CN114117778A - Control parameter determination method and device, electronic equipment and storage medium - Google Patents
Control parameter determination method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a control parameter determination method and device, electronic equipment and a storage medium, and relates to the technical field of industrial control. The control parameter determining method comprises the following steps: firstly, a plurality of control parameter sets are automatically selected by utilizing an optimization algorithm. And then, performing simulation control on the controlled system model based on the plurality of control parameter groups respectively to obtain a plurality of evaluation parameter groups in the simulation control process. And after determining that the termination condition of the optimization algorithm is met, determining a target control parameter group from the control parameter groups according to the evaluation parameter groups. Therefore, the optimal control parameters can be automatically determined on the basis of not damaging a real controlled system, and the determination efficiency of the control parameters is improved.
Description
[ technical field ] A method for producing a semiconductor device
The present application relates to the field of industrial control technologies, and in particular, to a method and an apparatus for determining a control parameter, an electronic device, and a storage medium.
[ background of the invention ]
The control system is widely applied to the field of industrial control and can be used for controlling the operation parameters of the controlled system, so that the controlled system gradually tends to a stable state. For a control system, the setting of control parameters greatly influences the working efficiency of the control system. If the control parameter setting is not reasonable, the use time of the controlled system reaching the stable state can be prolonged, and the controlled system can not even reach the stable state.
At present, one of the common control parameter determination methods is a trial and error method, that is, different control parameters are sequentially selected for trial operation until satisfactory control parameters are obtained. However, the method depends on manual selection and trial and error, and the execution efficiency is low; in addition, since the actual controlled system needs to be controlled by trial, damage is easily caused to the controlled system.
[ summary of the invention ]
The embodiment of the application provides a control parameter determination method, a control parameter determination device, electronic equipment and a storage medium, which can automatically determine optimal control parameters on the basis of not damaging a real controlled system and improve the determination efficiency of the control parameters.
In a first aspect, an embodiment of the present application provides a method for determining a control parameter, including: automatically selecting a plurality of control parameter sets by using an optimization algorithm; respectively carrying out simulation control on the controlled system model based on the plurality of control parameter groups to obtain a plurality of evaluation parameter groups of the simulation control process; and after determining that the termination condition of the optimization algorithm is met, determining a target control parameter group from the control parameter groups according to the evaluation parameter groups.
In one possible implementation manner, the method further includes: generating a controlled system model according to a first operation process of the controlled system; wherein the first operation process is an operation process of the controlled system when the operation parameter group is at an upper limit.
In one possible implementation manner, generating a controlled system model according to a first operation process of a controlled system includes: determining a state change curve of an adjusting object corresponding to a controlled system according to a first operation process of the controlled system; and generating a controlled system model according to the state change curve of the adjusting object.
In one possible implementation manner, performing simulation control on a controlled system model based on the plurality of control parameter sets to obtain a plurality of evaluation parameter sets of the simulation control process includes: calculating a plurality of operation parameter groups of the controlled system model based on the plurality of control parameter groups respectively; and enabling the controlled system model to simulate operation based on the plurality of operation parameter groups respectively to obtain a plurality of evaluation parameter groups in the simulated operation process.
In one possible implementation manner, determining that the termination condition of the optimization algorithm is satisfied includes: and determining that the terminal condition of the optimization algorithm is met according to the difference value between the control parameter groups and the control parameter groups selected in N times in the past, wherein N is a positive integer.
In one possible implementation manner, determining that the end condition of the optimization algorithm is satisfied according to the difference between the plurality of control parameter sets and the control parameter set selected N times previously includes: respectively calculating a first average value of the plurality of control parameter groups and a second average value of the control parameter groups selected in the previous N times; and determining that the terminal condition of the optimization algorithm is met when the difference value between the first average value and the second average value is smaller than a set threshold value.
In one possible implementation manner, the evaluation parameter set includes: when the controlled system model reaches a steady state, and/or the fluctuation coefficient of the controlled system model after the controlled system model reaches the steady state; determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups, including: and determining the corresponding one with the minimum time and/or the minimum fluctuation coefficient in the plurality of control parameter groups as a target control parameter group.
