CN115933597A - Parameter setting method, system and computer equipment of control system - Google Patents

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

Control system parameter setting method, system and computer equipment

Technical Field

The present application relates to the technical field of nuclear power plant control systems, and in particular to a control system parameter setting method, system and computer equipment.

Background Art

The control system of a nuclear power plant is the basis for the safe, stable and economical operation of the nuclear power plant. The control performance optimization of the nuclear power plant control system is achieved through the tuning optimization of control parameters. For example, the tuning optimization of proportional-differential-integral controller (PID) parameters can improve the transient response capability of nuclear power units, thereby improving the stability, safety and economy of nuclear power plant operation. At the same time, this is also an important task in the design, commissioning and operation and maintenance of nuclear power plant control systems.

In traditional technology, operation and maintenance personnel usually design control parameters based on their own experience and the relationship model between control parameters and control performance, and optimize the control parameters through multiple experiments.

However, the optimization adjustment of the control parameters in the above method is overly dependent on expert experience, resulting in the final setting value of the control parameter being difficult to guarantee the performance of the nuclear power plant control system.

Summary of the invention

Based on this, it is necessary to provide a control system parameter setting method, system and computer equipment that can avoid over-reliance on expert experience and improve the accuracy of the final control parameters, thereby ensuring the performance of the nuclear power plant control system in response to the above technical problems.

In a first aspect, the present application provides a method for parameter setting of a control system. The method comprises:

Acquire the current practical feasible control parameter combination of the control system, and send 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;

Evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result;

If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination.

In one embodiment, the method further comprises:

Determining a first difference between the current response measurement and a set point of the control system;

Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the running time of the control system;

The current composite control performance index is determined 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 step of obtaining the current practical feasible control parameter combination of the control system includes:

Obtain 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;

According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by an optimization algorithm to obtain a current second control parameter combination;

The second control parameter combination is restored to obtain the current actual feasible control parameter combination.

In one embodiment, the first control parameter combination is optimized by an optimization algorithm according to the current optimization algorithm parameter and the first control parameter combination to obtain the current second control parameter combination, including:

Determine the product result of the current perturbation step length and the Monte Carlo perturbation vector; the current perturbation step length is determined according to the current optimization algorithm parameters;

Determine a sum of the first control parameter combination and the current multiplication result, and use the sum as the current positive perturbation point;

If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used 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 perturbation point does not meet the preset condition, then determine the second difference between the first control parameter combination and the current multiplication result, and use the second difference as the current negative perturbation point;

If the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, the negative perturbation point is used as the second control parameter combination for the current time.

In one embodiment, the method further comprises:

If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset condition, then 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, the current optimized estimation point is obtained; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point;

If the control performance evaluation result corresponding to the optimized estimation point meets the preset condition, the optimized estimation point is used as the second control parameter combination for the current time.

In one embodiment, the current optimization estimation point is obtained 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, including:

Determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;

Determine 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;

Based on the ratio and the current first control parameter combination, the current optimization estimation point is obtained.

In one embodiment, the method further comprises:

If the control performance evaluation result corresponding to the optimization estimation point does not meet the preset condition, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value.

In one embodiment, the method further comprises:

Obtaining historical control performance evaluation results and preset optimization process evaluation parameters before the current time;

The historical control performance evaluation results are sorted to obtain the performance sequence arrangement results of the iteration points;

According to the preset optimization process evaluation parameter and the performance sequence arrangement result of the iteration point, the performance sequence arrangement result of the iteration point is smoothed to obtain a smooth termination sequence;

According to the smooth termination sequence, a termination factor is determined, and the termination factor is normalized to obtain a normalized termination factor;

The preset condition is obtained according to the normalized termination factor and the preset optimization process evaluation parameter.

In one embodiment, the preset optimization process evaluation parameters include a termination factor lower limit threshold and a termination state coefficient lower limit threshold, and the preset conditions are obtained according to the normalized termination factor and the preset optimization process evaluation parameters, including:

Determine the first number of times that the normalized termination factor is less than a lower threshold of the termination factor;

The condition that the first number is equal to the lower limit threshold of the termination state coefficient and the normalized termination factor is less than the lower limit threshold of the termination factor is determined as the preset condition.

In a second aspect, the present application also provides a parameter setting system for a control system. The overall parameter setting system includes:

An acquisition module, used for acquiring a 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;

A receiving module, used for receiving the current response measurement value sent by the control system;

An evaluation module, used to evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result;

The first determination module is used to determine a target control parameter combination according to the current actual feasible control parameter combination if the current control performance evaluation result meets a preset condition.

In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any method in the first aspect when executing the computer program.

In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any method in the first aspect when executed by a processor.

In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of any method in the first aspect are implemented.

The parameter setting method, system and computer equipment of the above-mentioned control system obtain the 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 the response measurement value of the control system, and then receives the current response measurement value sent by the control system, thereby evaluating the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result. If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. Since the control system operates according to the current actual feasible control parameter combination to obtain the response measurement value of the control system, and then evaluates the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result, it is judged whether the current control performance evaluation result meets the preset conditions. If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. Since this embodiment does not need to rely on the experience of the operation and maintenance personnel themselves, and the target control parameter combination finally determined is the parameter combination determined according to the actual feasible control parameters corresponding to the control performance evaluation result that meets the preset conditions, it is possible to improve the accuracy of the control parameters finally determined and ensure the performance of the nuclear power plant control system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is an application environment diagram of a control system parameter setting method provided by an embodiment of the present application;

FIG2 is a flow chart of a method for parameter setting of a control system provided in an embodiment of the present application;

FIG3 is a schematic diagram of a method flow chart of obtaining a preset condition provided in an embodiment of the present application;

FIG4 is a flow chart of a method for determining a current composite control performance index provided in an embodiment of the present application;

FIG5 is a flow chart of a method for obtaining a practical and feasible control parameter combination provided in an embodiment of the present application;

FIG6 is a schematic flow chart of a method for determining a current second control parameter combination provided by an embodiment of the present application;

FIG7 is a schematic diagram of a flow chart of a method for determining a current negative perturbation point provided by the present application;

FIG8 is a schematic diagram of a flow chart of a method for obtaining a current optimization estimation point provided by an embodiment of the present application;

FIG9 is a flow chart of another control system parameter setting method provided in an embodiment of the present application;

FIG10 is a structural block diagram of a parameter setting system of a control system provided in an embodiment of the present application;

FIG. 11 is an internal structure diagram of a computer device provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

With the widespread use of digital nuclear power plants, the use of power plant data to optimize the performance of nuclear power plant control systems can ensure the safe operation of nuclear power plants and make the economic and social benefits of nuclear power plants significant. At present, the control performance optimization methods for nuclear power plant control systems at home and abroad are usually:

The first is the trial and error method, which usually involves engineers constantly adjusting the setting values of control parameters based on their operating experience and conducting experiments until an optimal combination of control parameters is found. However, this method is overly dependent on the experience of engineers, and the control parameter setting process is time-consuming and laborious, and the optimality of the setting value cannot be guaranteed.

