CN117250869A - Control method and device of temperature control equipment, readable storage medium and electronic equipment - Google Patents
Control method and device of temperature control equipment, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a control method and a control device of temperature control equipment, a readable storage medium and electronic equipment, wherein a mathematical optimizer acceleration function is used for determining an optimization strategy; the optimization strategy is used for controlling and optimizing the prediction time domain and the reference track constant in the prediction function control algorithm to obtain an optimized prediction function control algorithm, the temperature control equipment is controlled by using the optimized prediction function control algorithm, so that the control precision of the prediction function control algorithm can be improved by using the arithmetic optimization algorithm, the optimization strategy is determined by using the mathematical optimizer accelerating function, the optimization mode which is more suitable for the prediction function control algorithm can be selected, the optimization effectiveness of the prediction function control algorithm is ensured, and the control effect of the temperature equipment is effectively improved.
Description
Technical Field
The present invention relates to the field of device control technologies, and in particular, to a control method and apparatus for a temperature control device, a readable storage medium, and an electronic device.
Background
Since the fourth industrial revolution, the smart age, system application and smart production are no longer just one production process. Production intelligence connecting factories, goods, and intelligent services will become a routine element of the new global manufacturing age in the future. The system application and intelligent production are no longer simple production processes, but rather communication between the product and the machine is linked by various advanced control methods. Automatic control is realized by the control means, so that the industrial consumption and the resource waste can be effectively reduced, and the industrial production efficiency is greatly improved.
The chip is known as the core of grain and manufacture of industry, and all industries are not separated from the chip. Due to the environmental differences of the chip application, the reliability test of the chip is very necessary. The three-temperature test of the chip means that the chip performs functional test at three temperatures, namely high temperature, normal temperature and low temperature. The temperature control link is very dependent on the effect of the control algorithm, so that different control algorithms have respective advantages and characteristics while ensuring accuracy and rapidness.
In the control world today, in particular in the industrial field, there are more and more advanced and convenient control methods than conventional ones, which are used for different industrial needs. The proportional-integral-derivative control (PID control) is the most typical control method, and has the characteristics of simple concept, simple implementation, wide application, independent control parameters, relatively good parameter selection and the like. The method is followed by some updated algorithms, namely, predictive Function Control (PFC) is one of the algorithms, and the method effectively reduces the calculation amount of the algorithms while maintaining the advantages of model pre-estimation control, thereby adapting to the rapid response requirement of a controlled object to the control algorithm, and becoming a control method commonly used in industry.
How to optimize the control algorithm to improve the control effect is a problem to be solved in the control field.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: provided are a control method and device for temperature control equipment, a readable storage medium and electronic equipment, which can effectively improve control effect.
In order to solve the technical problems, the invention adopts the following technical scheme:
a control method of a temperature control apparatus, comprising the steps of:
optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
In order to solve the technical problems, the invention adopts another technical scheme that:
a control apparatus of a temperature control device, comprising:
the optimization module is used for optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
the control module is used for controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
In order to solve the technical problems, the invention adopts another technical scheme that:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the control method of the temperature control apparatus described above.
In order to solve the technical problems, the invention adopts another technical scheme that:
an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the control method of the temperature control device described above when the computer program is executed.
The invention has the beneficial effects that: the arithmetic optimization algorithm is used for optimizing the predictive function control algorithm to obtain an optimized predictive function control algorithm, the temperature control equipment is controlled by the optimized predictive function control algorithm, so that the control precision of the predictive function control algorithm can be improved by the arithmetic optimization algorithm, the mathematical optimizer acceleration function is used for determining the optimization strategy, the optimization mode which is more suitable for the predictive function control algorithm can be selected, the optimization effectiveness of the predictive function control algorithm is ensured, and the control effect of the temperature equipment is effectively improved.
Drawings
FIG. 1 is a flow chart of steps of a control method of a temperature control device according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a control device of a temperature control apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
FIG. 4 is a graph showing the comparison of control effects of different control algorithms in a control method of a temperature control device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an optimization result in a control method of a temperature control device according to an embodiment of the present invention;
fig. 6 is a comparison chart of control effects of an optimized prediction function control algorithm and other control algorithms in a control method of a temperature control device according to an embodiment of the present invention.
Detailed Description
In order to describe the technical contents, the achieved objects and effects of the present invention in detail, the following description will be made with reference to the embodiments in conjunction with the accompanying drawings.