In one possible implementation manner, if it is determined that the termination condition of the optimization algorithm is not satisfied, the method further includes: and continuously selecting a plurality of new control parameter groups by utilizing the optimizing algorithm.
In a second aspect, an embodiment of the present application provides a control parameter determining apparatus, where the apparatus includes: the selection module is used for automatically selecting a plurality of control parameter sets by utilizing an optimization algorithm; the control module is used for performing simulation control on the controlled system model based on the control parameter sets respectively to obtain a plurality of evaluation parameter sets of the simulation control process; and the execution module is used for determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups after the judgment module determines that the termination condition of the optimization algorithm is met.
In one possible implementation manner, the apparatus further includes a generating module, configured to generate a controlled system model according to a first operation process of the controlled system; wherein the first operation process is an operation process of the controlled system when the operation parameter group is at an upper limit.
In one possible implementation manner, the generating module is specifically configured to determine, according to a first operation process of a controlled system, a state change curve of an adjustment object corresponding to the controlled system; and generating a controlled system model according to the state change curve of the adjusting object.
In one possible implementation manner, the control module is specifically configured to calculate a plurality of operation parameter sets of the controlled system model based on the plurality of control parameter sets, respectively; and enabling the controlled system model to simulate operation based on the plurality of operation parameter groups respectively to obtain a plurality of evaluation parameter groups in the simulated operation process.
In one possible implementation manner, the determining module is specifically configured to determine that a termination condition of the optimization algorithm is satisfied according to a difference between the plurality of control parameter sets and a control parameter set selected N times in the past, where N is a positive integer.
In one possible implementation manner, the determining module is specifically configured to calculate a first average value of the plurality of control parameter sets and a second average value of the control parameter sets selected N times in the past, respectively; and determining that the terminal condition of the optimization algorithm is met when the difference value between the first average value and the second average value is smaller than a set threshold value.
In one possible implementation manner, the state evaluation parameter includes: when the controlled system model reaches a steady state, and/or the fluctuation coefficient of the controlled system model after the controlled system model reaches the steady state; the execution module is specifically configured to determine, as a target control parameter group, a corresponding one of the plurality of control parameter groups that is the smallest in the time consumption and/or the fluctuation coefficient.
In one possible implementation manner, after the determining module determines that the termination condition of the optimization algorithm is not met, the selecting module is further configured to continue to select a plurality of new control parameter sets by using the optimization algorithm.
In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor being capable of performing the method of the first aspect when invoked by the processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer instructions for causing a computer to perform the method according to the first aspect.
In the embodiment of the application, firstly, a plurality of control parameter sets can be automatically selected by utilizing an optimization algorithm; then, simulation control can be carried out on the controlled system model based on a plurality of control parameter sets respectively to obtain a plurality of evaluation parameter sets in the simulation control process; and finally, after the condition that the end condition of the optimization algorithm is met is determined, a target control parameter group can be determined from the control parameter groups according to the evaluation parameter groups. Therefore, the optimal control parameters can be automatically determined on the basis of not damaging a real controlled system, and the determination efficiency of the control parameters is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a control parameter determining method according to an embodiment of the present application;
fig. 2 is a flowchart of another control parameter determination method provided in an embodiment of the present application;
fig. 3 is a flowchart of another control parameter determination method provided in an embodiment of the present application;
fig. 4 is a flowchart of another control parameter determination method provided in an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating a room temperature state change curve according to an embodiment of the present disclosure;
FIG. 6 is a flow chart of another control parameter determination method provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of a control parameter determining apparatus according to an embodiment of the present application;
fig. 8 is a schematic view of an electronic device according to an embodiment of the present application.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The control parameter determining method provided by the embodiment of the application can be used for automatically determining the optimal control parameter for the control system, so that the control system can control the controlled system under the optimal control parameter, and the controlled system can enter a stable state as soon as possible.
