Performance parameter acquisition method and device, product and storage medium

Abstract

The invention relates to a performance parameter acquisition method and device, a product and a storage medium, and the method comprises the steps: unifying controller parameters and test conditions as test parameters, then determining a first test parameter and a second test parameter from a plurality of test parameters, carrying out the actual measurement of the first test parameter, and obtaining a first performance parameter, furthermore, performance parameter prediction can be performed based on the performance parameter association relationship and the first performance parameter, and the second performance parameter of the controlled object under the second test parameter is determined, so that association prediction of different test parameters is realized; on the premise of ensuring the accuracy, test work such as rapid and efficient prediction and setting of performance parameters of multiple sets of controller parameters is realized, the test efficiency is remarkably improved, and the cost is reduced.

Description

Technical Field

[0001] This application relates to the field of optical image stabilization control technology, and in particular to a method, apparatus, product and storage medium for acquiring performance parameters of an optical image stabilization system. Background Technology

[0002] In optical image stabilization (OIS) systems, multiple sets of PID controller parameters are typically required to control the motor's movement under different conditions. However, existing technologies suffer from the following prominent problems: When an OIS system is configured with multiple sets of controller parameters, each set of parameters needs to be actually measured and verified, which can lead to a significant increase in testing time and cost on mass production line equipment.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] This application provides a method, apparatus, computer program product, and storage medium for obtaining performance parameters, which can improve testing efficiency and at least partially solve the above-mentioned technical problems.

[0005] To achieve the above objectives, according to a first aspect of this application, a method for obtaining performance parameters is provided, comprising: Determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different; Based on the first test parameters, a performance parameter test is performed to obtain the first performance parameter of the controlled object in the optical image stabilization system under the first test parameters. Obtain the correlation relationship of performance parameters, which includes the correlation relationship between the first performance parameter under the first test parameter and the second performance parameter under the second test parameter; Based on the correlation between the performance parameters and the first performance parameter, the performance parameters are predicted to determine the second performance parameters of the controlled object under the second test parameters.

[0006] Optionally, the second test parameter in the aforementioned method is the same as the controller parameter of the first test parameter but the test conditions are different. The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: Call the mapping function in the performance parameter association relationship, the mapping function including the association relationship between different performance parameters of the controlled object under the same controller parameter and different test conditions; Based on the mapping function, the second performance parameter is output according to the first test condition of the first test parameter, the second test condition of the second test parameter, and the first performance parameter.

[0007] Optionally, the second test parameter in the aforementioned method is different from the controller parameter of the first test parameter, but the test conditions are the same. The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: The conversion function in the performance parameter association relationship is invoked, and the conversion function includes the association relationship between the actual response parameters and performance parameters of the controlled object under known test parameters; Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and first test conditions of the first test parameters and the first performance parameters. The second performance parameter is output based on the actual response parameters of the controlled object, the second controller parameters of the second test parameter, and the second test conditions.

[0008] Optionally, the controller parameters of the second test parameter are different from those of the first test parameter, but the test conditions are the same. The test conditions are closed-loop test conditions. The step of predicting the performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: Call the mapping and transformation functions in the performance parameter association relationship; Based on the mapping function, the first controller parameter of the first test parameter and the first performance parameter under the first test condition are converted into the first controller parameter and the first performance parameter under the open-loop test condition. Based on the transformation function, the actual response parameters of the controlled object are determined according to the first controller parameters, the open-loop test conditions, and the first performance parameters. Based on the actual response parameters of the controlled object and the second controller parameters of the second test parameters, output the second controller parameters and the second performance parameters under the open-loop test conditions; Based on the mapping function, the second performance parameter under the second controller parameters and the open-loop test conditions is converted into the second controller parameters and the second performance parameter under the second test conditions.

[0009] Optionally, the second test parameter in the aforementioned method differs from the controller parameter of the first test parameter and the test conditions are different. The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: The mapping function and the conversion function in the performance parameter association relationship are called. The mapping function includes the association relationship between different performance parameters of the controlled object under the same controller parameters and different test conditions. The conversion function includes the association relationship between the actual response parameters and performance parameters of the controlled object under known test parameters. Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and first test conditions of the first test parameters and the first performance parameters. Based on the conversion function, intermediate performance parameters are output according to the actual response parameters of the controlled object, the second controller parameters of the second test parameters, and the first test conditions; Based on the mapping function, the second performance parameter is output according to the second test condition of the second test parameter, the first test condition, and the intermediate performance parameter.