继电反馈方法，且该方法主要用于PID参数整定。通常需要工程师先通过暂态响应试验、控制参数估计或频率响应试验获得模型，然后再根据经验整定公式给出参数整定值。虽然该方法实施简单，但由于过度依赖于模型，且模型的精确度难以或不可能获得，并经验公式的选取需要依赖于工程师经验以及对过程特性的了解程度。因此，确定的控制参数整定值并非是最优值。The second is the empirical formula method, such as the Ziegler-Nichols setting method and

Relay feedback method, and this method is mainly used for PID parameter tuning. Usually engineers need to obtain the model through transient response test, control parameter estimation or frequency response test, and then give the parameter setting value according to the empirical tuning formula. Although this method is simple to implement, it is overly dependent on the model, and the accuracy of the model is difficult or impossible to obtain, and the selection of the empirical formula depends on the engineer's experience and understanding of the process characteristics. Therefore, the determined control parameter setting value is not the optimal value.

The third is the optimization method based on the known control performance model. Assuming that the model between control performance and control parameters is known, the control parameters are optimized by optimizing the model. However, since the correlation between control performance and control parameters is very complex, it is usually difficult to obtain an accurate model.

Fourth, the particle swarm optimization algorithm (PSO) is a typical intelligent optimization method. Although this optimization method does not rely on the control performance model, it requires a large number of experiments to complete the adjustment of control parameters. The optimization cost is extremely high and it is not suitable for use in engineering design, debugging, and operation and maintenance.

However, the above optimization methods all have high requirements for model accuracy, high optimization costs, and excessive reliance on the experience of experts such as designers, commissioning, operation and maintenance personnel, and engineers. As a result, the final control parameter setting values are difficult to guarantee the performance of the nuclear power plant control system. If they are improperly set manually, they may lead to unexpected operating conditions such as unplanned shutdowns and reactor shutdowns of power plants and other high-risk activities due to human failures.

In order to solve the above technical problems, the embodiment of the present application provides a parameter setting method of a control system, which can be applied in the application environment shown in FIG1. The control system 102 communicates with the optimization system 104 through a network. The control system 102 is deployed on various personal computers, laptops, smart phones and tablet computers. The optimization system 104 is deployed on a computer device.

In one embodiment, as shown in FIG2 , FIG2 is a flow chart of a control system parameter setting method provided by an embodiment of the present application, and the method can be applied to the above-mentioned optimization system, which is deployed on a computer device. The method includes the following steps:

S201. Acquire the current practical feasible control parameter combination of the control system, and send 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 practical 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 practical feasible control parameter combination, and runs 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 practical feasible iterative control parameter combination, and measures 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.

S202: Receive the current response measurement value sent by the control system.

The above example is used to illustrate the response measurement value of the nuclear power plant control system sent by the detection sensor unit of the nuclear power plant control system.

S203. Evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result.

In this embodiment, the composite control performance index is determined by the current time absolute error integral index (ITAE), the weight coefficient λ ₂ corresponding to ITAE, the time square error integral index (ITSE) and the weight coefficient 1-λ ₂ corresponding to ITSE.

S204: If the current control performance evaluation result meets the preset conditions, determine the target control parameter combination according to the current actual feasible control parameter combination.

The preset conditions in this embodiment are determined based on the control performance evaluation results and optimization process evaluation parameters corresponding to all practical feasible control parameter combinations.

The method provided in this embodiment obtains the actual feasible control parameter combination of the control system at the current time, and sends 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 the response measurement value of the control system, and then receives the current response measurement value sent by the control system, thereby evaluating the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result. If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. Since the control system operates according to the current actual feasible control parameter combination to obtain the response measurement value of the control system, and then evaluates the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result, it is judged whether the current control performance evaluation result meets the preset conditions. If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. Since this embodiment does not need to rely on the experience of the operation and maintenance personnel themselves, and the target control parameter combination finally determined is obtained according to this method until a parameter combination determined according to the actual feasible control parameters corresponding to the control performance evaluation result that meets the preset conditions is obtained, the accuracy of the finally determined control parameters can be improved to ensure the performance of the nuclear power plant control system.

Referring to FIG. 3 , FIG. 3 is a flow chart of a method for obtaining a preset condition provided in an embodiment of the present application. This embodiment relates to a possible implementation method of how to obtain the preset condition. Based on the above embodiment, it specifically includes the following steps:

S301, obtaining historical control performance evaluation results before the current time and preset optimization process evaluation parameters.

The historical control performance evaluation results corresponding to all practical feasible control parameter combinations before the current time are obtained and stored in the sequence to obtain the historical iteration point performance sequence S _H . The current control performance evaluation result is stored in the historical iteration point performance sequence S _H , and the historical iteration point performance sequence S _H is updated. The optimization process evaluation parameters include: the initial value of the termination state coefficient κ, the lower limit threshold of the termination state coefficient κ _F , the lower limit threshold of the termination factor ζ _T , the smoothing coefficient λ, and the sliding termination coefficient η.

分别对应的历史控制性能评估结果Y₁、Y₂、Y₃和Y₄存储到序列中，得到历史迭代点性能序列S_H{Y₁、Y₂、Y₃、Y₄}，将当前次的实际可行控制参数组合

对应的控制性能评估结果Y₅存储到历史迭代点性能序列S_H{Y₁、Y₂、Y₃、Y₄}，更新历史迭代点性能序列S_H为{Y₁、Y₂、Y₃、Y₄、Y₅}。For example, all practically feasible control parameters before the current time are combined

The corresponding historical control performance evaluation results Y ₁ , Y ₂ , Y ₃ and Y ₄ are stored in the sequence to obtain the historical iteration point performance sequence S _H {Y ₁ , Y ₂ , Y ₃ , Y ₄ }. The actual feasible control parameter combination of the current time

The corresponding control performance evaluation result _Y5 is stored in the historical iteration point performance sequence _SH { _Y1 , _Y2 , _Y3 , _Y4 }, and the historical iteration point performance sequence _SH is updated to { _Y1 , _Y2 , _Y3 , _Y4 , _Y5 }.

S302: Sort the historical control performance evaluation results to obtain the iteration point performance sequence arrangement results.