The control system may be any one of a proportional-integral-derivative (PID) control system, a fuzzy control system, and the like; the controlled system may be any one of a compression refrigeration system, a vehicle speed control system, and the like, for example, and the present application is not limited thereto.
Fig. 1 is a flowchart of a control parameter determining method according to an embodiment of the present disclosure. As shown in fig. 1, the control parameter determining method may include:
In the embodiment of the application, the value intervals of the control parameters corresponding to the current control system can be predetermined. Then, a plurality of control parameter sets can be selected in a predetermined value interval by utilizing an optimization algorithm. Each control parameter set may contain all the control parameters required by the control system when executing the control algorithm. It should be noted that the number and type of control parameters required to execute the control algorithm may vary from control system to control system. For example, for a PID control system, the corresponding control parameters include 3, namely, a proportional coefficient, an integral time constant and a derivative time constant.
The Optimization Algorithm may be any one of a Particle Swarm Optimization (PSO) Algorithm, a Genetic Algorithm (GA), a Random Forest (RF) Algorithm, and the like, which is not limited in the present application.
And 102, performing simulation control on the controlled system model based on the plurality of control parameter sets respectively to obtain a plurality of evaluation parameter sets in the simulation control process.
In this embodiment, first, each control parameter set may be sequentially substituted into a control algorithm corresponding to the current control system, and the operation parameter set of the controlled system model is calculated based on the control algorithm. The number of parameters and the types of parameters included in the set of operating parameters may be different according to different models of the system to be controlled. For example, when the implement of the controlled system model is a compressor, the corresponding set of operating parameters may include a frequency; when the actuator of the controlled system model is a valve, the corresponding set of operating parameters may include opening, etc.
The PID control system is still used for illustration. The control algorithm corresponding to the PID control system is a PID control algorithm. Therefore, each control parameter set can be sequentially substituted into the PID control algorithm, and the operation parameter set of the controlled system model can be calculated.
Then, the controlled system model can be made to simulate operation based on a plurality of operation parameter sets respectively to obtain a plurality of evaluation parameter sets.
Specifically, initialization parameters may be set in advance for the controlled system model. The initialization parameters may include, for example, current operating parameters, current state parameters, and target state parameters, among others. Each set of operating parameters may then be input into the controlled system model in turn. Taking any one set of operation parameters as an example, the controlled system model may simulate operation according to the input set of operation parameters, so as to update the current state parameter to the target state parameter, and reach a stable state under the target state parameter. In this process, a set of evaluation parameters corresponding to the set of operating parameters may be determined. The evaluation parameter set may include, for example: the time when the controlled system model reaches the steady state, and/or the fluctuation coefficient after the controlled system model reaches the steady state.
And 103, determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups after determining that the termination condition of the optimization algorithm is met.
In the embodiment of the present application, after the step 102 is completed, it needs to determine whether a termination condition of the optimization algorithm is currently satisfied.
According to the algorithm execution principle of the optimization algorithm, the randomness of a plurality of control parameter groups selected for the first time is strongest, and the selection interval is the largest. The second selection of several new control parameter sets is based on the first selection, so the randomness is reduced and the selection interval is reduced. By analogy, when a new control parameter group is selected at the nth time, it can be understood that the selection interval of the control parameter group is greatly reduced. When the selection interval is reduced to a set threshold, the end condition of the optimization algorithm is considered to be reached. The value of the set threshold can be set according to actual requirements.
Based on the above description, in the embodiment of the present application, whether the termination condition of the optimization algorithm is satisfied may be determined according to the difference between the currently selected control parameter group and the previously selected control parameter group.
Specifically, a first average value of the plurality of control parameter sets and a second average value of the previously selected control parameter set may be calculated, respectively. If the difference between the first average value and the second average value is smaller than the set threshold, it indicates that the plurality of control parameter sets and the previously selected control parameter set are within a sufficiently small interval. At this point, it may be determined that the termination condition of the optimization algorithm is satisfied. Otherwise, it may be determined that the termination condition of the optimization algorithm is not satisfied.
