CN115933597A - Parameter setting method and system of control system and computer equipment - Google Patents
Parameter setting method and system of control system and computer equipment Download PDFInfo
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Abstract
The application relates to a parameter setting method and system of a control system and computer equipment. The method comprises the following steps: acquiring the current practical feasible control parameter combination of the control system, and sending the practical feasible control parameter combination to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system; receiving a current response measured value sent by a control system; evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result; and if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination. By adopting the method, the excessive dependence on expert experience can be avoided, and the accuracy of the finally determined control parameters is improved.
Description
Technical Field
The application relates to the technical field of control systems of nuclear power plants, in particular to a parameter setting method and system of a control system and computer equipment.
Background
The nuclear power plant control system is the basis of safe, stable and economic operation of the nuclear power plant, and the control performance optimization of the nuclear power plant control system is realized by setting and optimizing control parameters, such as proportional-derivative-integral controller (PID) parameter setting and optimizing, so that the transient response capability of a nuclear power unit can be improved, and the stability, the safety and the economy of the operation of the nuclear power plant are improved. Meanwhile, the method is also an important work for designing, debugging, operating and maintaining the control system of the nuclear power plant.
In the conventional technology, operation and maintenance personnel usually design control parameters according to a relation model of the control parameters and the control performance based on own experience, and complete optimization of the control parameters through multiple tests.
However, the optimal adjustment of the control parameters in the above method depends on expert experience excessively, so that the finally determined setting values of the control parameters are difficult to ensure the performance of the nuclear power plant control system.
Disclosure of Invention
Therefore, in order to solve the technical problems, it is necessary to provide a parameter setting method, a parameter setting system, and a computer device for a control system, which can avoid excessively relying on expert experience, and improve the accuracy of a finally determined control parameter, thereby ensuring the performance of the control system of a nuclear power plant.
In a first aspect, the present application provides a method for tuning parameters of a control system. The method comprises the following steps:
acquiring a current practical feasible control parameter combination of a control system, and sending the practical feasible control parameter combination to the control system so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
receiving the response measurement value of the current time sent by the control system;
evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
In one embodiment, the method further comprises:
determining a first difference between the current response measurement and a setpoint of the control system;
determining the current time multiplied by absolute error integral index ITAE and the time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE, the ITSE and the weight coefficient corresponding to the ITSE.
In one embodiment, the obtaining of the current actual feasible control parameter combination of the control system comprises:
acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system;
carrying out normalization processing on the current initial control parameter combination to obtain a first control parameter combination;
optimizing the first control parameter combination through an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and carrying out reduction processing on the second control parameter combination to obtain the current practical feasible control parameter combination.
In one embodiment, the optimizing the first control parameter combination according to the current optimization algorithm parameter and the first control parameter combination by the optimization algorithm to obtain the current second control parameter combination includes:
determining a product result of the perturbation step length of the current time and the Monte Carlo perturbation vector; the perturbation step length of the current time is determined according to the optimization algorithm parameter of the current time;
determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as the current positive action point;
and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
In one embodiment, the method further comprises:
if the control performance evaluation result corresponding to the current positive pickup point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive pickup point, and taking the second difference value as the current negative pickup point;
and if the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, taking the negative perturbation point as the second control parameter combination of the current time.
In one embodiment, the method further comprises:
if the control performance evaluation result corresponding to the current negative shot point does not meet the preset condition, obtaining an optimized evaluation point of the current time based on a first historical control performance evaluation result, a second historical control performance evaluation result, the current positive shot point, the current negative shot point and the first control parameter combination; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative pickup point;
and if the control performance evaluation result corresponding to the optimization evaluation point meets the preset condition, taking the optimization evaluation point as the current second control parameter combination.
In one embodiment, the obtaining the current optimized estimation point based on the first historical evaluation result, the second historical evaluation result, the current positive perturbation point, the current negative perturbation point and the first control parameter combination includes:
determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;
determining a fourth difference between the current positive perturbation point and the current negative perturbation point;
determining a ratio between the third difference and the fourth difference;
and obtaining the optimized estimation point of the current time based on the ratio and the first control parameter combination of the current time.
In one embodiment, the method further comprises:
and if the control performance evaluation result corresponding to the optimized estimation point does not meet the preset condition, acquiring a next practical and feasible control parameter combination of the control system, sending the next practical and feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical and feasible control parameter combination, and determining the target control parameter combination according to the response measurement value corresponding to the next.
In one embodiment, the method further comprises:
obtaining the current historical control performance evaluation result and a preset optimization process evaluation parameter;
sequencing the historical control performance evaluation results to obtain iteration point performance sequence arrangement results;
according to the preset optimization process evaluation parameter and the iteration point performance sequence arrangement result, smoothing the iteration point performance sequence arrangement result to obtain a smoothing termination sequence;
determining a termination factor according to the smooth termination sequence, and carrying out normalization processing on the termination factor to obtain a normalized termination factor;
and obtaining the preset condition according to the normalized termination factor and the preset optimization process evaluation parameter.
In one embodiment, the obtaining the preset condition according to the normalized termination factor and the preset optimized process evaluation parameter includes:
determining a first number of times that the normalized termination factor is smaller than the lower threshold of the termination factor;
and determining the condition when the first times is equal to the lower limit threshold of the termination state coefficient and the normalized termination factor is smaller than the lower limit threshold of the termination factor as a preset condition.
In a second aspect, the application further provides a parameter tuning system of the control system. The overall system of parameters includes:
the acquisition module is used for acquiring the current practical feasible control parameter combination of the control system and sending the practical feasible control parameter combination to the control system so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
the receiving module is used for receiving the response measured value of the current time sent by the control system;
the evaluation module is used for evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and the first determining module is used for determining a target control parameter combination according to the current practical feasible control parameter combination if the current control performance evaluation result meets the preset condition.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of any of the methods of the first aspect when the computer program is executed.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods of the first aspect described above.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program that when executed by a processor implements the steps of any of the methods of the first aspect.
According to the parameter setting method, the system and the computer equipment of the control system, the currently feasible control parameter combination of the control system is obtained, the actually feasible control parameter combination is sent to the control system, the control system operates according to the actually feasible control parameter combination to obtain the response measurement value of the control system, the currently responded measurement value sent by the control system is received, the currently responded measurement value is evaluated according to the currently combined control performance index, the currently estimated control performance result is obtained, and if the currently estimated control performance result meets the preset condition, the target control parameter combination is determined according to the currently feasible control parameter combination. The control system operates according to the current actual feasible control parameter combination to obtain a response measurement value of the control system, and then evaluates the current response measurement value according to the current combined control performance index to obtain a current control performance evaluation result, so as to judge whether the current control performance evaluation result meets a preset condition, if the current control performance evaluation result meets the preset condition, a target control parameter combination is determined according to the current actual feasible control parameter combination, and because the embodiment does not need to depend on the self experience of operation and maintenance personnel, and the finally determined target control parameter combination is a parameter combination determined according to the actual feasible control parameter corresponding to the control performance evaluation result meeting the preset condition, the accuracy of the finally determined control parameter can be improved, and the performance of the nuclear power plant control system is ensured.