Among them, the control performance evaluation results corresponding to all actual feasible control parameter combinations in the historical iteration point performance sequence _SH are sorted according to the control performance evaluation results to obtain the iteration point performance sequence arrangement result, that is, the relatively optimal iteration point performance sequence _SRO , and the control performance evaluation results corresponding to the current actual feasible control parameter combination are sorted to update the relatively optimal iteration point performance sequence _SRO . The current actual feasible control parameter combination refers to the new actual feasible control parameter combination, which refers to the new parameter combination relative to the actual feasible control parameter combination of the historical times before the current time.

对应的控制性能评估结果Y₅的控制性能优于相对最优迭代点性能序列S_RO{Y₁、Y₃、Y₂、Y₄}中任意一个控制性能评估结果，则更新相对最优迭代点性能序列S_RO为{Y₅、Y₁、Y₃、Y₂、Y₄}。Combined with the above example, all control performance evaluation results in the historical iteration point performance sequence _SH are sorted according to the control performance evaluation results to be Y ₁ >Y ₃ >Y ₂ >Y ₄ , then the relative optimal iteration point performance sequence _SR0 is {Y ₁ , Y ₃ , Y ₂ , Y ₄ }.

If the control performance of the corresponding control performance evaluation result Y ₅ is better than any control performance evaluation result in the relative optimal iteration point performance sequence S _RO {Y ₁ , Y ₃ , Y ₂ , Y ₄ }, the relative optimal iteration point performance sequence S _RO is updated to {Y ₅ , Y ₁ , Y ₃ , Y ₂ , Y ₄ }.

The relative optimal iteration point performance sequence S _RO is specifically calculated by the following formula (1):

S _RO (i) = min (S _H ) (1)

Wherein, i represents the i-th number of the optimal iteration point performance sequence S _RO .

S303: Smoothing the results of the performance sequence arrangement of the iteration points according to the preset optimization process evaluation parameters and the results of the performance sequence arrangement of the iteration points to obtain a smoothed termination sequence.

Among them, the arrangement result of the iteration point performance sequence is smoothed to obtain a smooth termination sequence, which specifically includes:

The result of arranging the iteration point performance sequence, that is, the relatively optimal iteration point performance sequence S _RO, is smoothed by a sliding average method to obtain a smooth trend sequence S _ST ; the smooth trend sequence S _ST is further smoothed to obtain a smooth termination sequence S _TM , wherein the smooth termination sequence S _TM is used to control the termination of the iteration of the actual feasible control parameter combination.

The smoothed trend sequence S _ST is calculated by the following formula (2):

Among them, n is the dimension of the parameter, λ is the smoothing coefficient, and k ₁ represents the k _1th number of the optimal iteration point performance sequence S _RO .

The smooth termination sequence S _TM is specifically calculated by the following formula (3):

Where η is the sliding termination coefficient.

S304: Determine a termination factor according to the smoothed termination sequence, and normalize the termination factor to obtain a normalized termination factor.

Among them, according to the smooth termination sequence, the termination factor is determined, and the termination factor is normalized to obtain the normalized termination factor, which specifically includes:

A differential control sequence ΔS _TM is calculated according to the smoothed termination sequence S _TM ; a termination factor ξ(i) is calculated according to the differential control sequence ΔS _TM , and the termination factor ξ(i) is normalized to obtain a normalized termination factor ζ(i).

The differential control sequence ΔS _TM is specifically calculated by the following formula (4):

The normalized termination factor ζ(i) is calculated by the following formulas (5) and (6):

Among them, min(S _ξ ) is the minimum value of the termination factor ξ(i), and max(S _ξ ) is the maximum value of the termination factor ξ(i).

S305 , obtaining preset conditions according to the normalized termination factor and preset optimization process evaluation parameters.

On the basis of the above embodiment, an optional implementation method of obtaining the preset condition according to the normalized termination factor and the preset optimization process evaluation parameter specifically includes:

Determine the first number of times that the normalized termination factor is less than the lower limit threshold of the termination factor; determine the condition when the first number is equal to the lower limit threshold of the termination state coefficient and the normalized termination factor is less than the lower limit threshold of the termination factor as the preset condition.

Among them, if the normalized termination factor ζ(i) is less than the termination factor lower limit threshold ζ _T , and the number of times κ that satisfies ζ(i)<ζ _T is equal to the termination state coefficient lower limit threshold κ _F , then the current control performance evaluation result meets the preset conditions.

The preset condition is specifically calculated by the following formula (7):

(ζ(i)<ζ _T )∩(κ＝κ _F ) (7)

The control system parameter setting method provided in the present embodiment obtains the actual feasible control parameter combination of the control system at the current time, and sends 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 the response measurement value of the control system, and then receives the current response measurement value sent by the control system, thereby evaluating the current response measurement value 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 conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. Since the control system operates according to the current actual feasible control parameter combination to obtain the response measurement value of the control system, and then evaluates the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result, it is judged whether the current control performance evaluation result meets the preset conditions. If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination; if the current control performance evaluation result does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and then it is judged whether the next control performance evaluation result meets the preset conditions according to the method provided in this embodiment to obtain the judgment result of whether the next control performance evaluation result meets the preset conditions. This method is followed until the control performance evaluation result that meets the preset conditions is obtained, and the target control parameter combination is determined by the actual feasible control parameter combination corresponding to the control performance evaluation result that meets the preset conditions, thereby avoiding excessive reliance on expert experience and ensuring the performance of the control system corresponding to the target control parameter combination.

In some embodiments, as shown in FIG4, FIG4 is a flow chart of a method for determining a composite control performance index of the current time provided by an embodiment of the present application. This embodiment relates to an optional implementation method of how to determine the composite control performance index of the current time. Based on the above embodiment, the method for determining the composite control performance index of the current time specifically includes the following steps:

S401. Determine a first difference between a current response measurement value and a set value of a control system.

S402 . Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the operating time of the control system.

S403. Determine the current composite control performance index according to the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE.

In this embodiment, the first difference e(t) between the current response measurement value and the set value of the control system is determined first, and then the current time multiplied absolute error integral index ITAE and the time multiplied square error integral index ITSE are determined according to the first difference e(t) and the running time t of the control system. Finally, the current composite control performance index Y is determined according to the current ITAE, the weight coefficient λ ₂ corresponding to ITAE, ITSE and the weight coefficient 1-λ ₂ corresponding to ITSE, where the value range of λ ₂ is (0, 1).