The previously selected control parameter set may be the previously N selected control parameter sets. The value of N can be any positive integer, for example 10.
After determining that the termination condition of the optimization algorithm is satisfied, a target set of control parameters may be selected from a number of sets of control parameters. The target control parameter group is the optimal control parameter group of the current control system.
Specifically, the optimal determination of the corresponding evaluation parameter group in the plurality of control parameter groups may be determined as the target control parameter group. The evaluation parameter set may include, for example: the time when the controlled system model reaches the steady state, and/or the fluctuation coefficient after the controlled system model reaches the steady state. The set of evaluation parameters may be optimized to minimize the time and/or the coefficient of variation. The fluctuation coefficient may be, for example, a variance of the state parameter within a preset time period after the controlled system model reaches the steady state.
In the technical scheme, the damage to the real controlled system can be avoided by performing simulation control on the controlled system model. And the control parameters are determined based on the optimization algorithm, so that the automatic optimization of the control parameters can be realized, the determination efficiency of the control parameters is improved, and meanwhile, the dependency on expert experience and successful sample data does not exist. Furthermore, the scheme can be applied to any control system, and the universality of the control parameter determination method is improved.
Fig. 2 is a flowchart of another control parameter determination method according to an embodiment of the present application. As shown in fig. 2, the control parameter determining method may include:
In the embodiment of the present application, when the termination condition of the optimization algorithm is not satisfied, a plurality of new control parameter sets may be continuously selected. The method flow can then be re-executed according to the new set of control parameters until the termination condition of the optimization algorithm is met.
And step 204, determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups.
In the technical scheme, the optimal control parameters can be automatically selected based on the optimization algorithm, so that the determination efficiency of the control parameters is improved. In addition, the technical scheme can automatically judge the termination condition of the optimization algorithm, and the process of the method can be executed circularly before the termination condition is met, so that the local optimal solution can be skipped, and the globally optimal control parameter can be obtained.
Fig. 3 is a flowchart of another control parameter determination method according to an embodiment of the present application. As shown in fig. 3, in the embodiment of the present application, the method for determining a control parameter may further include:
First, a state change curve of an adjustment object corresponding to the controlled system can be determined according to a first operation process of the controlled system.
In this embodiment, the first operation process refers to an operation process of the controlled system when the operation parameter set is at the upper limit.
In general, there may be unstable operation in the initial operation stage after the controlled system is powered on. If the state change curve of the adjustment target is acquired at this time, it is difficult for the acquired curve to explain the operation of the system in a normal state.
Based on the above description, the embodiment of the present application may execute the upper and lower limit alternative operation flows for multiple times (e.g., 3 times) after the controlled system is started, so that the controlled system enters a general operation state. Specifically, for any one time of the upper and lower limit alternate operation flow, the controlled system may be firstly made to perform trial operation when the operation parameter set is at the lower limit until the controlled system is in a stable state. Then, the controlled system can be made to perform trial operation when the operation parameter set is at the upper limit until the controlled system is in a stable state.
When the operation parameters are at the lower limit, the operation efficiency of the controlled system is the lowest; and otherwise, when the operation parameter is at the upper limit, the operation efficiency of the controlled system is highest. The controlled system is in a stable state, namely, the state change rate of the adjusting object corresponding to the controlled system is smaller than the set threshold value. For example, if the controlled system is a compression refrigeration system, then the compression refrigeration system being in a steady state means that the room temperature rate of change is less than the set rate of change threshold.
Then, the state change curve of the adjustment object corresponding to the controlled system can be determined according to the operation flow (i.e., the first operation flow) in which the operation parameter set is at the upper limit in the last upper-lower limit alternate operation flow.
Specifically, in the first operation process, the state parameters of the adjustment object corresponding to the controlled system may be acquired according to the set time interval. Then, the state change curve of the adjustment object can be generated by using the acquired state parameters.