Drawings
Fig. 1 is an application environment diagram of a parameter tuning method of a control system according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a parameter tuning method of a control system according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a method for obtaining preset conditions according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a method for determining a composite control performance index of the present time according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a method for obtaining a practical feasible control parameter combination according to an embodiment of the present disclosure;
fig. 6 is a flowchart illustrating a method for determining a current second control parameter combination according to an embodiment of the present application;
FIG. 7 is a schematic flowchart of a method for determining a current negative perturbation point according to the present application;
fig. 8 is a schematic flowchart of a method for obtaining an optimal estimation point of the current time according to an embodiment of the present application;
fig. 9 is a schematic flowchart of another parameter tuning method for a control system according to an embodiment of the present disclosure;
fig. 10 is a block diagram of a parameter tuning system of a control system according to an embodiment of the present disclosure;
fig. 11 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
With the wide application of the digital nuclear power plant, the performance optimization of the nuclear power plant control system is realized by using the power plant data, the safe operation of the nuclear power plant can be ensured, and the economic benefit and the social benefit of the nuclear power plant are remarkable. At present, the control performance optimization method for the nuclear power plant control system at home and abroad usually comprises the following steps:
the first is a trial and error method, in which an engineer continuously adjusts the setting values of the control parameters according to the operation experience and performs a test until a group of optimal control parameter combinations is found. However, the method is excessively dependent on the experience of engineers, the setting process of the control parameter is time-consuming and labor-consuming, and the optimality of the setting value cannot be guaranteed.
Second, empirical formula methods, e.g. Ziegler-Nichols tuning method and
the method is mainly used for PID parameter setting. Usually, an engineer is required to obtain a model through a transient response test, a control parameter estimation or a frequency response test, and then a parameter setting value is given according to an experience setting formula. Although this method is simple to implement, it relies heavily on models, and the accuracy of the models is difficult or impossible to obtain, and the choice of empirical formulas depends on the experience of the engineer and the degree of knowledge of the process characteristics. Therefore, the determined control parameter setting value is not an optimal value.
And thirdly, establishing an optimization method under the condition that a control performance model is known. Optimization of the control parameters is achieved by optimization of the model, assuming that the model between the control performance and the control parameters is known. However, since the correlation between the control performance and the control parameters is very complex, it is often difficult to obtain an accurate model.
And fourthly, an intelligent optimization method taking a Particle Swarm Optimization (PSO) as a typical model, although the optimization method does not depend on a control performance model, a large number of tests are required to complete setting of control parameters, the optimization cost is extremely high, and the method is not suitable for being applied in engineering design, debugging and operation and maintenance processes.
However, the optimization methods all have the unexpected working conditions that the requirement on the accuracy of the model is high, the optimization cost is high, the optimization method excessively depends on the expertise of design, debugging, operation and maintenance personnel, engineers and the like, the finally determined control parameter setting value is difficult to ensure the performance of the nuclear power plant control system, and if the setting is not manually set, the unplanned shutdown, shutdown and other high-risk activities of human failure of the power plant are caused.
In order to solve the above technical problem, an embodiment of the present application provides a parameter tuning method for a control system, which can be applied to an application environment shown in fig. 1. Wherein the control system 102 communicates with the optimization system 104 over a network. The control system 102 is deployed on various personal computers, notebook computers, smart phones, and tablet computers. The optimization system 104 is deployed on a computer device.
In an embodiment, as shown in fig. 2, fig. 2 is a flowchart of a parameter tuning method for a control system provided in an embodiment of the present application, where the method may be applied to the above-mentioned optimization system, and the optimization system is deployed on a computer device. The method comprises the following steps:
s201, obtaining the current practical feasible control parameter combination of the control system, and sending the practical feasible control parameter combination to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system.
In this embodiment, the current actual feasible control parameter combination is sent to the control system through the data communication interface, and the control system modifies the operation control parameters of the control system according to the current actual feasible control parameter combination and operates the control test to obtain the response measurement value of the control system. For example, if the control system is a nuclear power plant control system, the nuclear power plant control system modifies the operation control parameters of the nuclear power plant control system according to the combination of the actual feasible iterative control parameters, and determines the response of the nuclear power plant control system under the nominal transient condition to obtain the response measurement value of the nuclear power plant control system.
And S202, receiving the current response measured value sent by the control system.
The above example is taken as an example, and response measurement values of the nuclear power plant control system are received and sent by a detection sensing unit of the nuclear power plant control system.
And S203, evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result.
In this embodiment, the composite control performance indicator is a weight system corresponding to the current Time-multiplied Absolute Error Indicator (ITAE) and the ITAENumber lambda 2 Weight coefficients 1- λ corresponding to the Integral of time squared Error Indicator (ITSE) and ITSE 2 And (4) determining.
And S204, if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
The preset condition in this embodiment is determined according to the control performance evaluation result and the optimization process evaluation parameter corresponding to all the actually feasible control parameter combinations.
In the method provided by this embodiment, the current practical feasible control parameter combination of the control system is obtained, and the practical feasible control parameter combination is sent to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain the response measurement value of the control system, and further receives the current response measurement value sent by the control system, so that the current response measurement value is evaluated according to the current composite control performance index to obtain the current control performance evaluation result, and if the current control performance evaluation result meets the preset condition, the target control parameter combination is determined according to the current practical feasible control parameter combination. The control system operates according to the current practical feasible control parameter combination to obtain a response measurement value of the control system, and then evaluates the current response measurement value according to the current combined control performance index to obtain a current control performance evaluation result, so as to judge whether the current control performance evaluation result meets a preset condition, if the current control performance evaluation result meets the preset condition, a target control parameter combination is determined according to the current practical feasible control parameter combination, and because the embodiment does not need to depend on the self experience of operation and maintenance personnel, and the finally determined target control parameter combination is determined according to the method until the parameter combination determined according to the practical feasible control parameter corresponding to the control performance evaluation result meeting the preset condition is obtained, the accuracy of the finally determined control parameter can be improved, and the performance of the nuclear power plant control system is ensured.
Referring to fig. 3, fig. 3 is a schematic flowchart of a method for obtaining a preset condition according to an embodiment of the present disclosure. The present embodiment relates to a possible implementation of how the preset condition is obtained. On the basis of the above embodiment, the method specifically includes the following steps:
s301, obtaining a historical control performance evaluation result before the current time and a preset optimization process evaluation parameter.