Referring to fig. 1, a control method of a temperature control device includes the steps of:
optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
From the above description, the beneficial effects of the invention are as follows: the arithmetic optimization algorithm is used for optimizing the predictive function control algorithm to obtain an optimized predictive function control algorithm, the temperature control equipment is controlled by the optimized predictive function control algorithm, so that the control precision of the predictive function control algorithm can be improved by the arithmetic optimization algorithm, the mathematical optimizer acceleration function is used for determining the optimization strategy, the optimization mode which is more suitable for the predictive function control algorithm can be selected, the optimization effectiveness of the predictive function control algorithm is ensured, and the control effect of the temperature equipment is effectively improved.
Further, the determining an optimization strategy using a mathematical optimizer acceleration function includes:
determining a first random number;
obtaining the maximum iteration times, the current iteration times, the minimum value and the maximum value of the acceleration function of the mathematical optimizer;
calculating a current acceleration function value according to the maximum iteration number, the current iteration number, the minimum value and the maximum value;
and judging whether the current acceleration function value is smaller than the first random number, if so, determining that the optimization strategy is global exploration, and if not, determining that the optimization strategy is local development.
From the above description, it can be seen that if the current acceleration function value is smaller than the first random number, the optimization strategy is determined to be global exploration, otherwise, the optimization strategy is determined to be local development, so that the subsequent more accurate optimization can be performed.
Further, the optimizing strategy is used for controlling and optimizing the prediction time domain and the reference track constant in the prediction function control algorithm, and the obtaining of the optimized prediction function control algorithm comprises the following steps:
if the optimization strategy is the global exploration, performing global exploration on a prediction time domain and a reference track constant in a prediction function control algorithm by using a division search strategy or a multiplication search strategy to obtain an optimized prediction function control algorithm;
if the optimization strategy is the local development, an addition search strategy or a subtraction search strategy is used for carrying out local development on a prediction time domain and a reference track constant in the prediction function control algorithm, so that the optimized prediction function control algorithm is obtained.
As can be seen from the above description, the use of the division search strategy or the multiplication search strategy to globally explore the prediction time domain and the reference track constant in the prediction function control algorithm can improve the dispersion of the solution, enhance the global optimization of the algorithm and overcome the premature convergence capability, realize more effective global exploration and optimization, and use the addition search strategy or the subtraction search strategy to locally develop the prediction time domain and the reference track constant in the prediction function control algorithm, thereby reducing the dispersion of the solution, being beneficial to fully developing the population in a local range, enhancing the local optimization capability of the algorithm, and improving the control speed of an unstable processor, so that the optimized prediction function control algorithm has an optimal control effect.
Further, the global exploration is performed on the prediction time domain and the reference track constant in the prediction function control algorithm by using a division search strategy or a multiplication search strategy, and the obtaining of the optimized prediction function control algorithm comprises the following steps:
determining a second random number;
judging whether the second random number is smaller than a first preset value, if yes, performing global exploration on a prediction time domain and a reference track constant in a prediction function control algorithm by using a division search strategy to obtain an optimized prediction function control algorithm, and if not, performing global exploration on the prediction time domain and the reference track constant in the prediction function control algorithm by using a multiplication search strategy to obtain the optimized prediction function control algorithm.
Further, the division search strategy includes:
；
in the method, in the process of the invention,representing updated position, +_>Represents the t-th position in the best iteration, < >>Representing mathematical optimizer probabilities, +.>Represents a minimum value, UB represents an upper limit value of the iteration, LB represents a lower limit value of the iteration, ++>Indicating control parameters for adjusting the search process;
the multiplication search strategy comprises:
。
from the above description, whether to adopt a division searching strategy or a multiplication searching strategy is determined based on the second random number and the first preset value, so that the efficiency and reliability of the algorithm global exploration optimizing are improved.
Further, the local development of the prediction time domain and the reference track constant in the prediction function control algorithm is performed by using an addition search strategy or a subtraction search strategy, and the obtaining of the optimized prediction function control algorithm includes:
determining a third random number;
judging whether the third random number is smaller than a second preset value, if so, locally developing the prediction time domain and the reference track constant in the prediction function control algorithm by using a subtraction search strategy to obtain an optimized prediction function control algorithm, and if not, locally developing the prediction time domain and the reference track constant in the prediction function control algorithm by using an addition search strategy to obtain the optimized prediction function control algorithm.