Finally, a controlled system model can be generated according to the state change curve of the regulating object.
Specifically, data fitting may be performed on the state change curve of the adjustment object to obtain an exponential function corresponding to the state change curve. Furthermore, the controlled system can be modeled according to the exponential function, and a controlled system model is obtained.
In another embodiment of the present application, for convenience of understanding, a specific example is used to further describe the generation method of the controlled system model.
In the embodiment of the application, the control system is a PID control system, and the controlled system is a compression type refrigeration system.
Fig. 4 is a flowchart of another control parameter determination method according to an embodiment of the present application. As shown in fig. 4, a method for determining a control parameter provided in an embodiment of the present application may include:
In the embodiment of the application, in the Mth upper and lower limit alternate operation process, the frequency of the compressor of the compression type refrigerating system can be set as the upper limit frequency Hmax。
Furthermore, the upper limit frequency H of the compressor is setmaxIn the operation process, the room temperature can be collected according to a set time interval, and the room temperature change rate is calculated.
When the room temperature change rate is lower than the set change rate threshold value RsNamely, after the room temperature is in a stable state, a state change curve of the room temperature can be generated according to the room temperature data acquired in the running process of the compressor. RoomThe temperature profile can be seen in fig. 5.
And step 402, performing data fitting on the state change curve of the room temperature to obtain a corresponding exponential function.
Specifically, first, the average value of the outdoor ambient temperature in the first operation process is set to ToThe cold energy model of the compressor is QC(h), wherein QCAnd H is the compressor frequency. Then, according to the law of conservation of energy, a room temperature change model can be obtained as follows:
wherein Q ishHeat is generated for a heat source in a room, and lambda is the comprehensive heat exchange coefficient of the room maintenance structure. QhAnd the value of lambda may be determined from the mth upper and lower limit alternation process. I.e. H and T after stabilizing the upper and lower limits of operation, respectivelyiAnd substituting the formula into the formula to obtain the product.
Then, according to the characteristics of the temperature control system, exponential fitting can be performed on the above formula to obtain an exponential function as follows:
Tcal＝a·eb·t+c
wherein, TcalFor the temperature fitting result, t is time, a takes a value of 19.98, b takes a value of-0.1, and c takes a value of 12.01.
And 403, modeling the compression type refrigeration system according to the exponential function obtained by data fitting to obtain a compression type refrigeration system model.
Since the above-mentioned exponential function is obtained according to a specific first operation process, it is difficult to indicate the operation state of the controlled system in general. In order to make the exponential function more versatile, the exponential function may be generalized and converted to obtain a general exponential function as follows:
y＝-b·ebx
further, the time t can be stabilized according to the temperaturesAnd a calculation cycle t of a control algorithm in the PID control systemcAnd designing an inertia array arr.
In one specific example, the respective parameter values may be preset as follows: t is ts46 minutes, i.e. 2760 seconds, tc5 seconds, the length L of the inertia array arr is ts/tc+1＝553。
On the basis, the calculation flow of the inertia array arr is as follows:
first, can be based on tcAnd L obtains the initialization time array arrs as follows:
then, a compressor frequency shift register array arrH with length L can be defined as follows:
arrH＝[H1，H2，…，HL]
in the initial state, each element in arrH is 0. When the PID control system starts to operate, namely after the control algorithm starts to operate (the operation interval is t)c) Each calculation results in a new compressor frequency H. Then, the arrH data is shifted to the right, and the first element of arrH is updated to H (i.e., H)1H). With the periodic operation of the control algorithm, and so on.
Finally, the universal exponential function y may be defined as-b · ebxInitialization time array arrs and compressor frequency shift register array arrH, which are brought into the room temperature change model
The compression refrigeration system model was obtained as follows:
further, in another embodiment of the present application, the method for determining the control parameter is further described with reference to the above description of the method for generating the controlled system model.
Fig. 6 is a flowchart of another control parameter determination method according to an embodiment of the present application. As shown in fig. 6, a method for determining a control parameter provided in an embodiment of the present application may include:
And 503, polling each control parameter group, and calculating by using a control algorithm to obtain a corresponding operation parameter group.