Obtaining historical control performance evaluation results corresponding to all practical feasible control parameter combinations at present and the next time, and storing the historical control performance evaluation results into a sequence to obtain a historical iteration point performance sequence S H . And storing the current control performance evaluation result to the historical iteration point performance sequence S H In (1), updating the historical iteration point performance sequence S H . Wherein, optimizing the process evaluation parameters comprises: initial value of coefficient of termination state k, lower threshold value of coefficient of termination state k F Lower threshold value ζ of termination factor T A smoothing coefficient λ, a slip termination coefficient η.
For example, all the actual feasible control parameters at the present time are combined
Respectively corresponding historical control performance evaluation results Y 1 、Y 2 、Y 3 And Y 4 Storing the sequence to obtain a historical iteration point performance sequence S H {Y 1 、Y 2 、Y 3 、Y 4 H, combining the actual feasible control parameter of the current time->
Corresponding control Performance evaluation result Y 5 Storing to historical iteration point performance sequence S H {Y 1 、Y 2 、Y 3 、Y 4 And updating a historical iteration point performance sequence S H Is { Y 1 、Y 2 、Y 3 、Y 4 、Y 5 }。
S302, sequencing the historical control performance evaluation results to obtain an iteration point performance sequence arrangement result.
Wherein the history is iteratedSubstitution performance sequence S H The control performance evaluation results corresponding to all the practical feasible control parameter combinations are sorted according to the control performance evaluation results to obtain an iteration point performance sequence arrangement result, namely a relatively optimal iteration point performance sequence S RO And sorting the control performance evaluation results based on the current practical feasible control parameter combination, and updating the relatively optimal iteration point performance sequence S RO The current actually feasible control parameter combination refers to a new actually feasible control parameter combination, and refers to a new parameter combination corresponding to the actually feasible control parameter combination of the historical time before the current time.
The description is made in conjunction with the above examples: performing performance sequence S on historical iteration points according to control performance evaluation result H All the control performance evaluation results are ranked and then are Y 1 >Y 3 >Y 2 >Y 4 Then the performance sequence S is compared with the optimal iteration point RO Is { Y 1 、Y 3 、Y 2 、Y 4 Due to new practically feasible control parameter combinations
Corresponding control Performance evaluation result Y 5 The control performance of the control system is superior to the relatively optimal iteration point performance sequence S RO {Y 1 、Y 3 、Y 2 、Y 4 Any one of the control performance evaluation results updates the relatively optimal iteration point performance sequence S RO Is { Y 5 、Y 1 、Y 3 、Y 2 、Y 4 }。
Wherein, the relative optimal iteration point performance sequence S RO Specifically, the calculation is performed by the following formula (1):
S RO (i)＝min(S H ) (1)
wherein i represents the optimal iteration point performance sequence S RO The ith number of (2).
And S303, smoothing the performance sequence arrangement result of the iteration points according to the preset optimization process evaluation parameter and the performance sequence arrangement result of the iteration points to obtain a smoothing termination sequence.
The method for obtaining the smooth termination sequence by performing smoothing processing on the iteration point performance sequence arrangement result specifically comprises the following steps:
arranging the results of the iteration point performance sequence, namely the relative optimal iteration point performance sequence S RO Smoothing is carried out by a moving average method to obtain a smoothing trend sequence S ST (ii) a For the smooth trend sequence S ST Then smoothing is carried out to obtain a smooth termination sequence S TM Wherein the sequence S is terminated smoothly TM And the method is used for controlling the iteration termination of the practical feasible control parameter combination.
Wherein the trend sequence S is smoothed ST Specifically, the calculation is performed by the following formula (2):
where n is the dimension of the parameter, λ is the smoothing coefficient, k 1 Representing an optimal iteration point performance sequence S RO Kth of (1) 1 And (4) the number of the cells.
Smoothing termination sequence S TM Specifically, the calculation is performed by the following formula (3):
where η is the slip termination coefficient.
S304, determining a termination factor according to the smooth termination sequence, and carrying out normalization processing on the termination factor to obtain the normalized termination factor.
Determining a termination factor according to the smooth termination sequence, and performing normalization processing on the termination factor to obtain a normalized termination factor, specifically comprising:
according to a smoothing termination sequence S TM Calculating a differential control sequence Δ S TM (ii) a According to a differential control sequence deltaS TM And calculating the termination factor xi (i), and carrying out normalization processing on the termination factor xi (i) to obtain a normalized termination factor zeta (i).
Wherein the differential control sequence Δ S TM Specifically, the calculation is performed by the following formula (4):
wherein, the normalized termination factor ζ (i) is specifically calculated by the following formulas (5) and (6):
wherein, min (S) ξ ) Is the minimum value of the end factor xi (i), max (S) ξ ) Is the maximum value of the termination factor ξ (i).
S305, obtaining a preset condition according to the normalized termination factor and a preset optimization process evaluation parameter.
On the basis of the foregoing embodiment, an optional implementation manner of the preset condition is obtained according to the normalized termination factor and the preset optimization process evaluation parameter, and specifically includes:
determining a first number of times that the normalized termination factor is smaller than a lower threshold of the termination factor; and determining the condition when the first times is equal to the lower limit threshold of the termination state coefficient and the normalized termination factor is smaller than the lower limit threshold of the termination factor as a preset condition.
Wherein, if the normalized termination factor zeta (i) is less than the lower threshold zeta (l) of the termination factor T And satisfies ζ (i)<ζ T Is equal to the lower coefficient limit threshold k of the end state F And if so, the current control performance evaluation result meets the preset condition.
Wherein the preset condition is specifically calculated by the following formula (7):
(ζ(i)<ζ T )∩(κ＝κ F ) (7)
in the parameter tuning method for the control system provided in this embodiment, the currently feasible control parameter combination of the control system is obtained, and the actually feasible control parameter combination is sent to the control system, so that the control system operates according to the actually feasible control parameter combination to obtain the response measurement value of the control system, and further receives the currently feasible response measurement value sent by the control system, so that the currently feasible response measurement value is evaluated according to the currently feasible composite control performance index, and the currently feasible control performance evaluation result is obtained. The control system operates according to the current practical feasible control parameter combination to obtain a response measurement value of the control system, and then evaluates the current response measurement value according to the current combined control performance index to obtain a current control performance evaluation result, so as to judge whether the current control performance evaluation result meets a preset condition, and if the current control performance evaluation result meets the preset condition, a target control parameter combination is determined according to the current practical feasible control parameter combination; if the current control performance evaluation result does not meet the preset condition, acquiring the next practical feasible control parameter combination of the control system, then judging whether the next control performance evaluation result meets the preset condition according to the method provided by the embodiment to obtain the judgment result whether the next control performance evaluation result meets the preset condition, and determining the practical feasible control parameter combination corresponding to the control performance evaluation result meeting the preset condition to be the target control parameter combination according to the method until the control performance evaluation result meeting the preset condition is obtained, so that excessive dependence on expert experience can be avoided, and the performance of the control system corresponding to the target control parameter combination is ensured.