[0010] Optionally, the aforementioned method of predicting performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: The model correction function in the performance parameter correlation relationship is invoked, and the model correction function includes the correlation relationship between the theoretical model, the real model and the modeling error function of the controlled object; Based on the theoretical model of the controlled object, the first controller parameter and the first test condition of the first test parameter, and the first performance parameter, the modeling error function of the controlled object is output. Based on the model correction function, and according to the modeling error function and theoretical model of the controlled object, the true model of the controlled object is output; Based on the real model of the controlled object, the second performance parameter is output according to the second controller parameter and the second test conditions of the second test parameter.

[0011] Optionally, the aforementioned method of predicting performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: Based on the correlation of the performance parameters, the first amplitude and the first phase of the first performance parameter in the frequency domain, the second amplitude and the second phase of the second performance parameter in the frequency domain are output.

[0012] According to a second aspect of this application, a performance parameter acquisition device is provided, comprising: A determination module is used to determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different; The testing module is used to perform performance parameter testing based on the first test parameters to obtain the first performance parameters of the controlled object in the optical image stabilization system under the first test parameters. The acquisition module is used to acquire the performance parameter correlation relationship, which includes the correlation relationship between the first performance parameter under the first test parameter and the second performance parameter under the second test parameter. The prediction module is used to predict performance parameters based on the correlation between the performance parameters and the first performance parameter, and to determine the second performance parameter of the controlled object under the second test parameter.

[0013] According to a third aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described method for obtaining performance parameters.

[0014] According to a fourth aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the above-described performance parameter acquisition method.

[0015] According to a fifth aspect of this application, an electronic device is provided, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the above-described performance parameter acquisition method.

[0016] This application provides a method, apparatus, product, and storage medium for obtaining performance parameters. The method unifies controller parameters and test conditions into test parameters, then determines a first test parameter and a second test parameter from multiple test parameters, performs actual measurements on the first test parameter to obtain a first performance parameter, and then predicts performance parameters based on the correlation between performance parameters and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter. This achieves the correlation prediction of different test parameters and enables rapid and efficient prediction and setting of performance parameters for multiple sets of controller parameters while ensuring accuracy, significantly improving testing efficiency and reducing costs.

[0017] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0020] Figure 1 This is a flowchart illustrating a method for obtaining performance parameters provided in an embodiment of this application; Figure 2 This is another flowchart illustrating the performance parameter acquisition method provided in this application embodiment; Figure 3 This is a schematic diagram of the performance parameter acquisition device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0022] In the traditional debugging process of controller parameters in existing optical image stabilization systems, the testing process is repetitive and redundant because different combinations of controller parameters require independent frequency response analysis tests under open-loop or closed-loop test conditions. The test parameters for each set of controller parameters need to be constructed separately, including reconfiguring controller gain parameters, switching between open-loop and closed-loop test modes, and executing the complete test procedure. The test data acquisition and processing cycle increases linearly with the number of controllers, and the system identification efficiency is limited by the throughput capacity of the hardware test platform.

[0023] For example, in the testing phase of an optical image stabilization motor production line using a multi-modal PID controller, multiple independent frequency response analysis tests need to be performed under both open-loop and closed-loop test conditions for the three sets of controller parameters C1, C2, and C3. This results in low testing efficiency and high costs. If these problems are not addressed, the parameter tuning process for the optical image stabilization system will become a bottleneck on the production line, limiting the testing throughput of mass-produced equipment.

[0024] Faced with the aforementioned problems, the inventors of this application discovered that there are decomposable correlations between different test parameters: when the controller parameters are fixed, there is a deterministic transformation relationship between open-loop and closed-loop test conditions; when the test conditions are fixed, a transitive relationship can be established between different controller parameters through a system model. Based on this, this application proposes to construct a test parameter decoupling model, treating controller parameters and test conditions as independent variables, and establishing a prediction method / mechanism across test parameters.

[0025] To address this issue, this application proposes a method for obtaining performance parameters, such as... Figure 1 As shown, the method provided in this application includes: S101: Determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different.