And 505, determining whether the system reaches a stable state or not according to the room temperature change rate. If yes, go to step 507; otherwise, step 506 is performed.
Step 506, determining whether the calculation time exceeds a set time threshold. If yes, go to step 507; otherwise, step 504 is performed.
In step 509, an optimal control parameter set is selected from the control parameter sets.
Fig. 7 is a schematic structural diagram of a control parameter determining apparatus according to an embodiment of the present application. As shown in fig. 7, the control parameter determining apparatus provided in the embodiment of the present application may include: a selection module 61, a control module 62, a judgment module 63 and an execution module 64.
And the selecting module 61 is used for automatically selecting a plurality of control parameter sets by utilizing an optimization algorithm.
And the control module 62 is configured to perform simulation control on the controlled system model based on the plurality of control parameter sets, respectively, to obtain a plurality of evaluation parameter sets in the simulation control process.
And the executing module 64 is configured to determine the target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups after the determining module 63 determines that the termination condition of the optimization algorithm is satisfied.
In a specific implementation process, the apparatus further includes a generating module 65, configured to generate a controlled system model according to a first operation process of the controlled system; the first operation process is an operation process of the controlled system when the operation parameter group is at the upper limit.
In a specific implementation process, the generating module 65 is specifically configured to determine, according to a first operation process of the controlled system, a state change curve of an adjustment object corresponding to the controlled system; and generating a controlled system model according to the state change curve of the regulating object.
In a specific implementation process, the control module 62 is specifically configured to calculate a plurality of operation parameter sets of the controlled system model based on the plurality of control parameter sets, respectively; and the controlled system model is made to simulate operation based on the plurality of operation parameter groups respectively to obtain a plurality of evaluation parameter groups in the simulated operation process.
In a specific implementation process, the determining module 63 is specifically configured to determine that the termination condition of the optimization algorithm is satisfied according to differences between a plurality of control parameter sets and the control parameter sets selected N times in the past, where N is a positive integer.
In a specific implementation process, the determining module 63 is specifically configured to calculate a first average value of a plurality of control parameter sets and a second average value of the control parameter sets selected N times in the past, respectively; and determining that the terminal condition of the optimization algorithm is met when the difference value between the first average value and the second average value is smaller than a set threshold value.
In a specific implementation, the state evaluation parameters include: when the controlled system model reaches a stable state, and/or the fluctuation coefficient of the controlled system model after the controlled system model reaches the stable state; the execution module 64 is specifically configured to determine, as the target control parameter group, one of the plurality of control parameter groups that has the smallest time consumption and/or fluctuation coefficient.
In a specific implementation process, after the determining module 63 determines that the termination condition of the optimization algorithm is not satisfied, the selecting module 61 is further configured to continue to select a plurality of new control parameter sets by using the optimization algorithm.
In the embodiment of the present application, first, the selecting module 61 may automatically select a plurality of control parameter sets by using an optimization algorithm; then, the control module 62 may perform simulation control on the controlled system model based on the plurality of control parameter sets, respectively, to obtain a plurality of evaluation parameter sets of the simulation control process; finally, after the determining module 63 determines that the termination condition of the optimization algorithm is satisfied, the executing module 64 may determine the target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups. Therefore, the optimal control parameters can be automatically determined on the basis of not damaging a real controlled system, and the determination efficiency of the control parameters is improved.
Fig. 8 is a schematic diagram of an electronic device according to an embodiment of the present application, where the electronic device may include at least one processor, as shown in fig. 8; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the control parameter determination method provided by the embodiment of the application.
The electronic device may be a control parameter determining device, and the embodiment does not limit the specific form of the electronic device.
FIG. 8 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present application. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 8, the electronic device is in the form of a general purpose computing device. Components of the electronic device may include, but are not limited to: one or more processors 410, a memory 430, a communication interface 420, and a communication bus 440 that connects the various system components (including the memory 430 and the processors 410).