In some embodiments, as shown in fig. 4, fig. 4 is a schematic flowchart of a method for determining a current composite control performance index according to an embodiment of the present application. The embodiment relates to an alternative implementation of how to determine the current composite control performance index. On the basis of the above embodiment, the method for determining the current composite control performance index specifically includes the following steps:
s401, determining a first difference value between the current response measured value and a set value of the control system.
And S402, determining the current time multiplied by absolute error integral index ITAE and the current time multiplied by square error integral index ITSE according to the first difference and the running time of the control system.
And S403, determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE and the weight coefficients corresponding to the ITSE and the ITSE.
In this embodiment, a first difference e (t) between the current response measurement value and the set value of the control system is determined, a current time-multiplied absolute error integral indicator ITAE and a current time-multiplied squared error integral indicator ITSE are determined according to the first difference e (t) and the operation time t of the control system, and a weight coefficient λ corresponding to the current ITAE and ITAE is determined according to the current ITAE and the time-multiplied squared error integral indicator ITSE 2 ITSE and ITSE corresponding weight coefficient 1-lambda 2 Determining the current composite control performance index Y, wherein lambda 2 The value range of (1) is (0).
The current composite control performance index Y is specifically calculated by the following formula (8):
Y＝lg(λ 2 *ITAE+(1-λ 2 )ITSE) (8)
wherein, the first and the second end of the pipe are connected with each other,
in the parameter setting method of the control system provided in this embodiment, a first difference between a current response measurement value and a set value of the control system is determined, and a current time-by-absolute-error integral index ITAE and a current time-by-square-error integral index ITSE are determined according to the first difference and an operation time of the control system, and then a current composite control performance index is determined according to a current ITAE, a weight coefficient corresponding to ITAE, and a weight coefficient corresponding to ITSE and ITSE. In this embodiment, a first difference between a current response measurement value and a set value of the control system is determined, a current time multiplied by absolute error integral index ITAE and a current time multiplied by squared error integral index ITSE are determined by the first difference and the running time of the control system, and then a current composite control performance index is determined according to a weight coefficient corresponding to the current ITAE and ITAE, and a weight coefficient corresponding to the ITSE and ITSE, so that the current response measurement value can be evaluated according to the current composite control performance index, and whether a current control performance evaluation result meets a preset condition is judged, so as to determine whether to continue optimization iteration on a current actual feasible control parameter combination, and thus, the problem that optimization adjustment of control parameters in the conventional technology depends on expert experience excessively is solved.
Referring to fig. 5, fig. 5 is a schematic flowchart of a method for obtaining a practical feasible control parameter combination according to an embodiment of the present application. The present embodiment relates to an alternative implementation of how to obtain the current time actually feasible combination of control parameters for the control system. On the basis of the foregoing embodiment, the method for acquiring the currently practical and feasible control parameter combination of the control system in S201 specifically includes the following steps:
s501, acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system.
Wherein, the optimization algorithm parameters comprise: the method comprises an initial iteration step factor a, a step correction reference parameter A, a step factor dynamic correction factor alpha, a step reference factor c, a perturbation step attenuation factor gamma and an iteration operator s.
In this embodiment, the current optimization algorithm parameters { a, c, α, γ }, the current iteration operator s, and the current initial control parameter combination X are obtained k And k is a counting operator of the control parameter combination. For example, when the iterative operator s =1, the current initial control parameter combination X set by the engineer is acquired 0 And the initial values of the current optimization algorithm parameters a, c, α, γ.
S502, normalization processing is carried out on the current initial control parameter combination to obtain a first control parameter combination.
In this embodiment, the current control parameter is combined with X k Carrying out normalization processing to obtain a first control parameter combination
Specifically, the normalization processing is performed by the following formula (9):
wherein the content of the first and second substances,
for the current time of the combination of initial control parameters>
For the first combination of control parameters of the present time, is selected>
Is the initial value of the t-th control parameter, t =1,2, \8230, n is the number of the control parameters, (X k t ) L ＝inf(X k t ) Is lower bound, (X) k t ) H ＝sup(X k t ) Is the upper bound.
For example, by combining X with the initial control parameter of the current time when the iterative operator s =1 of the current time 0 Carrying out normalization processing, and obtaining a first control parameter combination X through the calculation of the formula (9) 0 。
S503, optimizing the first control parameter combination through the optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain the current second control parameter combination.
The optimization algorithm is a synchronous disturbance Stochastic Approximation (SPSA), and the SPSA gradually approximates an optimal solution by estimating gradient information of an objective function.
And S504, restoring the second control parameter combination to obtain the current practical and feasible control parameter combination.
In the embodiment, the second control parameters are combined
Performing reduction processing to obtain the currently practical and feasible control parameter combination>
Specifically, the calculation is performed by the following formula (10):
in the method provided by this embodiment, the current initial control parameter combination and the optimization algorithm parameter of the optimization system are obtained, and the normalization processing is performed on the current initial control parameter combination to obtain the first control parameter combination, and then the first control parameter combination is optimized through the optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain the current second control parameter combination, so that the reduction processing is performed on the second control parameter combination to obtain the current practical feasible control parameter combination. In the implementation, the first control parameter combination is optimized through an optimization algorithm to obtain the current second control parameter combination, and the second control parameter combination is reduced to obtain the current practical and feasible control parameter combination, so that the number of experiments of optimization iteration required in the optimization process can be reduced, and the control parameter combination setting efficiency is improved.
Referring to fig. 6, fig. 6 is a schematic flow chart of a method for determining a current second control parameter combination provided in an embodiment of the present application, where this embodiment relates to an optional implementation manner of how to optimize, according to a current optimization algorithm parameter and a first control parameter combination, the first control parameter combination through an optimization algorithm to obtain the current second control parameter combination, and on the basis of the above embodiment, the above S503 specifically includes the following steps:
s601, determining a product result of the current perturbation step length and the Monte Carlo perturbation vector; and determining the perturbation step length of the current time according to the optimization algorithm parameters of the current time.
Wherein the Monte Carlo perturbation vector Delta s Is an n-dimensional vector, each one-dimensional vector is randomly generated by the Bernoulli distribution +/-1, and each one-dimensional vector element is independent and meets the zero-mean principle.
The current perturbation step length is determined according to the current optimization algorithm parameter, and optionally, the current perturbation step length c s The method is obtained by calculating an iterative operator s, a step length reference factor c and a perturbation step length attenuation factor gamma at the current time, and specifically by calculating according to the following formula (11):
in this embodiment, the current perturbation step length c is determined s And Monte Carlo perturbation vector delta s Result of multiplication of c s Δ s 。
And S602, determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as the current positive perturbation point.