[0026] In this application, the first test parameter refers to the test parameter that needs to be actually tested on the controlled object to obtain the performance parameter of the corresponding controller. The first test parameter can generally be a set of test parameters, while the second test parameter refers to the test parameter that predicts the performance parameter based on the actual test result. Since the performance prediction can be the prediction of the performance parameters of multiple other controllers in the optical image stabilization system, the number of second test parameters can be multiple sets.

[0027] In this application, the test parameters include controller parameters and test conditions, and the first test parameter and the second test parameter differ in at least one of the two dimensions of controller parameters and test conditions.

[0028] Among them, controller parameters refer to the adjustable parameters in the control algorithm that affect the dynamic characteristics of the optical image stabilization system, specifically including the proportional gain K. p Integral gain K i Differential gain K d The controller parameters include at least one of the integral time constant, derivative time constant, etc., and changing these parameters can adjust the system's response speed and stability. It should be noted that the controller parameters can also be other parameters, and there are no specific limitations. The test conditions include open-loop and closed-loop, which refer to two states of the optical image stabilization system's operating mode. Open-loop testing directly measures the response of the controlled object by disconnecting the feedback loop, while closed-loop testing keeps the feedback loop closed to simulate the actual control scenario.

[0029] S102: Perform performance parameter testing based on the first test parameters to obtain the first performance parameters of the controlled object in the optical image stabilization system under the first test parameters.

[0030] In this application, this step can employ conventional testing equipment, which includes an optical image stabilization system, comprising a controlled object and a controller. Performance parameter tests are performed on this testing equipment based on first test parameters to obtain the first performance parameters of the controlled object in the optical image stabilization system under the first test parameters.

[0031] In this application, the controlled object in the optical image stabilization system refers to the moving part that needs to be controlled in the optical image stabilization system, which may be a voice coil motor or a piezoelectric motor, etc., and its function is to compensate for image shift caused by external vibration through physical displacement.

[0032] Generally, the controlled object involves multiple sets of controller parameters, such as first controller parameters and second controller parameters. For example, the controlled object may involve multiple controllers, each corresponding to at least one set of controller parameters, such as first controller parameters for the first controller and second controller parameters for the second controller. For instance, the first controller parameters can be used to control the controlled object to move at a slower speed during the normal travel phase of an optical image stabilization system, while the second controller parameters are used to control the controlled object to move at a faster speed during startup or return to center. It should be noted that the first controller parameters and the second controller parameters can be parameters of the same controller; this embodiment is used as an example. Alternatively, the first controller parameters and the second controller parameters can be parameters corresponding to different controllers.

[0033] The first performance parameter refers to the system dynamic characteristic data of the optical image stabilization system obtained through actual measurement, specifically including amplitude-frequency characteristics, phase-frequency characteristics, resonant frequency parameters, etc. In this embodiment, frequency response data is used as an example for illustration. For example, under the first test parameter, the frequency response data corresponding to the frequency response analysis test of the controlled object is collected, and the frequency response data here is the actual measured value of the frequency response data.

[0034] Frequency response data can be obtained through frequency sweep testing, providing a benchmark reference for subsequent predictions.

[0035] S103: Obtain the correlation of performance parameters.

[0036] The performance parameter correlation includes the correlation between a first performance parameter under a first test parameter and a second performance parameter under a second test parameter. In this application, the performance parameter correlation refers to the mapping / transformation relationship between performance parameters of a controlled object in the same optical image stabilization system under different test parameters. This includes mapping functions, transformation functions, model correction functions, etc., which will be described in detail below.

[0037] S104: Based on the performance parameter correlation and the first performance parameter, perform performance parameter prediction to determine the second performance parameter of the controlled object under the second test parameter.

[0038] Based on the first performance parameters of the controlled object under the first test parameters in optical image stabilization, the second performance parameters of the controlled object under the second test parameters are output, such as the frequency response data of the controlled object under the second test parameters.

[0039] The second performance parameter refers to the predicted performance index obtained through calculation and derivation. Specifically, a mapping function or transformation function is used to mathematically transform the first performance parameter to realize the performance index calculation under different controller parameter combinations, thus avoiding repeated actual measurement operations.