From the above description, it is determined whether the subtraction search strategy or the addition search strategy is used for local development based on the third random number and the second preset value, so that the local optimizing capability of the algorithm is enhanced, and the control capability of the predictive function control algorithm is improved.
Referring to fig. 2, another embodiment of the present invention provides a control apparatus of a temperature control device, including:
the optimization module is used for optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
the control module is used for controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
From the above description, the beneficial effects of the invention are as follows: the arithmetic optimization algorithm is used for optimizing the predictive function control algorithm to obtain an optimized predictive function control algorithm, the temperature control equipment is controlled by the optimized predictive function control algorithm, so that the control precision of the predictive function control algorithm can be improved by the arithmetic optimization algorithm, the mathematical optimizer acceleration function is used for determining the optimization strategy, the optimization mode which is more suitable for the predictive function control algorithm can be selected, the optimization effectiveness of the predictive function control algorithm is ensured, and the control effect of the temperature equipment is effectively improved.
Another embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the control method of the temperature control apparatus described above.
Referring to fig. 3, another embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement each step in the control method of the temperature control device.
The control method and device of the temperature control equipment, the readable storage medium and the electronic equipment provided by the invention can be suitable for the control scene of the temperature control equipment such as the temperature control equipment of the three-temperature test equipment and the aging cabinet, and the control scene is described by the following specific embodiments:
referring to fig. 1 and fig. 4-6, a first embodiment of the present invention is as follows:
a control method of a temperature control apparatus, comprising the steps of:
s1, optimizing a prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm, wherein the method specifically comprises the following steps of:
s11, determining an optimization strategy by using a mathematical optimizer acceleration function (Math Optimizer Accelerated, MOA), wherein the method specifically comprises the following steps:
s111, determining a first random number.
Wherein the first random number r1 is a random number between [0,1].
S112, obtaining the maximum iteration times, the current iteration times, the minimum value and the maximum value of the acceleration function of the mathematical optimizer.
S113, calculating a current acceleration function value according to the maximum iteration number, the current iteration number, the minimum value and the maximum value, wherein the current acceleration function value is specifically:
；
in the formula, MOA (T) represents a current acceleration function value, min represents a minimum value of the acceleration function of the mathematical optimizer, max represents a maximum value of the acceleration function of the mathematical optimizer, T represents a current iteration number, and T represents a maximum iteration number.
In an alternative embodiment, min=0.2, max=1.
And S114, judging whether the current acceleration function value is smaller than the first random number, if so, determining that the optimization strategy is global exploration, and if not, determining that the optimization strategy is local development.
S12, performing control optimization on a prediction time domain and a reference track constant in a prediction function control algorithm by using the optimization strategy to obtain an optimized prediction function control algorithm, wherein the method specifically comprises the following steps of:
s121, if the optimization strategy is the global exploration, performing global exploration on a prediction time domain and a reference track constant in a prediction function control algorithm by using a division search strategy or a multiplication search strategy to obtain an optimized prediction function control algorithm, wherein the method specifically comprises the following steps of:
s1211, determining a second random number.
Wherein the second random number r2 e 0, 1.
S1212, judging whether the second random number is smaller than a first preset value, if yes, globally exploring a prediction time domain and a reference track constant in a prediction function control algorithm by using a division searching strategy to obtain an optimized prediction function control algorithm, and if not, globally exploring the prediction time domain and the reference track constant in the prediction function control algorithm by using a multiplication searching strategy to obtain the optimized prediction function control algorithm, improving the known dispersibility, enhancing the global optimizing and overcoming the premature convergence capacity of the algorithm.
Wherein the division search strategy comprises:
；
in the method, in the process of the invention,representing updated position, +_>Represents the t-th position in the best iteration, < >>Representing mathematical optimizer probabilities, +.>Represents a minimum value, UB represents an upper limit value of the iteration, LB represents a lower limit value of the iteration, ++>Indicating control parameters for adjusting the search process;
the multiplication search strategy comprises:
；
in an alternative embodiment, the first preset value is 0.5,=0.499。
the mathematical optimizer probabilities are:
；
where α represents a sensitive parameter, defining the local development accuracy in the iterative process, in an alternative embodiment α=5.