In this embodiment, the current positive perturbation point
Is combined by means of a first control parameter>
Result of multiplication with current time c s Δ s The calculation is specifically obtained by the following formula (12):
for example, the first time when the iterative operator s =1 at the current time is determinedA control parameter combination
Sum of product results with current time c 1 Δ 1 And the sum result c is 1 Δ 1 As the positive perturbation point of the current time->
It should be noted that: set of iteration point properties to be formed
Added to the iteration point performance sequence.
And S603, if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
In this embodiment, if the current positive perturbation point
Corresponding control performance evaluation result Y k+1 If the preset condition is met, the current positive pickup point is greater than or equal to>
A second control parameter combination as a current time; if the current positive perturbation point
Corresponding control performance evaluation result Y k+1 If the preset condition is not satisfied, the following steps S701 to S702 are continuously executed to acquire the present negative pickup point->
As the current second control parameter combination.
By way of example, if the current positive perturbation point is
Corresponding control performance evaluation result Y 1 If the preset condition is met, the current positive pickup point is greater than or equal to>
As the current second control parameter combination.
In the method provided by this embodiment, a result of multiplying the current perturbation step length by the monte carlo perturbation vector is determined, a result of summing the result of multiplying the first control parameter combination by the current perturbation vector is determined, the summed result is used as the current positive perturbation point, and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the positive perturbation point is used as the current second control parameter combination. That is to say, in the embodiment, according to the perturbation step length of the current time and the monte carlo perturbation vector, a summation result of a product result of the first control parameter combination and the current time is determined, and the summation result is used as a positive perturbation point of the current time, and whether the positive perturbation point of the current time is continuously optimized is determined by judging whether a control performance evaluation result corresponding to the positive perturbation point of the current time meets a preset condition, so that the experiment number of optimization iterations required in the optimization process can be reduced, and the control parameter combination setting efficiency is improved.
Optionally, as shown in fig. 7, fig. 7 is a schematic flowchart of a method for determining a current negative perturbation point provided by the present application. The present embodiment relates to how to determine the current negative panning point, and on the basis of the above embodiments, the following implementation manners may be further included:
and S701, if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive perturbation point, and taking the second difference value as the current negative perturbation point.
In this embodiment, the current negative perturbation point
Is combined by means of a first control parameter>
Result of multiplication with current time c s Δ s The calculation is specifically obtained by the following formula (13):
by way of example, if the current positive perturbation point is
Corresponding control performance evaluation result Y 1 If the preset condition is not met, a first combination of control parameters is determined>
Result of multiplication with current time c s Δ s And the second difference is taken as the current negative perturbation point->
It should be noted that: set of iteration point properties to be formed
Added to the iteration point performance sequence. />
And S702, if the control performance evaluation result corresponding to the negative perturbation point of the current time meets a preset condition, taking the negative perturbation point as a second control parameter combination of the current time.
In this embodiment, if the current negative perturbation point
Corresponding control performance evaluation result Y k+2 If the preset condition is met, the current negative pickup point is greater than or equal to>
A second control parameter combination as a current time; if the current negative perturbation point
Corresponding control performance evaluation result Y k+2 If the preset condition is not met, the following steps S801 to S802 are continuously executed to acquire the currently optimized evaluation point->
As the current second control parameter combination.
By way of example, if the current negative perturbation point
Corresponding control performance evaluation result Y 2 If the preset condition is met, the current negative pickup point is greater than or equal to>
As the current second control parameter combination.
In the method provided by this embodiment, a second difference of a result of a multiplication of the first control parameter combination and the current time is determined, and the second difference is used as a negative perturbation point of the current time, and if a control performance evaluation result corresponding to the negative perturbation point of the current time meets a preset condition, the negative perturbation point is used as the second control parameter combination of the current time. That is to say, in this embodiment, a second difference of a result of a product of the first control parameter combination and the current time is determined, the second difference is used as a negative perturbation point of the current time, and whether the negative perturbation point of the current time is continuously optimized to obtain the target control parameter combination is determined by judging whether a control performance evaluation result corresponding to the negative perturbation point of the current time meets a preset condition, so that the setting accuracy of the target control parameter combination can be improved, and the performance of the control system corresponding to the target control parameter combination is ensured.
Referring to fig. 8, fig. 8 is a schematic flowchart of a current time optimization estimation point acquisition method according to an embodiment of the present application. The embodiment relates to how to determine the current optimal estimation point, and on the basis of the above embodiment, the following implementation manners may also be included:
and S801, if the control performance evaluation result corresponding to the negative perturbation point at the current time does not meet the preset condition, obtaining an optimization estimation point at the current time based on the first historical control performance evaluation result, the second historical control performance evaluation result, the positive perturbation point at the current time, the negative perturbation point at the current time and the first control parameter combination.
The first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point.
In the present embodiment, the performance evaluation result Y is controlled according to the first history k+1 And a second historical control performance evaluation result Y k+2 Current positive perturbation point
The current negative pickup point->
And a first control parameter combination->
Obtaining the optimized evaluation point->
It should be noted that: set of iteration point properties to be formed
Adding the data into the performance sequence of the iteration points and evaluating the optimization point of the current time to be based on the judgment result>
Added to the sequence of optimized estimator point performance.
As will be explained in connection with the above examples,if the current negative perturbation point
Corresponding control performance evaluation result Y 2 If the preset condition is not met, the performance evaluation result Y is controlled according to the first history 1 And a second historical control performance evaluation result Y 2 The current point of positive pickup->
The current negative pickup point->
And a first control parameter combination->
Obtaining the optimized evaluation point->
And S802, if the control performance evaluation result corresponding to the optimization estimation point meets a preset condition, taking the optimization estimation point as a current second control parameter combination.
In this embodiment, if the current time of the optimization estimation point
Corresponding control performance evaluation result Y k+3 If the preset condition is met, the current optimization evaluation point is evaluated>
A second control parameter combination as a current time; if the current evaluation point of optimization is greater than or equal to>
Corresponding control performance evaluation result Y k+3 If the preset condition is not satisfied, the method returns to the steps S601 to S603 to acquire the next positive perturbation point->
As the second control parameter combination of the current time, the iterative operator s adds 1 at this time.
It should be noted that: and when the next optimization is carried out, the first control parameter combination is the last optimization estimation point.
According to the method provided by the embodiment, the optimization estimation point of the current time is obtained according to the first historical control performance evaluation result, the second historical control performance evaluation result, the positive perturbation point of the current time, the negative perturbation point of the current time and the first control parameter combination, whether the optimization estimation point of the current time is continuously optimized to obtain the target control parameter combination is determined by judging whether the control performance evaluation result corresponding to the optimization estimation point of the current time meets the preset condition, and therefore the setting accuracy of the target control parameter combination can be improved, and the performance of a control system corresponding to the target control parameter combination is ensured.