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)

in,

The parameter setting method of the control system provided in the present embodiment determines the first difference between the current response measurement value and the setting value of the control system, and determines the current time multiplied absolute error integral index ITAE and the time multiplied square error integral index ITSE according to the first difference and the running time of the control system, and then determines the current composite control performance index according to the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE. In the present embodiment, by determining the first difference between the current response measurement value and the setting value of the control system, the first difference and the running time of the control system determine the current time multiplied absolute error integral index ITAE and the time multiplied square error integral index ITSE, and then determines the current composite control performance index according to the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE, so that the current response measurement value can be evaluated according to the current composite control performance index, and it is judged whether the current control performance evaluation result meets the preset conditions, so as to determine whether to continue to optimize and iterate the current actual feasible control parameter combination, so as to solve the problem of excessive reliance on expert experience in the optimization adjustment of control parameters in traditional technology.

Referring to FIG. 5 , FIG. 5 is a flow chart of a method for obtaining an actual feasible control parameter combination provided by an embodiment of the present application. This embodiment relates to an optional implementation of how to obtain the actual feasible control parameter combination of the current control system. Based on the above embodiment, the method for obtaining the actual feasible control parameter combination of the current control system in S201 specifically includes the following steps:

S501. Obtain the current initial control parameter combination and optimization algorithm parameters of the optimization system.

The optimization algorithm parameters include: initial iteration step size factor a, step size correction reference parameter A, step size factor dynamic correction factor α, step size reference factor c, perturbation step size attenuation factor γ and iteration operator s.

In this embodiment, the current optimization algorithm parameters {a, A, c, α, γ}, the current iteration operator s and the current initial control parameter combination X _k are obtained, where k is a counting operator of the control parameter combination. For example, when the iteration operator s=1, the current initial control parameter combination X ₀ and the initial values of the current optimization algorithm parameters {a, A, c, α, γ} set by the engineer are obtained.

S502: Normalize the current initial control parameter combination to obtain a first control parameter combination.

具体是通过如下公式(9)进行归一化处理：In this embodiment, the first control parameter combination Xk is obtained by normalizing the current control parameter combination _Xk .

Specifically, the normalization is performed using the following formula (9):

为当前次的初始控制参数组合，

为当前次的第一控制参数组合，

为第t个控制参数的初值，t＝1,2,…,n，n为控制参数的个数，(X_k ^t)^L＝inf(X_k ^t)为下界，(X_k ^t)^H＝sup(X_k ^t)为上界。in,

is the initial control parameter combination for the current time,

is the first control parameter combination of the current time,

is the initial value of the tth control parameter, t = 1, 2, …, n, n is the number of control parameters, (X _k ^t ) ^L = inf(X _k ^t ) is the lower bound, and (X _k ^t ) ^H = sup(X _k ^t ) is the upper bound.

For example, by normalizing the current initial control parameter combination X ₀ when the current iteration operator s=1, the first control parameter combination X ₀ is calculated by the above formula (9).

S503: According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by using an optimization algorithm to obtain the current second control parameter combination.

Among them, the optimization algorithm adopted is the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. SPSA gradually approaches the optimal solution by estimating the gradient information of the objective function.

S504: Perform restoration processing on the second control parameter combination to obtain the current actual feasible control parameter combination.

进行还原处理，得到当前次的实际可行控制参数组合

具体是通过如下公式(10)计算：In this embodiment, by combining the second control parameter

Perform restoration processing to obtain the current actual feasible control parameter combination

Specifically, it is calculated by the following formula (10):

The method provided in this embodiment obtains the current initial control parameter combination and optimization algorithm parameters of the optimization system, and normalizes the current initial control parameter combination to obtain a first control parameter combination, and then optimizes the first control parameter combination through the optimization algorithm according to the current optimization algorithm parameters and the first control parameter combination to obtain the current second control parameter combination, and then restores the second control parameter combination to obtain the current actual feasible control parameter combination. Since this implementation optimizes the first control parameter combination through the optimization algorithm to obtain the current second control parameter combination, and restores the second control parameter combination to obtain the current actual feasible control parameter combination, the number of experiments required for optimization iterations during the optimization process can be reduced to improve the efficiency of control parameter combination setting.

6, which is a flow chart of a method for determining a current second control parameter combination provided by an embodiment of the present application. This embodiment relates to an optional implementation method of how to optimize the first control parameter combination by an optimization algorithm according to the current optimization algorithm parameters and the first control parameter combination to obtain the current second control parameter combination. On the basis of the above embodiment, the above S503 specifically includes the following steps:

S601, determining the product result of the current perturbation step length and the Monte Carlo perturbation vector; the current perturbation step length is determined according to the current optimization algorithm parameters.

Among them, the Monte Carlo perturbation vector _Δs is an n-dimensional vector, each one-dimensional vector is randomly generated by Bernoulli distribution ±1, and each one-dimensional vector element is independent and satisfies the zero mean principle.

The current perturbation step size is determined according to the current optimization algorithm parameters. Optionally, the current perturbation step size _cs is calculated by the current iteration operator s, the step size reference factor c and the perturbation step size attenuation factor γ, and is specifically calculated by the following formula (11):

In this embodiment, the product result c _s Δ _s of the current perturbation step length c _s and the Monte Carlo perturbation vector Δ _s is determined.

S602: Determine a sum of the first control parameter combination and the current multiplication result, and use the sum as the current positive perturbation point.

是通过第一控制参数组合

和当前次的乘积结果c_sΔ_s计算得到，具体是通过如下公式(12)计算：In this embodiment, the current positive perturbation point

is the combination of the first control parameters

The product result c _s Δ _s of the current time is calculated, which is specifically calculated by the following formula (12):

与当前次的乘积结果的求和结果c₁Δ₁，并将求和结果c₁Δ₁作为当前次的正摄动点

For example, determine the first control parameter combination when the current iteration operator s=1

The sum of the product result c ₁ Δ ₁ and the current product result c ₁ Δ ₁ is used as the positive perturbation point of the current time.

添加至迭代点性能序列中。It should be noted that the performance group of the iteration point formed

Added to the iteration point performance sequence.

S603: If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used as the current second control parameter combination.

所对应的控制性能评估结果Y_k+1满足预设条件，则将当前次的正摄动点

作为当前次的第二控制参数组合；若当前次的正摄动点

所对应的控制性能评估结果Y_k+1不满足预设条件，则继续执行下述步骤S701至S702获取当前次的负摄动点

作为当前次的第二控制参数组合。In this embodiment, if the current positive perturbation point

If the corresponding control performance evaluation result Y _k+1 meets the preset conditions, the current positive perturbation point

As the second control parameter combination of the current time; if the current positive perturbation point

If the corresponding control performance evaluation result Y _k+1 does not meet the preset conditions, then continue to execute the following steps S701 to S702 to obtain the current negative perturbation point

As the second control parameter combination for the current time.