S122, if the optimization strategy is the local development, locally developing a prediction time domain and a reference track constant in a prediction function control algorithm by using an addition search strategy or a subtraction search strategy to obtain an optimized prediction function control algorithm, reducing the dispersion of solutions, being beneficial to the full development of population in a local range and enhancing the local optimizing capability of the algorithm, and specifically comprising the following steps:
s1221, determining a third random number.
Wherein the third random number r3 e [0,1].
S1222, judging whether the third random number is smaller than a second preset value, if yes, locally developing a prediction time domain and a reference track constant in the prediction function control algorithm by using a subtraction search strategy to obtain an optimized prediction function control algorithm, and if not, locally developing the prediction time domain and the reference track constant in the prediction function control algorithm by using an addition search strategy to obtain the optimized prediction function control algorithm.
Wherein the subtractive search strategy comprises:
；
the additive search strategy includes:
。
after the optimization, the optimal values of the prediction time domains P1 and P2 and the reference track constant Tr can be obtained, and the parameters are substituted into the prediction function control algorithm to obtain the optimized prediction function control algorithm.
S2, controlling the temperature control equipment by using the optimized predictive function control algorithm.
Due to industry needs, the basic requirement of control is to ensure that the settling time, which refers to the minimum time required for the controlled variable to revert from a previous steady state to a new equilibrium state once the control system is destroyed, reaches as short a steady state as possible.
The predictive function control algorithm (PFC) effectively reduces the calculated amount of the algorithm while maintaining the advantages of model pre-estimation control, thereby adapting to the rapid response requirement of a controlled object to the control algorithm, and becoming a control method commonly used in industry.
The arithmetic optimization algorithm is adopted to optimize important control parameters of PFC, the mathematical optimizer is used for accelerating function selection optimization strategy, multiplication search strategy and division search strategy are used for global search, solution dispersibility is improved, global optimization of the algorithm is enhanced, premature convergence capacity is overcome, global exploration optimization is realized, the addition search strategy and subtraction search strategy are used for reducing solution dispersibility, population is fully developed in a local range, local optimization capacity of the algorithm is enhanced, and therefore control speed of an unstable processor is improved. It has been shown that arithmetic optimization algorithm can indeed optimize PFC algorithm, optimized PFC control can even improve 2 to 3 times of accuracy, simulation results show that performance is superior to that of conventional PFC.
For a PID controller, the control law for each parameter indicates that the ratio p makes the response faster, the derivative d makes the response earlier, and the integral i makes the response lag. Within a certain range, the larger the values of p and d, the better the regulation effect. The adjustment principle of each parameter is as follows:
the proportional gain p determines the response speed and force, with too small a response resulting in a slow response and too large a response resulting in oscillations, which are the basis for i and d. When there is system fault and external influence, i can reduce error and raise accuracy, but can raise response speed, and when it is too large, it can produce overdose oscillation. D is to protect the system from external impact and from abrupt changes. And determining the most appropriate value of each of the three parameters in turn as the final parameter of the PID controller.
For the PFC controller, initial values of H (P1 and P2 for the temperature control device) and Tr are selected according to past experimental experience, and therefore, the most suitable P, i, d, H and Tr are selected as control parameter inputs through experimental debugging. PID selection p=7.72, i=1, d=14, pfc selection p1=1, p2=2, tr=1, the control effect of which is shown in fig. 4, settling time (Settling time), rise time (Rising time) introduced in the present invention are one of the most important parameters determining the control effect, which are the time required for the oscillation signal to stabilize to a specified final value and the interval between two moments when the instantaneous value of the pulse initially reaches a specified lower limit and a specified upper limit.
Table 1 time comparison of different control algorithms
As shown in table 1, the PID controller is almost twice as large as the PFC controller, with very short settling time and good performance. However, PID is very limited for complex nonlinear systems and complex signal tracking. Because the traditional model pre-estimation control algorithm is more complex than the traditional PID control algorithm, the online calculation amount is large, and the real-time performance is reduced, the application of the model pre-estimation control is generally limited to a slow process. Therefore, the prediction function control method is under the background, and a new prediction control algorithm is developed in order to adapt to the requirement of quick control.