On the basis of the above embodiments, the present embodiment relates to how to obtain the current optimal estimation point based on the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive perturbation point, the current negative perturbation point, and the first control parameter combination. In step S801, obtaining the current optimal estimation point based on the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive perturbation point, the current negative perturbation point, and the first control parameter combination may be further implemented by:
determining a third difference value between the first historical control performance evaluation result and the second historical control performance evaluation result, determining a fourth difference value between the current positive perturbation point and the current negative perturbation point, determining a ratio between the third difference value and the fourth difference value, and obtaining an optimized estimation point of the current time based on the ratio and the current first control parameter combination.
In this embodiment, a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result is determined as Y k+1 -Y k+2 Then determining the current positive perturbation pointThe fourth difference between the negative perturbation points of the current time is
Finally, a ratio g(s) between the third difference and the fourth difference, i.e. an approximation g(s) of the gradient at the current time, is determined, specifically, by the following equation (14):
wherein the content of the first and second substances,
all come from the iteration point performance sequence, and since the gradient approximation g(s) of the current time is a vector, the gradient approximation g(s) of the current time is stored in the gradient estimation sequence.
On the basis of the above embodiment, the optimized estimation point of the current time is obtained based on the ratio and the combination of the first control parameter of the current time, specifically, calculated by the following formulas (15) to (28):
wherein G(s) is the gradient value of the current time after correction, and G(s) is the gradient approximation value of the current time and rho s Is the gradient compensation factor corresponding to the current iteration operator s.
Where ρ is Bench Is a compensation factor reference value, rho E Is the compensation factor correction offset value, s is the current iteration operator and σ is the correction factor.
d s ＝a s ×(1+max(d sG ,d sI )) (17)
Wherein d is s Is a step size dynamic adjustment operator, a s Is an overlapGrowth factor of walk generation, d sG Step length adjustment operator for gradient correction, d sI Is the adjacent perturbation step size adjustment operator.
Wherein A is a step size correction reference parameter, a is an iteration step size factor, s is a current iteration operator and alpha is a step size factor dynamic correction factor.
Wherein d is sG Is a gradient correction step size adjustment operator, R G Is step size adjustment constant, ω is operator iterative adjustment coefficient, a s Is the iteration step size factor and SI s Is the indicator corresponding to the iteration operator s at the current time.
Where G(s) is the corrected gradient value of the current time and G(s) is the gradient approximation of the current time.
Wherein d is sI Is adjacent perturbation step size adjusting operator, xi is step size adjusting coefficient,
Is the optimized process state signal corresponding to the current iteration operator s, a s Is the iteration step-size factor and->
Is the iteration step adjustment factor. Wherein, the value range of the step length adjusting coefficient xi is (0, 1).
Wherein, the first and the second end of the pipe are connected with each other,
is a previous optimization evaluation point,/>
Is the optimum evaluation point sum of the current time>
Is the last optimization estimation point.
Wherein sgn (·) is a sign function, and the discriminant factor is constructed as follows:
wherein the content of the first and second substances,
is the control performance evaluation result corresponding to the optimization evaluation point of the current time>
Is the control performance evaluation result corresponding to the previous optimization evaluation point>
The control performance evaluation result corresponding to the previous optimization estimation point and delta are relative change factors.
It should be noted that: and constructing the iterative operator s of the current time in the discrimination factor to be more than or equal to 3.
Or
In the method provided by this embodiment, a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result is determined, a fourth difference between the current positive perturbation point and the current negative perturbation point is determined, and a ratio between the third difference and the fourth difference is further determined, so that the current optimal estimation point can be obtained based on the ratio and the current first control parameter combination.
On the basis of the above embodiment, the following implementation manners may also be included:
and if the control performance evaluation result corresponding to the optimization estimation point does not meet the preset condition, acquiring the next practical feasible control parameter combination of the control system, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining a target control parameter combination according to the next corresponding response measurement value.
For example, if the iterative operator s =1 corresponds to the optimization evaluation point of the current time
Corresponding control performance evaluation result Y 3 If the preset condition is not met, the next practical feasible control parameter combination ^ of the control system corresponding to the iterative operator s =2 is obtained>
And sends the next actually feasible combination of control parameters to the control system>
To obtain the next actually feasible combination of control parameters->
And determining a target control parameter combination according to the corresponding response measured value next time.
In the method provided by this embodiment, the next practical feasible control parameter combination of the control system is obtained, and the next practical feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next practical feasible control parameter combination, and the target control parameter combination is determined according to the response measurement value corresponding to the next practical feasible control parameter combination, and according to this method, the process is performed cyclically until the target control parameter combination is determined, so that excessive dependence on expert experience can be avoided, and the performance of the control system corresponding to the target control parameter combination is ensured.
In order to facilitate a person skilled in the art to more clearly understand the parameter tuning method of the control system provided in the present application, which is explained herein with reference to fig. 9, fig. 9 is a schematic flow diagram of another parameter tuning method of a control system provided in an embodiment of the present application, where the parameter tuning method specifically includes the following steps:
and S901, calculating the perturbation step length of the current time and the iteration step length factor of the current time by using the optimization algorithm parameter of the current time.
Wherein, the perturbation step length of the current time and the iteration step length factor of the current time are calculated, namely the algorithm gain calculation.
S902, determining the current positive perturbation point according to the current first control parameter combination and the current perturbation step length, and judging whether the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition.
S903, determining a negative perturbation point of the current time according to the first control parameter combination of the current time and the perturbation step length of the current time, and judging whether a control performance evaluation result corresponding to the negative perturbation point of the current time meets a preset condition.
And S904, determining a gradient approximate value of the current time according to the positive perturbation point of the current time, the control performance evaluation result corresponding to the positive perturbation point of the current time, and the control performance evaluation result corresponding to the negative perturbation point of the current time.
And S905, determining an indicator factor corresponding to the current iteration operator according to the current gradient approximation value.
And S906, determining a gradient correction step length adjustment operator according to the indicating factor corresponding to the current iteration operator.
And S907, determining an adjacent perturbation step size adjustment operator according to the current iteration step size factor.
And S908, determining step length dynamic adjustment according to the adjacent perturbation step length adjustment operator and the gradient correction step length adjustment operator.
And S909, determining an optimization estimation point according to the first control parameter combination and the step length dynamic adjustment, and judging whether a control performance evaluation result corresponding to the current positive perturbation point meets a preset condition.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially shown as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a parameter setting system for realizing the parameter setting method of the control system. The implementation scheme for solving the problem provided by the parameter tuning system is similar to the implementation scheme described in the above method, so the specific limitations in the parameter tuning system embodiments of one or more control systems provided below may refer to the limitations on the parameter tuning method in the above description, and details are not repeated here.