[0040] The core innovation of this application lies in constructing a performance parameter prediction mechanism across test parameters. By establishing a mathematical correlation model between different controller parameters and test conditions, and combining measured data with theoretical derivation, it achieves the mapping of single test data to performance indicators of multiple controller parameter configurations. This method overcomes the limitations of traditional methods that require multiple tests. While maintaining prediction accuracy, it can greatly improve testing efficiency and supports bidirectional parameter derivation between open-loop and closed-loop modes, adapting to complex and ever-changing controller parameter configuration requirements.

[0041] In one embodiment, the solution of this application is implemented as follows: On the optical image stabilization motor production line, a set of typical PID controller parameters are selected as the controller parameters for the first test parameter. Under open-loop test conditions, a frequency response analysis test is performed on the controlled object to obtain the first performance parameter, such as frequency response data. Then, the second test parameter is set as the closed-loop test condition, while the controller parameters remain unchanged. Based on the mapping relationship between open-loop and closed-loop, the second performance parameter under the second test parameter is calculated using a mathematical model, such as a mapping function. Further, a third test parameter can be set, whose controller parameters are different from the first test parameter, but the open-loop test conditions are maintained. Based on the conversion relationship between different controller parameters, such as a conversion function, the performance parameter under the third test parameter is derived using the first performance parameter, such as frequency response data, under the open-loop test condition. Thus, performance parameter prediction results under multiple test parameter combinations can be obtained through only one actual test.

[0042] Through the above scheme, this application significantly reduces the number of frequency response analysis tests in the optical image stabilization system. Since it is not necessary to test each controller parameter and test condition combination independently, production efficiency is greatly improved. The test time and cost no longer increase linearly with the increase of the number of controllers, but remain relatively stable.

[0043] In some embodiments, the second test parameter is the same as the controller parameter in the first test parameter, but the test conditions are different. For example, the frequency response data of the controlled object under the first test parameter (such as the first controller parameter) and open-loop test conditions can be used to predict the frequency response data of the controlled object under the second test parameter (such as the second controller parameter) and closed-loop test conditions, or the frequency response data of the controlled object under the first test parameter (such as the first controller parameter) and closed-loop test conditions can be used to predict the frequency response data of the controlled object under the second test parameter (such as the second controller parameter) and open-loop test conditions, wherein the first controller parameter and the second controller parameter are the same.

[0044] Correspondingly, Figure 1 Step S104 includes: calling the mapping function in the performance parameter association relationship, which includes the association relationship between different performance parameters of the controlled object under the same controller parameters and different test conditions; based on the mapping function, outputting the second performance parameter according to the first test condition of the first test parameter, the second test condition of the second test parameter, and the first performance parameter.

[0045] In the conversion between open-loop and closed-loop test conditions, a mapping function can be used to convert frequency response data under the same controller parameters and different test conditions. For example, closed-loop frequency response data under the same controller parameters can be converted into equivalent open-loop frequency response data using a mapping function formula, forming a correlation between different test conditions. This correlation can be represented by the mapping function. Furthermore, the mapping function can include complex number operation rules for frequency domain response data, such as converting the complex form of the closed-loop frequency response into the complex form of the open-loop frequency response, or performing linear operations using amplitude (dB) values ​​and phase angles. The closed-loop frequency response data is also represented in this paper using frequency response data or closed-loop frequency response under closed-loop test conditions, and the open-loop frequency response data can also be represented in this paper using frequency response data or open-loop frequency response under open-loop test conditions.

[0046] Specifically, when the controller parameters are fixed, performance differences caused by changes in test conditions are directly corrected through mathematical relationships in the mapping function.

[0047] Mapping functions can be mathematically modeled to represent the transformation relationship between open-loop and closed-loop frequency responses. For example, open-loop frequency response data... With closed-loop frequency response data There exists a relationship as shown in formula (1) or formula (2). Wherein, Represents the frequency domain. It is the imaginary unit. It is the angular frequency. Formula (1) and formula (2) are essentially the same.

[0048] (1) (2) Based on this relationship, closed-loop frequency response data can be used... The equivalent open-loop frequency response data under the same controller parameters were calculated. .

[0049] As a preferred embodiment, the solution of this application is specifically implemented as follows: In an optical image stabilization system, the first step is to acquire the first performance parameters of the controlled object under the first test parameters. The first test parameters can be open-loop test conditions and first controller parameters, while the first performance parameters can be the first controller parameters and frequency response data under open-loop test conditions.