Electronic devices typically include a variety of computer system readable media. Such media may be any available media that is accessible by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility having a set (at least one) of program modules, including but not limited to an operating system, one or more application programs, other program modules, and program data, may be stored in memory 430, each of which examples or some combination may include an implementation of a network environment. The program modules generally perform the functions and/or methodologies of the embodiments described herein.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication may occur via communication interface 420. Furthermore, the electronic device may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via a Network adapter (not shown in FIG. 8) that may communicate with other modules of the electronic device via the communication bus 440. It should be appreciated that although not shown in FIG. 8, other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape Drives, and data backup storage systems, among others.
The processor 410 executes various functional applications and data processing, for example, implementing the control parameter determination method provided in the embodiment of the present application, by executing the program stored in the memory 430.
The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the control parameter determining method provided in the embodiment of the present application.
The computer-readable storage medium described above may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable compact disc Read Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (11)
1. A control parameter determination method, comprising:
automatically selecting a plurality of control parameter sets by using an optimization algorithm;
respectively carrying out simulation control on the controlled system model based on the plurality of control parameter groups to obtain a plurality of evaluation parameter groups of the simulation control process;
and after determining that the termination condition of the optimization algorithm is met, determining a target control parameter group from the control parameter groups according to the evaluation parameter groups.
2. The method of claim 1, further comprising:
generating a controlled system model according to a first operation process of the controlled system;
wherein the first operation process is an operation process of the controlled system when the operation parameter group is at an upper limit.
3. The method of claim 2, wherein generating the controlled system model based on the first operational procedure of the controlled system comprises:
determining a state change curve of an adjusting object corresponding to a controlled system according to a first operation process of the controlled system;
and generating a controlled system model according to the state change curve of the adjusting object.
4. The method of claim 1, wherein performing simulation control on the controlled system model based on the plurality of control parameter sets to obtain a plurality of evaluation parameter sets of the simulation control process comprises:
calculating a plurality of operation parameter groups of the controlled system model based on the plurality of control parameter groups respectively;
and enabling the controlled system model to simulate operation based on the plurality of operation parameter groups respectively to obtain a plurality of evaluation parameter groups in the simulated operation process.
5. The method of claim 1, wherein determining that a termination condition of the optimization algorithm is satisfied comprises:
and determining that the terminal condition of the optimization algorithm is met according to the difference value between the control parameter groups and the control parameter groups selected in N times in the past, wherein N is a positive integer.
6. The method of claim 5, wherein determining that the termination condition of the optimization algorithm is satisfied based on the difference between the plurality of control parameter sets and the previous N selected control parameter sets comprises:
respectively calculating a first average value of the plurality of control parameter groups and a second average value of the control parameter groups selected in the previous N times;
and determining that the terminal condition of the optimization algorithm is met when the difference value between the first average value and the second average value is smaller than a set threshold value.
7. The method of claim 1, wherein the set of evaluation parameters comprises: when the controlled system model reaches a steady state, and/or the fluctuation coefficient of the controlled system model after the controlled system model reaches the steady state;
determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups, including:
and determining the corresponding one with the minimum time and/or the minimum fluctuation coefficient in the plurality of control parameter groups as a target control parameter group.
8. The method of claim 6, wherein it is determined that a termination condition of the optimization algorithm is not satisfied, the method further comprising:
and continuously selecting a plurality of new control parameter groups by utilizing the optimizing algorithm.
9. A control parameter determination apparatus: it is characterized by comprising:
the selection module is used for automatically selecting a plurality of control parameter sets by utilizing an optimization algorithm;
the control module is used for performing simulation control on the controlled system model based on the plurality of control parameter groups respectively to obtain a plurality of evaluation parameter groups of the simulation control process;
and the execution module is used for determining a target control parameter group from the plurality of control parameter groups according to the plurality of evaluation parameter groups after the judgment module determines that the termination condition of the optimization algorithm is met.
10. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 8.
11. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 8.
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