所对应的控制性能评估结果Y₁满足预设条件，则将当前次的正摄动点

作为当前次的第二控制参数组合。Combined with the above example, if the current positive perturbation point is

If the corresponding control performance evaluation result _Y1 meets the preset conditions, the current positive perturbation point

As the second control parameter combination for the current time.

The method provided in this embodiment determines the product result of the current perturbation step length and the Monte Carlo perturbation vector, determines the sum result of the first control parameter combination and the current product result, and uses the sum result as the current positive perturbation point. If the control performance evaluation result corresponding to the current positive perturbation point meets the preset conditions, the positive perturbation point is used as the current second control parameter combination. That is to say, this embodiment determines the sum result of the first control parameter combination and the current product result according to the current perturbation step length and the Monte Carlo perturbation vector, and uses the sum result as the current positive perturbation point. By judging whether the control performance evaluation result corresponding to the current positive perturbation point meets the preset conditions, it is determined whether to continue to optimize the current positive perturbation point, thereby reducing the number of experiments required for optimization iterations in the optimization process to improve the efficiency of control parameter combination tuning.

Optionally, as shown in Figure 7, Figure 7 is a flow chart of a method for determining the current negative perturbation point provided by the present application. This embodiment relates to how to determine the current negative perturbation point. Based on the above embodiment, the following implementation methods may also be included:

S701. If the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset conditions, determine the second difference between the first control parameter combination and the current multiplication result, and use the second difference as the current negative perturbation point.

是通过第一控制参数组合

和当前次的乘积结果c_sΔ_s计算得到，具体是通过如下公式(13)计算：In this embodiment, the current negative perturbation point

is the combination of the first control parameters

The product result c _s Δ _s of the current time is calculated, which is specifically calculated by the following formula (13):

所对应的控制性能评估结果Y₁不满足预设条件，则确定第一控制参数组合

和当前次的乘积结果c_sΔ_s的第二差值，并将第二差值作为当前次的负摄动点

Combined with the above example, if the current positive perturbation point is

If the corresponding control performance evaluation result _Y1 does not meet the preset conditions, the first control parameter combination is determined

The second difference between the product result c _s Δ _s and the current one, and the second difference is used as the negative perturbation point of the current one

添加至迭代点性能序列中。It should be noted that the performance group of the iteration point formed

Added to the iteration point performance sequence.

S702: If the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, the negative perturbation point is used as the current second control parameter combination.

所对应的控制性能评估结果Y_k+2满足预设条件，则将当前次的负摄动点

作为当前次的第二控制参数组合；若当前次的负摄动点

所对应的控制性能评估结果Y_k+2不满足预设条件，则继续执行下述步骤S801至S802获取当前次的优化估计点

作为当前次的第二控制参数组合。In this embodiment, if the current negative perturbation point

If the corresponding control performance evaluation result Y _k+2 meets the preset conditions, the current negative perturbation point

As the second control parameter combination of the current time; if the negative perturbation point of the current time

If the corresponding control performance evaluation result Y _k+2 does not meet the preset conditions, then continue to execute the following steps S801 to S802 to obtain the current optimization estimation point

As the second control parameter combination for the current time.

所对应的控制性能评估结果Y₂满足预设条件，则将当前次的负摄动点

作为当前次的第二控制参数组合。Combined with the above example, if the current negative perturbation point

If the corresponding control performance evaluation result _Y2 meets the preset conditions, the current negative perturbation point

As the second control parameter combination for the current time.

The method provided by this embodiment determines the second difference between the multiplication result of the first control parameter combination and the current time, and uses the second difference as the negative perturbation point of the current time. If the control performance evaluation result corresponding to the negative perturbation point of the current time meets the preset conditions, the negative perturbation point is used as the second control parameter combination of the current time. That is to say, this embodiment determines the second difference between the multiplication result of the first control parameter combination and the current time, and uses the second difference as the negative perturbation point of the current time. By judging whether the control performance evaluation result corresponding to the negative perturbation point of the current time meets the preset conditions, it is determined whether to continue to optimize the negative perturbation point of the current time to obtain the target control parameter combination, thereby improving the setting accuracy of the target control parameter combination to ensure the performance of the control system corresponding to the target control parameter combination.

Referring to Figure 8, Figure 8 is a flow chart of a method for obtaining the current optimization estimation point provided by an embodiment of the present application. This embodiment involves how to determine the current optimization estimation point. Based on the above embodiment, the following implementation methods may also be included:

S801. If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset conditions, the current optimization estimation point is obtained 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.

The first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point.

当前次的负摄动点

以及第一控制参数组合

得到当前次的优化估计点

In this embodiment, according to the first historical control performance evaluation result Y _k+1 , the second historical control performance evaluation result Y _k+2 , and the current positive perturbation point

The current negative perturbation point

And the first control parameter combination

Get the current optimization estimate point

添加至迭代点性能序列中，并将当前次的优化估计点

添加至优化估计点性能序列中。It should be noted that the performance group of the iteration point formed

Add to the iteration point performance sequence and make the current optimization estimate point

Added to the optimization estimate point performance sequence.

所对应的控制性能评估结果Y₂不满足预设条件，则根据第一历史控制性能评估结果Y₁、第二历史控制性能评估结果Y₂、当前次的正摄动点

当前次的负摄动点

以及第一控制参数组合

得到当前次的优化估计点

Combined with the above example, if the current negative perturbation point is

If the corresponding control performance evaluation result _Y2 does not meet the preset conditions, then according to the first historical control performance evaluation result _Y1 , the second historical control performance evaluation result _Y2 , the current positive perturbation point

The current negative perturbation point

And the first control parameter combination

Get the current optimization estimate point

S802: If the control performance evaluation result corresponding to the optimized estimation point meets the preset condition, the optimized estimation point is used as the second control parameter combination for the current time.

所对应的控制性能评估结果Y_k+3满足预设条件，则将当前次的优化估计点

作为当前次的第二控制参数组合；若当前次的优化估计点

所对应的控制性能评估结果Y_k+3不满足预设条件，则返回至上述步骤S601至S603获取下一次的正摄动点

作为当前次的第二控制参数组合，此时迭代算子s加1。In this embodiment, if the current optimization estimation point

If the corresponding control performance evaluation result Y _k+3 meets the preset conditions, the current optimization estimation point

As the second control parameter combination for the current time; if the current optimization estimation point

If the corresponding control performance evaluation result Y _k+3 does not meet the preset conditions, then return to the above steps S601 to S603 to obtain the next positive perturbation point

As the second control parameter combination for the current time, the iteration operator s is increased by 1.