Under the optimization of the arithmetic optimization algorithm of the present invention, optimization parameters, that is, optimal values of the prediction time domain and the reference trajectory constant, p1= 1.06153, p2=2.07378, tr=0.1, are obtained, as shown in fig. 5. The control effect of the optimized PFC and PID on the temperature control equipment is shown in figure 6. The time pair is shown in table 2.
Table 2 time comparison
It can be seen that the control effect of the PFC algorithm is greatly improved after the Arithmetic Optimization Algorithm (AOA) is optimized.
Referring to fig. 2, a second embodiment of the present invention is as follows:
a control apparatus of a temperature control device, comprising:
the optimization module is used for optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
the control module is used for controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
The third embodiment of the invention is as follows:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, can implement the steps of the control method of the temperature control apparatus in the first embodiment.
Referring to fig. 3, a fourth embodiment of the present invention is as follows:
an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the control method of the temperature control device of the first embodiment when the computer program is executed.
In summary, the control method, the device, the readable storage medium and the electronic equipment for the temperature control equipment provided by the invention use the mathematical optimizer to accelerate the function to determine the optimization strategy, use the optimization strategy to control and optimize the prediction time domain and the reference track constant in the prediction function control algorithm to obtain the optimized prediction function control algorithm, use the optimized prediction function control algorithm to control the temperature control equipment, so that the control precision of the prediction function control algorithm can be improved by using the arithmetic optimization algorithm, use the mathematical optimizer to accelerate the function to determine the optimization strategy, select the optimization mode more suitable for the prediction function control algorithm, ensure the optimization effectiveness of the prediction function control algorithm, and effectively improve the control effect of the temperature equipment; and the method adopts a division search strategy or a multiplication search strategy to globally explore the prediction time domain and the reference track constant in the prediction function control algorithm, so that the dispersion of the solution can be improved, the global optimizing of the algorithm is enhanced, the premature convergence capacity is overcome, more effective global exploration optimizing is realized, the addition search strategy or the subtraction search strategy is used for locally developing the prediction time domain and the reference track constant in the prediction function control algorithm, the dispersion of the solution can be reduced, the population can be fully developed in a local range, the local optimizing capacity of the algorithm is enhanced, and the control speed of an unstable processor is improved, so that the optimized prediction function control algorithm has the optimal control effect.
In the foregoing embodiments provided by the present application, it should be understood that the disclosed method, apparatus, computer readable storage medium and electronic device may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple components or modules may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be an indirect coupling or communication connection via some interfaces, devices or components or modules, which may be in electrical, mechanical, or other forms.
The components illustrated as separate components may or may not be physically separate, and components shown as components may or may not be physical modules, i.e., may be located in one place, or may be distributed over multiple network modules. Some or all of the components may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each component may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the present invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent changes made by the specification and drawings of the present invention, or direct or indirect application in the relevant art, are included in the scope of the present invention.
Claims (9)
1. A control method of a temperature control apparatus, characterized by comprising the steps of:
optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
2. A control method of a temperature control apparatus according to claim 1, wherein said determining an optimization strategy using a mathematical optimizer acceleration function comprises:
determining a first random number;
obtaining the maximum iteration times, the current iteration times, the minimum value and the maximum value of the acceleration function of the mathematical optimizer;
calculating a current acceleration function value according to the maximum iteration number, the current iteration number, the minimum value and the maximum value;
and judging whether the current acceleration function value is smaller than the first random number, if so, determining that the optimization strategy is global exploration, and if not, determining that the optimization strategy is local development.
3. The method for controlling a temperature control device according to claim 2, wherein the optimizing the prediction time domain and the reference trajectory constant in the prediction function control algorithm by using the optimizing strategy, and obtaining the optimized prediction function control algorithm includes:
if the optimization strategy is the global exploration, performing global exploration on a prediction time domain and a reference track constant in a prediction function control algorithm by using a division search strategy or a multiplication search strategy to obtain an optimized prediction function control algorithm;
if the optimization strategy is the local development, an addition search strategy or a subtraction search strategy is used for carrying out local development on a prediction time domain and a reference track constant in the prediction function control algorithm, so that the optimized prediction function control algorithm is obtained.