[0050] Next, a pre-established mapping function is invoked. This mapping function contains the correlation of the performance parameters of the controlled object under the same controller parameters but different test conditions. For example, the mapping function can describe the correspondence between the frequency responses under open-loop and closed-loop test conditions.

[0051] Then, based on the mapping function as shown in formula (1) above, also known as the first mapping function, the open-loop test conditions of the first test parameter, the closed-loop test conditions of the second test parameter, and the first performance parameter (open-loop frequency response data) are input, and the second performance parameter (closed-loop frequency response data) is calculated.

[0052] Based on this, without conducting actual tests under the second test parameters, the second performance parameters of the controlled object under closed-loop test conditions, such as frequency response data, can be obtained, realizing rapid conversion of performance parameters under the same controller parameters and different test conditions.

[0053] The first test parameter may include closed-loop test conditions and first controller parameters. The second test parameter may include open-loop test conditions and second controller parameters. When the second controller parameter is the same as the first controller parameter, a pre-established mapping function is called, such as formula (2) above, which can also be called the second mapping function. The closed-loop test conditions of the first test parameter, the open-loop test conditions of the second test parameter, and the first performance parameter (closed-loop frequency response data) are input to calculate the second performance parameter (open-loop frequency response data).

[0054] This embodiment avoids the need to repeatedly perform actual tests under different test conditions, significantly improving the efficiency of performance parameter acquisition. Simultaneously, the use of a pre-established mapping function ensures the accuracy of parameter transformation, overcoming potential errors in existing solutions. Furthermore, this method can be flexibly applied to the transformation of various test conditions, enhancing the comprehensiveness and convenience of system performance evaluation.

[0055] In some embodiments, the second test parameter is different from the controller parameter of the first test parameter, but the test conditions are the same. For example, the frequency response data of the controlled object under the first controller parameter and open-loop test conditions can be used to predict the frequency response data of the controlled object under the second controller parameter and open-loop test conditions, or the frequency response data of the controlled object under the first controller parameter and closed-loop test conditions can be used to predict the frequency response data of the controlled object under the second controller parameter and closed-loop test conditions, etc., wherein the first controller parameter and the second controller parameter are different, and the first test condition in the first test parameter and the second test condition in the second test parameter are the same.

[0056] If both the first and second test conditions are open-loop test conditions, the frequency response data of the controlled object under the first controller parameters and open-loop test conditions are used to predict the frequency response data of the controlled object under the second controller parameters and open-loop test conditions. Correspondingly, Figure 1 Step S104 includes: calling a conversion function in the performance parameter correlation relationship, which includes the correlation between the actual response parameters of the controlled object and the performance parameters under known test parameters, such as the correlation between the actual frequency response data of the controlled object under known controller parameters and open-loop test conditions and the actual measured value obtained by frequency response data analysis test; based on the conversion function, determining the actual response parameters of the controlled object according to the first controller parameters and the first test conditions and the first performance parameters of the first test parameters; and outputting the second performance parameters according to the actual response parameters of the controlled object, the second controller parameters and the second test conditions of the second test parameters. The establishment of the transfer function can be based on a transfer function model or a data-driven model, such as obtaining the true frequency response characteristics (also known as true frequency response characteristics) of the controlled object through system identification methods. The determination of the true frequency response parameters involves decoupling the controller parameters from the system response. The first controller parameters and the first test conditions constitute a known transfer path, and the independent dynamic characteristics of the controlled object are separated from the first performance parameters through complex division. When outputting the second performance parameters, the new controller parameters, such as the second controller parameters, and the true response parameters reconstruct the complete transfer path through complex multiplication. Maintaining the test conditions ensures the consistency of the system's dynamic characteristics.

[0057] Known First controller model in the complex frequency domain Theoretical model of the controlled object The controller model includes first controller parameters, or it can be understood that the first controller model is represented by the first controller parameters, which are the theoretical values ​​of the open-loop transfer function of the optical image stabilization system. It can be obtained from the first controller model and the theoretical model of the controlled object, as shown in formula (3).

[0058] (3) Based on this relationship, the open-loop true frequency response of the controlled object The open-loop frequency response data under the first controller parameters can be used, that is, based on the actual measured value of the open-loop frequency response. With the first controller parameters The result is shown in formula (4).