In one embodiment, as shown in fig. 10, there is provided a parameter tuning system of a control system, the parameter integration system 1000 including: an obtaining module 1001, a receiving module 1002, an evaluating module 1003, and a first determining module 1004, wherein:
an obtaining module 1001, configured to obtain a current actual feasible control parameter combination of the control system, and send the actual feasible control parameter combination to the control system, so that the control system operates according to the actual feasible control parameter combination to obtain a response measurement value of the control system;
a receiving module 1002, configured to receive a current response measurement value sent by the control system;
the evaluation module 1003 is configured to evaluate the current response measurement value according to the current composite control performance index, so as to obtain a current control performance evaluation result;
the first determining module 1004 is configured to determine a target control parameter combination according to the currently-available actual control parameter combination if the current control performance evaluation result meets a preset condition.
In one embodiment, the parameter integration system 1000 further comprises:
a second determination module for determining a first difference between the current response measurement and a set point of the control system;
the third determination module is used for determining the current time multiplied by absolute error integral index ITAE and the current time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and the fourth determining module is used for determining the current composite control performance index according to the ITAE of the current time, the weight coefficient corresponding to the ITAE and the weight coefficients corresponding to the ITSE and the ITSE.
In one embodiment, the obtaining module 1001 includes an obtaining unit, a normalizing unit, an optimizing unit, and a restoring unit:
the acquiring unit is used for acquiring the current initial control parameter combination and the optimization algorithm parameters of the optimization system;
the normalization unit is used for performing normalization processing on the current initial control parameter combination to obtain a first control parameter combination;
the optimization unit is used for optimizing the first control parameter combination through the optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and the reduction unit is used for carrying out reduction processing on the second control parameter combination to obtain the current practical feasible control parameter combination.
In one embodiment, the optimization unit is specifically configured to determine a product of the current perturbation step size and the monte carlo perturbation vector; determining the perturbation step length of the current time according to the optimization algorithm parameter of the current time; determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as a positive perturbation point of the current time; and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the positive perturbation point as the second control parameter combination of the current time.
In one embodiment, the parameter integration system 1000 further comprises:
the fifth determining module is used for determining a second difference value of a product result of the first control parameter combination and the current time if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, and taking the second difference value as the current negative perturbation point;
and the first judgment module is used for taking the negative perturbation point as the second control parameter combination of the current time if the control performance evaluation result corresponding to the negative perturbation point of the current time meets the preset condition.
In one embodiment, the parameter integration system 1000 further comprises:
the obtaining module is used for obtaining an optimal estimation point of the current time based on a first historical control performance evaluation result, a second historical control performance evaluation result, the positive pickup point of the current time, the negative pickup point of the current time and a first control parameter combination if a control performance evaluation result corresponding to the negative pickup point of the current time does not meet a preset condition; the first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point;
and the second judgment module is used for taking the optimization estimation point as the current second control parameter combination if the control performance evaluation result corresponding to the optimization estimation point meets the preset condition.
In one embodiment, the obtaining module is specifically configured to determine a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result; determining a fourth difference value between the current positive perturbation point and the current negative perturbation point; determining a ratio between the third difference and the fourth difference; and obtaining the current optimization estimation point based on the ratio and the current first control parameter combination.
In one embodiment, the parameter integration system 1000 further comprises:
and the sixth determining module is used for acquiring the next practical feasible control parameter combination of the control system if the control performance evaluation result corresponding to the optimization evaluation point does not meet the preset condition, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining the target control parameter combination according to the response measurement value corresponding to the next practical feasible control parameter combination.
All modules in the parameter setting system of the control system can be completely or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 11. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing control performance evaluation result data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of parameter tuning of a control system.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring the current practical feasible control parameter combination of the control system, and sending the practical feasible control parameter combination to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
receiving a current response measured value sent by a control system;
evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining a first difference between the current response measurement and a set point of the control system;
determining the current time multiplied by absolute error integral index ITAE and the current time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE and the weight coefficients corresponding to the ITSE and the ITSE.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system;
normalizing the current initial control parameter combination to obtain a first control parameter combination;
optimizing the first control parameter combination through an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and restoring the second control parameter combination to obtain the current practical feasible control parameter combination.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining a product result of the perturbation step length of the current time and the Monte Carlo perturbation vector; determining the perturbation step length of the current time according to the optimization algorithm parameter of the current time;
determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as a positive perturbation point of the current time;
and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive perturbation point, and taking the second difference value as the current negative perturbation point;
and if the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, taking the negative perturbation point as the current second control parameter combination.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
if the control performance evaluation result corresponding to the current negative shot point does not meet the preset condition, obtaining the current optimized evaluation point based on the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive shot point, the current negative shot point and the first control parameter combination; the first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point;
and if the control performance evaluation result corresponding to the optimization evaluation point meets the preset condition, taking the optimization evaluation point as the current second control parameter combination.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;
determining a fourth difference value between the current positive perturbation point and the current negative perturbation point;
determining a ratio between the third difference and the fourth difference;
and obtaining the current optimization estimation point based on the ratio and the current first control parameter combination.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and if the control performance evaluation result corresponding to the optimization estimation point does not meet the preset condition, acquiring the next practical feasible control parameter combination of the control system, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining a target control parameter combination according to the next corresponding response measurement value.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring the current practical feasible control parameter combination of the control system, and sending the practical feasible control parameter combination to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
receiving a current response measured value sent by a control system;
evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a first difference between the current response measurement and a set point of the control system;
determining the current time multiplied by absolute error integral index ITAE and the current time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE and the weight coefficients corresponding to the ITSE and the ITSE.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system;
carrying out normalization processing on the current initial control parameter combination to obtain a first control parameter combination;
optimizing the first control parameter combination through an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and restoring the second control parameter combination to obtain the current practical feasible control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a product result of the perturbation step length of the current time and the Monte Carlo perturbation vector; determining the perturbation step length of the current time according to the optimization algorithm parameter of the current time;
determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as a positive perturbation point of the current time;
and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive perturbation point, and taking the second difference value as the current negative perturbation point;
and if the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, taking the negative perturbation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the control performance evaluation result corresponding to the current negative shot point does not meet the preset condition, obtaining the current optimized evaluation point based on the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive shot point, the current negative shot point and the first control parameter combination; the first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point;
and if the control performance evaluation result corresponding to the optimization estimation point meets the preset condition, taking the optimization estimation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;
determining a fourth difference value between the current positive perturbation point and the current negative perturbation point;
determining a ratio between the third difference and the fourth difference;
and obtaining the current optimization estimation point based on the ratio and the current first control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and if the control performance evaluation result corresponding to the optimization estimation point does not meet the preset condition, acquiring the next practical feasible control parameter combination of the control system, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining a target control parameter combination according to the response measurement value corresponding to the next time.