It should be noted that: when performing the next optimization, the first control parameter combination is the optimization estimation point of the previous time.

The method provided in this embodiment obtains the current optimization 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, and determines whether to continue to optimize the current optimization estimation point to obtain the target control parameter combination by judging whether the control performance evaluation result corresponding to the current optimization estimation point meets the preset conditions, thereby improving the setting accuracy of the target control parameter combination to ensure the performance of the control system corresponding to the target control parameter combination.

On the basis of the above embodiment, this embodiment involves how to obtain the current optimization 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. The above S801, 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, obtaining the current optimization estimation point can also be achieved in the following way:

Determine the third difference between the first historical control performance evaluation result and the second historical control performance evaluation result, determine the fourth difference between the current positive perturbation point and the current negative perturbation point, determine the ratio between the third difference and the fourth difference, and obtain the current optimization estimation point based on the ratio and the current first control parameter combination.

最后确定第三差值与第四差值之间的比值g(s)，即当前次的梯度近似值g(s)，具体是通过如下公式(14)计算：In this embodiment, the third difference between the first historical control performance evaluation result and the second historical control performance evaluation result is first determined to be Y _k+1 -Y _k+2 , and then the fourth difference between the current positive perturbation point and the current negative perturbation point is determined to be

Finally, the ratio g(s) between the third difference and the fourth difference is determined, that is, the current gradient approximation g(s), which is specifically calculated by the following formula (14):

均来自迭代点性能序列，由于当前次的梯度近似值g(s)为一个向量，则将当前次的梯度近似值g(s)存入梯度估计序列中。in,

They all come from the iteration point performance sequence. Since the current gradient approximation g(s) is a vector, the current gradient approximation g(s) is stored in the gradient estimation sequence.

On the basis of the above embodiment, based on the ratio and the first control parameter combination of the current time, the current optimization estimation point is obtained, which is specifically calculated by the following formulas (15)-(28):

Among them, G(s) is the corrected gradient value of the current time, g(s) is the gradient approximation of the current time and _ρs is the gradient compensation factor corresponding to the current iterative operator s.

Among them, ρ _Bench is the compensation factor benchmark value, ρ _E is the compensation factor correction deviation value, s is the current iteration operator and σ is the correction coefficient.

d _s =a _s ×(1+max(d _sG ,d _sI )) (17)

Among them, _ds is the step size dynamic adjustment operator, _as is the iteration step size factor, _dsG is the gradient correction step size adjustment operator, and _dsI is the adjacent perturbation step size adjustment operator.

Among them, A is the step size correction reference parameter, a is the iteration step size factor, s is the current iteration operator and α is the step size factor dynamic correction factor.

Among them, _dsG is the gradient correction step size adjustment operator, _RG is the step size adjustment constant, ω is the operator iteration adjustment coefficient, _as is the iteration step size factor and _SIs is the indicator factor corresponding to the current iteration operator s.

Among them, G(s) is the corrected gradient value of the current time and g(s) is the approximate gradient value of the current time.

是当前次的迭代算子s对应的优化进程状态信号、a_s是迭代步长因子和

是迭代步长调整因子。其中，步长调节系数ξ的取值范围在(0,1)。Among them, _dsI is the adjacent perturbation step adjustment operator, ξ is the step adjustment coefficient,

is the optimization process status signal corresponding to the current iteration operator s, a _s is the iteration step factor and

is the iteration step adjustment factor. The value range of the step adjustment coefficient ξ is (0,1).

是前一次的优化估计点、

是当前次的优化估计点和

是前前次的优化估计点。in,

is the previous optimization estimate point,

is the current optimization estimate point and

is the previous optimization estimate point.

Among them, sgn(·) is the sign function, and the discriminant factor is constructed as follows:

是当前次的优化估计点所对应的控制性能评估结果、

是前一次的优化估计点所对应的控制性能评估结果、

是前前次的优化估计点所对应的控制性能评估结果和δ是相对变化因子。in,

is the control performance evaluation result corresponding to the current optimization estimation point,

is the control performance evaluation result corresponding to the previous optimization estimation point,

is the control performance evaluation result corresponding to the previous optimization estimation point and δ is the relative change factor.

It should be noted that the current iteration operator s≥3 in constructing the discriminant factor.

or

The method provided in this embodiment determines the third difference between the first historical control performance evaluation result and the second historical control performance evaluation result, and determines the fourth difference between the current positive perturbation point and the current negative perturbation point, and then determines the ratio between the third difference and the fourth difference, so that the current optimization estimation point can be obtained based on the ratio and the current first control parameter combination.

Based on the above embodiments, the following implementations may also be included:

If the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value.

所对应的控制性能评估结果Y₃不满足预设条件，则获取迭代算子s＝2对应的控制系统的下一次的实际可行控制参数组合

并向控制系统发送下一次的实际可行控制参数组合

以得到下一次的实际可行控制参数组合

所对应的响应测量值，并根据下一次所对应的响应测量值确定目标控制参数组合。For example, if the iteration operator s = 1, the current optimization estimate point

If the corresponding control performance evaluation result _Y3 does not meet the preset conditions, the next practical feasible control parameter combination of the control system corresponding to the iteration operator s=2 is obtained.

And send the next practical control parameter combination to the control system

To obtain the next practical feasible control parameter combination

The corresponding response measurement value is used to determine the target control parameter combination according to the next corresponding response measurement value.

The method provided in this embodiment obtains the next actual feasible control parameter combination of the control system and sends the next actual feasible control parameter combination to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and determines the target control parameter combination according to the next corresponding response measurement value. This method is repeated until the target control parameter combination is determined, thereby avoiding excessive reliance on expert experience and ensuring the performance of the control system corresponding to the target control parameter combination.

In order to facilitate those skilled in the art to more clearly understand the parameter setting method of the control system provided by the present application, an explanation is given here in conjunction with FIG. 9, which is a flow chart of another parameter setting method of the control system provided by an embodiment of the present application. The parameter setting method specifically includes the following steps:

S901. Calculate the current perturbation step size and the current iteration step size factor using the current optimization algorithm parameters.

Among them, calculating the current perturbation step size and the current iteration step size factor is the algorithm gain calculation.

S902: Determine the current positive perturbation point according to the current first control parameter combination and the current perturbation step size, and judge whether the control performance evaluation result corresponding to the current positive perturbation point meets the preset conditions.

S903. Determine the current negative perturbation point according to the current first control parameter combination and the current perturbation step size, and judge whether the control performance evaluation result corresponding to the current negative perturbation point meets the preset conditions.

S904, determine the current gradient approximation value according to the current positive perturbation point, the control performance evaluation result corresponding to the current positive perturbation point, the current negative perturbation point, and the control performance evaluation result corresponding to the current negative perturbation point.