[0059] (4) Formula (4) is the conversion function described in the embodiments of this application. Using formula (4), the true response parameters of the controlled object under open-loop test conditions can be obtained, that is, the true open-loop frequency response.

[0060] After obtaining the actual response parameters of the controlled object under open-loop test conditions, the second performance parameter is output based on the actual response parameters of the controlled object, the second controller parameters of the second test parameters, and the second test conditions. That is, based on the actual response parameters of the controlled object under open-loop test conditions and the second controller parameters, the frequency response data of the controlled object under the second controller parameters and open-loop test conditions is output.

[0061] Assuming the second controller parameters are used If we represent it this way, then the frequency response data of the controlled object under the second controller parameters and open-loop test conditions can be used to represent the true open-loop frequency response of the controlled object. With the second controller parameters (frequency domain) Multiply to obtain. See formula (5) for details.

[0062] (5) This enables the prediction of the frequency response data of the controlled object under the second controller parameters and open-loop test conditions using the frequency response data of the controlled object under the first controller parameters and open-loop test conditions.

[0063] If both the first and second test conditions are closed-loop test conditions, the frequency response data of the controlled object under the first controller parameters and closed-loop test conditions is used to predict the frequency response data of the controlled object under the second controller parameters and closed-loop test conditions. Correspondingly, Figure 1Step S104 includes: calling the mapping function and conversion function in the performance parameter association relationship; based on the mapping function, converting the first controller parameter of the first test parameter and the first performance parameter under the first test condition (closed-loop test condition) into the first controller parameter and the first performance parameter under the open-loop test condition; based on the conversion function, determining the true response parameter of the controlled object according to the first controller parameter, the open-loop test condition, the first performance parameter, and the first controller parameter; outputting the second controller parameter and the second performance parameter under the open-loop test condition according to the true response parameter of the controlled object and the second controller parameter of the second test parameter; and based on the mapping function, converting the second controller parameter and the second performance parameter under the open-loop test condition into the second controller parameter and the second performance parameter under the second test condition.

[0064] When the actual measurement / collection is of controller parameters and frequency response data under closed-loop test conditions, it is necessary to derive the frequency response data under closed-loop test conditions into equivalent frequency response data under open-loop test conditions based on the mapping function. For example, using formula (2), i.e. the second mapping function, the frequency response data under closed-loop test conditions can be derived into equivalent frequency response data under open-loop test conditions. After obtaining the frequency response data under open-loop test conditions, the true response parameters of the controlled object under open-loop test conditions are determined based on the transformation function. Then, based on the true response parameters of the controlled object under open-loop test conditions and the second controller parameters, the open-loop frequency response data under the second controller parameters is obtained. Then, according to the mapping function, such as the first mapping function, i.e. formula (1), the frequency response data under the second controller parameters and open-loop test conditions are converted into frequency response data under the second controller parameters and closed-loop test conditions.

[0065] This method enables the prediction of the frequency response data of the controlled object under the second controller parameters and closed-loop test conditions using the frequency response data of the controlled object under the first controller parameters and closed-loop test conditions.

[0066] Understandably, when the actual measured frequency response data is under closed-loop test conditions, it needs to be converted into frequency response data under open-loop test conditions using a mapping function such as the second mapping function. Similarly, when the frequency response data under open-loop test conditions is predicted, if the desired frequency response data is to be obtained under closed-loop test conditions, it is also necessary to convert the open-loop test response data into closed-loop test response data using a mapping function such as the first mapping function.

[0067] Thus, the system performance under different controller parameters can be predicted through a single frequency response analysis test and simple mathematical calculations, such as predicting frequency response data under different controller parameters, without the need for repeated testing.

[0068] Through the above technical solution, this application achieves accurate prediction of system performance under different controller parameters, avoiding the need for repeated full frequency response analysis tests. This step-by-step processing method ensures the consistency of test conditions and quantifies the impact of controller parameters through mathematical calculations, thereby significantly reducing test time and resource consumption, while ensuring the consistency between the prediction results and the actual system characteristics. This method is particularly effective for systems that require frequent adjustment of controller parameters, and can greatly improve the efficiency of system optimization and debugging.