In one embodiment, a computer program product is provided, comprising a computer program which when executed by a processor performs the steps of:
acquiring the current practical feasible control parameter combination of the control system, and sending the practical feasible control parameter combination to the control system, so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
receiving a current response measured value sent by a control system;
evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and if the current control performance evaluation result meets the preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a first difference between the current response measurement and a set point of the control system;
determining the current time multiplied by absolute error integral index ITAE and the current time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE and the weight coefficients corresponding to the ITSE and the ITSE.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system;
normalizing the current initial control parameter combination to obtain a first control parameter combination;
optimizing the first control parameter combination through an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and restoring the second control parameter combination to obtain the current practical feasible control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a product result of the perturbation step length of the current time and the Monte Carlo perturbation vector; determining the perturbation step length of the current time according to the optimization algorithm parameter of the current time;
determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as a positive perturbation point of the current time;
and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive perturbation point, and taking the second difference value as the current negative perturbation point;
and if the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, taking the negative perturbation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the control performance evaluation result corresponding to the current negative shot point does not meet the preset condition, obtaining the current optimized evaluation point based on the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive shot point, the current negative shot point and the first control parameter combination; the first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point;
and if the control performance evaluation result corresponding to the optimization estimation point meets the preset condition, taking the optimization estimation point as the current second control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;
determining a fourth difference value between the current positive perturbation point and the current negative perturbation point;
determining a ratio between the third difference and the fourth difference;
and obtaining the current optimization estimation point based on the ratio and the current first control parameter combination.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and if the control performance evaluation result corresponding to the optimization estimation point does not meet the preset condition, acquiring the next practical feasible control parameter combination of the control system, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining a target control parameter combination according to the next corresponding response measurement value.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, the computer program may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (12)
1. A parameter setting method of a control system is characterized by comprising the following steps:
acquiring a current practical feasible control parameter combination of a control system, and sending the practical feasible control parameter combination to the control system so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
receiving the current response measurement value sent by the control system;
evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and if the current control performance evaluation result meets a preset condition, determining a target control parameter combination according to the current practical feasible control parameter combination.
2. The method of claim 1, further comprising:
determining a first difference between the current response measurement and a setpoint of the control system;
determining the current time multiplied by absolute error integral index ITAE and the time multiplied by square error integral index ITSE according to the first difference and the operation time of the control system;
and determining the current composite control performance index according to the current ITAE, the weight coefficient corresponding to the ITAE, the ITSE and the weight coefficient corresponding to the ITSE.
3. The method of claim 1, wherein obtaining a current time actual feasible control parameter combination for the control system comprises:
acquiring the current initial control parameter combination and optimization algorithm parameters of the optimization system;
carrying out normalization processing on the current initial control parameter combination to obtain a first control parameter combination;
optimizing the first control parameter combination through an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain a current second control parameter combination;
and restoring the second control parameter combination to obtain the current practical feasible control parameter combination.
4. The method according to claim 3, wherein the optimizing the first control parameter combination by the optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain the current second control parameter combination comprises:
determining a product result of the perturbation step length of the current time and the Monte Carlo perturbation vector; the current perturbation step length is determined according to the current optimization algorithm parameter;
determining a summation result of the product result of the first control parameter combination and the current time, and taking the summation result as the current positive shooting point;
and if the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, taking the current positive perturbation point as the current second control parameter combination.
5. The method of claim 4, further comprising:
if the control performance evaluation result corresponding to the current positive pickup point does not meet the preset condition, determining a second difference value of the product result of the first control parameter combination and the current positive pickup point, and taking the second difference value as the current negative pickup point;
and if the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, taking the negative perturbation point as the current second control parameter combination.
6. The method of claim 5, further comprising:
if the control performance evaluation result corresponding to the current negative shot point does not meet the preset condition, obtaining an optimized evaluation point of the current time based on a first historical control performance evaluation result, a second historical control performance evaluation result, the current positive shot point, the current negative shot point and the first control parameter combination; the first historical control performance evaluation result is a control performance evaluation result corresponding to the current positive pickup point, and the second historical control performance evaluation result is a control performance evaluation result corresponding to the current negative pickup point;
and if the control performance evaluation result corresponding to the optimization evaluation point meets the preset condition, taking the optimization evaluation point as the current second control parameter combination.
7. The method of claim 6, wherein obtaining the current optimized estimation point based on a combination of the first historical control performance evaluation result, the second historical control performance evaluation result, the current positive perturbation point, the current negative perturbation point and the first control parameter comprises:
determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;
determining a fourth difference value between the current positive perturbation point and the current negative perturbation point;
determining a ratio between the third difference and the fourth difference;
and obtaining the current optimization estimation point based on the ratio and the current first control parameter combination.
8. The method according to claim 6 or 7, characterized in that the method further comprises:
and if the control performance evaluation result corresponding to the optimized evaluation point does not meet the preset condition, acquiring a next practical feasible control parameter combination of the control system, sending the next practical feasible control parameter combination to the control system to obtain a response measurement value corresponding to the next practical feasible control parameter combination, and determining the target control parameter combination according to the response measurement value corresponding to the next.
9. The method according to any one of claims 1-7, further comprising:
obtaining the historical control performance evaluation result before the current time and a preset optimization process evaluation parameter;
sequencing the historical control performance evaluation results to obtain an iteration point performance sequence arrangement result;
according to the preset optimization process evaluation parameter and the iteration point performance sequence arrangement result, smoothing the iteration point performance sequence arrangement result to obtain a smoothing termination sequence;
determining a termination factor according to the smooth termination sequence, and carrying out normalization processing on the termination factor to obtain a normalized termination factor;
and obtaining the preset condition according to the normalized termination factor and the preset optimization process evaluation parameter.
10. The method according to claim 9, wherein the preset optimized process evaluation parameters include a termination factor lower threshold and a termination state coefficient lower threshold, and the obtaining the preset condition according to the normalized termination factor and the preset optimized process evaluation parameters includes:
determining a first number of times that the normalized termination factor is smaller than the lower threshold of the termination factor;
and determining the condition when the first times is equal to the termination state coefficient lower limit threshold and the normalized termination factor is smaller than the termination factor lower limit threshold as a preset condition.
11. A parameter tuning system of a control system, wherein the parameter integration system comprises:
the acquisition module is used for acquiring the current practical feasible control parameter combination of the control system and sending the practical feasible control parameter combination to the control system so that the control system operates according to the practical feasible control parameter combination to obtain a response measurement value of the control system;
the receiving module is used for receiving the current response measured value sent by the control system;
the evaluation module is used for evaluating the current response measurement value according to the current combined control performance index to obtain the current control performance evaluation result;
and the first determining module is used for determining a target control parameter combination according to the current practical feasible control parameter combination if the current control performance evaluation result meets a preset condition.
12. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 10 when executing the computer program.
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