S905 . Determine the indicator factor corresponding to the current iteration operator according to the current gradient approximation value.

S906: Determine a gradient correction step size adjustment operator according to the indicator factor corresponding to the current iteration operator.

S907: Determine an adjacent perturbation step size adjustment operator according to the current iteration step size factor.

S908. Determine the dynamic adjustment of the step size according to the adjacent perturbation step size adjustment operator and the gradient correction step size adjustment operator.

S909: Determine the optimal estimation point according to the first control parameter combination and the dynamic adjustment of the step size, and judge whether the control performance evaluation result corresponding to the current positive perturbation point meets the preset conditions.

It should be understood that, although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately 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 present application also provides a parameter setting system for implementing the parameter setting method of the control system involved above. The implementation scheme for solving the problem provided by the parameter setting system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiment of the parameter setting system of one or more control systems provided below can refer to the limitations of the parameter setting method above, and will not be repeated here.

In one embodiment, as shown in FIG10 , a parameter setting system for a control system is provided. The parameter overall system 1000 includes: an acquisition module 1001, a receiving module 1002, an evaluation module 1003 and a first determination module 1004, wherein:

An acquisition module 1001 is used to acquire the current practical feasible control parameter combination of the control system, and send 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 1002 is used to receive the current response measurement value sent by the control system;

An evaluation module 1003 is used to evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result;

The first determination module 1004 is used to determine a target control parameter combination according to the current actual feasible control parameter combination if the current control performance evaluation result meets a preset condition.

In one embodiment, the parameter integration system 1000 further includes:

A second determination module, used to determine a first difference between a current response measurement value and a set value of the control system;

A third determination module is used to determine the current time multiplied absolute error integral index ITAE and the time multiplied square error integral index ITSE according to the first difference and the running time of the control system;

The fourth determination module is used to determine the current composite control performance index according to the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE.

In one embodiment, the acquisition module 1001 includes an acquisition unit, a normalization unit, an optimization unit, and a restoration unit:

An acquisition unit, used to acquire the current initial control parameter combination and optimization algorithm parameters of the optimization system;

A normalization unit, used for normalizing the current initial control parameter combination to obtain a first control parameter combination;

An optimization unit, configured to optimize the first control parameter combination by an optimization algorithm according to the current optimization algorithm parameters and the first control parameter combination, to obtain the current second control parameter combination;

The restoration unit is used to restore the second control parameter combination to obtain the current actual feasible control parameter combination.

In one of the embodiments, the optimization unit is specifically used to determine the product result of the current perturbation step size and the Monte Carlo perturbation vector; the current perturbation step size is determined according to the current optimization algorithm parameters; the sum of the first control parameter combination and the current product result is determined, and the sum result is used as the current positive perturbation point; if the control performance evaluation result corresponding to the current positive perturbation point meets the preset conditions, the positive perturbation point is used as the current second control parameter combination.

In one embodiment, the parameter integration system 1000 further includes:

A fifth determination module, for determining a second difference between the first control parameter combination and the current multiplication result if the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, and using the second difference as the current negative perturbation point;

The first judgment module is used to use the negative perturbation point as the second control parameter combination for the current time if the control performance evaluation result corresponding to the negative perturbation point for the current time meets the preset conditions.

In one embodiment, the parameter integration system 1000 further includes:

An obtaining module is used to obtain the current optimization 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 if the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset conditions; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point;

The second judgment module is used to use the optimized estimation point as the second control parameter combination for the current time if the control performance evaluation result corresponding to the optimized estimation point meets the preset condition.

In one embodiment, the obtaining module is specifically used to determine the third difference between the first historical control performance evaluation result and the second historical control performance evaluation result; determine the fourth difference between the current positive perturbation point and the current negative perturbation point; determine the ratio between the third difference and the fourth difference; based on the ratio and the current first control parameter combination, obtain the current optimization estimation point.

In one embodiment, the parameter integration system 1000 further includes:

The sixth determination module is used to obtain the next actual feasible control parameter combination of the control system if the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, and send the next actual feasible control parameter combination to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and determine the target control parameter combination according to the next corresponding response measurement value.

Each module in the parameter setting system of the control system can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG11. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used 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 the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store control performance evaluation result data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a parameter setting method of a control system is implemented.

Those skilled in the art will understand that the structure shown in FIG. 11 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

Acquire the current practical feasible control parameter combination of the control system, and send 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;

The current response measurement value is evaluated according to the current composite control performance index to obtain the current control performance evaluation result;

If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined based on the current actual feasible control parameter combination.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

Determining a first difference between a current response measurement and a set point of the control system;

Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the running time of the control system;

The current composite control performance index is determined based on the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

Obtain 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;

According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by the optimization algorithm to obtain the current second control parameter combination;

The second control parameter combination is restored to obtain the current actual feasible control parameter combination.

In one embodiment, when the processor executes the computer program, the following steps are also implemented:

Determine the product result of the current perturbation step size and the Monte Carlo perturbation vector; the current perturbation step size is determined according to the current optimization algorithm parameters;

Determine the sum of the first control parameter combination and the current multiplication result, and use the sum as the current positive perturbation point;

If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used as the current second control parameter combination.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

If the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determine the second difference between the first control parameter combination and the current multiplication result, and use the second difference as the current negative perturbation point;

If the control performance evaluation result corresponding to the current negative perturbation point meets the preset conditions, the negative perturbation point is used as the current second control parameter combination.

In one embodiment, when the processor executes the computer program, the following steps are also implemented:

If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset conditions, then 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, the current optimization estimation point is obtained; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point;

If the control performance evaluation result corresponding to the optimized estimation point meets the preset conditions, the optimized estimation point is used as the second control parameter combination for the current time.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;

Determine 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;

Based on the ratio and the current first control parameter combination, the current optimization estimation point is obtained.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

If the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Acquire the current practical feasible control parameter combination of the control system, and send 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;

Receive the current response measurement value sent by the control system;

The current response measurement value is evaluated according to the current composite control performance index to obtain the current control performance evaluation result;

If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined based on the current actual feasible control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Determining a first difference between a current response measurement and a set point of the control system;

Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the running time of the control system;

The current composite control performance index is determined based on the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Obtain 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;

According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by the optimization algorithm to obtain the current second control parameter combination;

The second control parameter combination is restored to obtain the current actual feasible control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Determine the product result of the current perturbation step size and the Monte Carlo perturbation vector; the current perturbation step size is determined according to the current optimization algorithm parameters;

Determine the sum of the first control parameter combination and the current multiplication result, and use the sum as the current positive perturbation point;

If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used as the current second control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determine the second difference between the first control parameter combination and the current multiplication result, and use the second difference as the current negative perturbation point;

If the control performance evaluation result corresponding to the current negative perturbation point meets the preset conditions, the negative perturbation point is used as the current second control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset conditions, then 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, the current optimization estimation point is obtained; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point;

If the control performance evaluation result corresponding to the optimized estimation point meets the preset conditions, the optimized estimation point is used as the second control parameter combination for the current time.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;

Determine 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;

Based on the ratio and the current first control parameter combination, the current optimization estimation point is obtained.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value.

