
10. A model predictive temperature control system for an air-cooled heating process, the system comprising:
the heating model identification module is used for identifying a heating model of the controlled object in the air cooling heating process and determining steady-state gain and time constant in the heating model;
the model prediction module is used for converting the heating model into an integral incremental model, predicting the heating state of the heating model by using the integral incremental model to obtain a predicted heating state, and determining the predicted temperature output of the integral incremental model by using the integral incremental model and the predicted heating state;
a constraint condition construction module for determining a control amount constraint and a control amount variation constraint of the heating model; the control quantity change upper limit value in the control quantity change constraint is determined according to the steady-state gain, the time constant and the control quantity change upper limit coefficient;
the weight coefficient determining module is used for introducing a control quantity change weight coefficient and determining the value of the control quantity change weight coefficient and the value of the control quantity change upper bound coefficient according to the overshoot degree and the oscillation degree of the current heating model;
the objective function construction module is used for determining a solving objective function of control quantity change according to the reference temperature input of the heating model, the predicted temperature output of the integral incremental model and the control quantity change weight coefficient;
and the temperature control module is used for solving the current control quantity change based on the solving objective function of the control quantity change, the control quantity constraint and the control quantity change constraint, determining the current control quantity of the heating model based on the current control quantity change and controlling the temperature of the heating model according to the current control quantity.
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