4. A control method of a temperature control device according to claim 3, wherein the global exploration is performed on the prediction time domain and the reference track constant in the prediction function control algorithm by using a division search strategy or a multiplication search strategy, and the obtaining the optimized prediction function control algorithm includes:
determining a second random number;
judging whether the second random number is smaller than a first preset value, if yes, performing global exploration on a prediction time domain and a reference track constant in a prediction function control algorithm by using a division search strategy to obtain an optimized prediction function control algorithm, and if not, performing global exploration on the prediction time domain and the reference track constant in the prediction function control algorithm by using a multiplication search strategy to obtain the optimized prediction function control algorithm.
5. The method for controlling a temperature control apparatus according to claim 4, wherein the division search strategy comprises:
；
in the method, in the process of the invention,representing updated position, +_>Represents the t-th position in the best iteration, < >>Representing mathematical optimizer probabilities, +.>Represents a minimum value, UB represents an upper limit value of the iteration, LB represents a lower limit value of the iteration, ++>Indicating control parameters for adjusting the search process;
the multiplication search strategy comprises:
。
6. a control method of a temperature control device according to claim 3, wherein the locally developing the prediction time domain and the reference trajectory constant in the prediction function control algorithm by using an addition search strategy or a subtraction search strategy, and obtaining the optimized prediction function control algorithm includes:
determining a third random number;
judging whether the third random number is smaller than a second preset value, if so, locally developing the prediction time domain and the reference track constant in the prediction function control algorithm by using a subtraction search strategy to obtain an optimized prediction function control algorithm, and if not, locally developing the prediction time domain and the reference track constant in the prediction function control algorithm by using an addition search strategy to obtain the optimized prediction function control algorithm.
7. A control device of a temperature control apparatus, characterized by comprising:
the optimization module is used for optimizing the prediction function control algorithm by using an arithmetic optimization algorithm to obtain an optimized prediction function control algorithm;
the control module is used for controlling the temperature control equipment by using the optimized predictive function control algorithm;
the method for optimizing the predictive function control algorithm by using the arithmetic optimization algorithm comprises the following steps:
determining an optimization strategy using a mathematical optimizer acceleration function;
and using the optimization strategy to control and optimize a prediction time domain and a reference track constant in the prediction function control algorithm, and obtaining the optimized prediction function control algorithm.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, realizes the respective steps in a control method of a temperature control apparatus according to any one of claims 1 to 6.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of a control method of a temperature control device according to any one of claims 1 to 6 when executing the computer program.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050267608A1 (en) * | 2004-05-27 | 2005-12-01 | Nissan Motor Co., Ltd. | Model predictive control apparatus |
| CN114861521A (en) * | 2022-03-31 | 2022-08-05 | 河北工业大学 | Method for capacity optimization configuration of combined cooling heating and power type microgrid |
| CN114897129A (en) * | 2022-03-24 | 2022-08-12 | 华北电力大学 | Photovoltaic power station short-term power prediction method based on similar daily clustering and Kmeans-GRA-LSTM |
| CN116454950A (en) * | 2023-04-21 | 2023-07-18 | 云南电网有限责任公司大理供电局 | Multi-objective arithmetic optimization algorithm-based electric hydrogen hybrid energy storage system configuration method |
-
2023
- 2023-11-15 CN CN202311516324.6A patent/CN117250869A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050267608A1 (en) * | 2004-05-27 | 2005-12-01 | Nissan Motor Co., Ltd. | Model predictive control apparatus |
| CN114897129A (en) * | 2022-03-24 | 2022-08-12 | 华北电力大学 | Photovoltaic power station short-term power prediction method based on similar daily clustering and Kmeans-GRA-LSTM |
| CN114861521A (en) * | 2022-03-31 | 2022-08-05 | 河北工业大学 | Method for capacity optimization configuration of combined cooling heating and power type microgrid |
| CN116454950A (en) * | 2023-04-21 | 2023-07-18 | 云南电网有限责任公司大理供电局 | Multi-objective arithmetic optimization algorithm-based electric hydrogen hybrid energy storage system configuration method |
Non-Patent Citations (3)
| Title |
|---|
| 侯宁;费树岷;: "基于预测函数算法的陶瓷烧成窑炉温度控制研究", 计算机与现代化, no. 11, pages 109 - 112 * |
| 李晓斌;寇得民;: "真空退火温度的建模与解耦控制", 机械工程学报, no. 10, pages 95 - 101 * |
| 沈国良;赵均;钱积新;: "基于期望响应的预测函数控制参考轨迹自校正方法", 化工学报, vol. 59, no. 01, pages 118 - 125 * |
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