In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

Acquire the current practical feasible control parameter combination of the control system, and send 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;

Receive the current response measurement value sent by the control system;

The current response measurement value is evaluated according to the current composite control performance index to obtain the current control performance evaluation result;

If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined based on the current actual feasible control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Determining a first difference between a current response measurement and a set point of the control system;

Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the running time of the control system;

The current composite control performance index is determined based on the current ITAE, the weight coefficient corresponding to ITAE, ITSE and the weight coefficient corresponding to ITSE.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Obtain 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;

According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by the optimization algorithm to obtain the current second control parameter combination;

The second control parameter combination is restored to obtain the current actual feasible control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Determine the product result of the current perturbation step size and the Monte Carlo perturbation vector; the current perturbation step size is determined according to the current optimization algorithm parameters;

Determine the sum of the first control parameter combination and the current multiplication result, and use the sum as the current positive perturbation point;

If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used as the current second control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determine the second difference between the first control parameter combination and the current multiplication result, and use the second difference as the current negative perturbation point;

If the control performance evaluation result corresponding to the current negative perturbation point meets the preset conditions, the negative perturbation point is used as the current second control parameter combination.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset conditions, then 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, the current optimization estimation point is obtained; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point;

If the control performance evaluation result corresponding to the optimized estimation point meets the preset conditions, the optimized estimation point is used as the second control parameter combination for the current time.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result;

Determine 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;

Based on the ratio and the current first control parameter combination, the current optimization estimation point is obtained.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

If the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value.

Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims (12)

1. A method for parameter setting of a control system, characterized in that the method comprises: Acquire a current practical feasible control parameter combination of the control system, and send 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; Evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result; If the current control performance evaluation result meets the preset conditions, the target control parameter combination is determined according to the current actual feasible control parameter combination. 2. The method according to claim 1, characterized in that the method further comprises: Determining a first difference between the current response measurement and a set point of the control system; Determine the current time multiplied absolute error integral index ITAE and the current time multiplied square error integral index ITSE according to the first difference and the running time of the control system; The current composite control performance index is determined 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 according to claim 1, characterized in that the step of obtaining the current practical feasible control parameter combination of the control system comprises: Obtain 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; According to the current optimization algorithm parameters and the first control parameter combination, the first control parameter combination is optimized by an optimization algorithm to obtain a current second control parameter combination; The second control parameter combination is restored to obtain the current actual feasible control parameter combination. 4. The method according to claim 3, characterized in that the step of optimizing the first control parameter combination by an optimization algorithm based on the current optimization algorithm parameters and the first control parameter combination to obtain the current second control parameter combination comprises: Determine the product result of the current perturbation step length and the Monte Carlo perturbation vector; the current perturbation step length is determined according to the current optimization algorithm parameters; Determine a summation result of the first control parameter combination and the current multiplication result, and use the summation result as the current positive perturbation point; If the control performance evaluation result corresponding to the current positive perturbation point meets the preset condition, the current positive perturbation point is used as the current second control parameter combination. 5. The method according to claim 4, characterized in that the method further comprises: If the control performance evaluation result corresponding to the current positive perturbation point does not meet the preset condition, determining a second difference between the first control parameter combination and the current multiplication result, and using the second difference as the current negative perturbation point; If the control performance evaluation result corresponding to the current negative perturbation point meets the preset condition, the negative perturbation point is used as the current second control parameter combination. 6. The method according to claim 5, characterized in that the method further comprises: If the control performance evaluation result corresponding to the current negative perturbation point does not meet the preset condition, then 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, the current optimized estimation point is obtained; the first historical control performance evaluation result is the control performance evaluation result corresponding to the current positive perturbation point, and the second historical control performance evaluation result is the control performance evaluation result corresponding to the current negative perturbation point; If the control performance evaluation result corresponding to the optimized estimation point meets the preset condition, the optimized estimation point is used as the second control parameter combination for the current time. 7. The method according to claim 6, characterized in that the step of obtaining the current optimization 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 comprises: determining a third difference between the first historical control performance evaluation result and the second historical control performance evaluation result; Determine 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; Based on the ratio and the current first control parameter combination, the current optimization estimation point is obtained. 8. The method according to claim 6 or 7, characterized in that the method further comprises: If the control performance evaluation result corresponding to the optimized estimation point does not meet the preset conditions, the next actual feasible control parameter combination of the control system is obtained, and the next actual feasible control parameter combination is sent to the control system to obtain the response measurement value corresponding to the next actual feasible control parameter combination, and the target control parameter combination is determined according to the next corresponding response measurement value. 9. The method according to any one of claims 1 to 7, characterized in that the method further comprises: Obtaining historical control performance evaluation results before the current time and preset optimization process evaluation parameters; Sorting the historical control performance evaluation results to obtain an iteration point performance sequence arrangement result; According to the preset optimization process evaluation parameters and the iteration point performance sequence arrangement result, the iteration point performance sequence arrangement result is smoothed to obtain a smooth termination sequence; Determining a termination factor according to the smooth termination sequence, and normalizing the termination factor to obtain a normalized termination factor; The preset condition is obtained according to the normalized termination factor and the preset optimization process evaluation parameter. 10. The method according to claim 9, characterized in that the preset optimization process evaluation parameters include a termination factor lower limit threshold and a termination state coefficient lower limit threshold, and the preset conditions are obtained according to the normalized termination factor and the preset optimization process evaluation parameters, comprising: Determine a first number of times that the normalized termination factor is less than the termination factor lower limit threshold; The condition that the first number is equal to the lower limit threshold of the termination state coefficient and the normalized termination factor is less than the lower limit threshold of the termination factor is determined as a preset condition. 11. A parameter setting system for a control system, characterized in that the overall parameter system comprises: An acquisition module, used for acquiring a 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; A receiving module, used for receiving the current response measurement value sent by the control system; An evaluation module, used to evaluate the current response measurement value according to the current composite control performance index to obtain the current control performance evaluation result; The first determination module is used to determine a target control parameter combination according to the current actual 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, wherein the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 10 when executing the computer program.

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