[0069] In some embodiments, the second test parameter differs from the controller parameter of the first test parameter, and the test conditions are also different. For example, frequency response data under the second controller parameter and closed-loop test conditions can be predicted using the first controller parameter and frequency response data under the first controller parameter and closed-loop test conditions, or vice versa. Wherein, the first controller parameter and the second controller parameter are different, and the first test condition in the first test parameter and the second test condition in the second test parameter are also different.

[0070] When using the frequency response data under the first controller parameters and the first test conditions (open-loop test conditions) to predict the frequency response data under the second controller parameters and the second test conditions (closed-loop test conditions), correspondingly, Figure 1 Step S104 includes: calling the mapping function and the conversion function in the performance parameter association relationship. The mapping function includes the association relationship between different performance parameters of the controlled object under the same controller parameters and different test conditions. The conversion function includes the association relationship between the actual response parameters and performance parameters of the controlled object under known test parameters. Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and the first test conditions of the first test parameters and the first performance parameters. The first intermediate performance parameter is output according to the actual response parameters of the controlled object, the second controller parameters and the first test conditions of the second test parameters, and the first test conditions. The second performance parameter is output according to the mapping function, the second test conditions of the second test parameters, the first test conditions, and the first intermediate performance parameter.

[0071] Among them, based on the transformation function, the true response parameters of the controlled object are determined according to the first controller parameters, the first test conditions, and the first performance parameters of the first test parameters. This includes determining the true response parameters of the controlled object according to the first controller parameters, the first performance parameters under the first test conditions, and the first controller parameters, as shown in the above formula (4). The true response parameters of the controlled object under the first controller parameters and the first test conditions (open-loop test conditions) are obtained according to formula (4), which is the open-loop true frequency response.

[0072] Based on the actual response parameters of the controlled object, the second controller parameters of the second test parameters, and the first test conditions, the first intermediate performance parameter is output. Specifically, according to formula (5), the actual response parameters of the controlled object, the second controller parameters, and the first test conditions (open-loop test conditions) are input to obtain the frequency response data of the controlled object under the second controller parameters and the first test conditions (open-loop test conditions), which is the first intermediate performance parameter.

[0073] Based on the mapping function, the second performance parameter is output according to the second test condition, the first test condition, and the first intermediate performance parameter of the second test parameter. The second test condition is a closed-loop test condition, and the second performance parameter is output based on the first intermediate performance parameter of the controlled object under the second controller parameters and the first test condition, as well as the second test condition (closed-loop test condition). For example, the frequency response data of the controlled object under the second controller parameters and the closed-loop test condition can be obtained using formula (1), i.e., the first mapping function.

[0074] This enables the prediction of frequency response data under second controller parameters and second test conditions (closed-loop test conditions) using frequency response data under first controller parameters and first test conditions (open-loop test conditions).

[0075] When using the first controller parameters and the first performance parameters (frequency response data) under the first test conditions (closed-loop test conditions) to predict the second controller parameters and the second performance parameters (frequency response data) under the second test conditions (open-loop test conditions), correspondingly, Figure 1 Step S104 includes: calling the mapping function and conversion function in the performance parameter association relationship; based on the mapping function, determining the second intermediate performance parameter under the first controller parameter and the second test condition according to the first test parameter, the first test condition, the first performance parameter, and the second test condition; based on the conversion function, determining the actual response parameter of the controlled object according to the first controller parameter, the second test condition, and the second intermediate performance parameter; and outputting the second performance parameter according to the actual response parameter of the controlled object, the second controller parameter of the second test parameter, and the second test condition.

[0076] Among them, based on the mapping function, such as formula (2), which is the second mapping function, the first performance parameter (frequency response data) under the first controller parameter and the first test condition (closed-loop test condition) is converted into the second intermediate performance parameter (frequency response data) under the first controller parameter and the second test condition (open-loop test condition). Then, based on the conversion function, such as formula (4), according to the second intermediate performance data under the first controller parameter and the second test condition (open-loop test condition), and the controller theoretical performance parameter (controller theoretical frequency response), the actual response parameter of the controlled object under the first controller parameter and the second test condition (open-loop test condition) is obtained; then, by using formula (5), inputting the actual response parameter of the controlled object, the second controller parameter and the second test condition (open-loop test condition), the second performance parameter under the second controller parameter and the second test condition (open-loop test condition) can be obtained.

[0077] Understandably, when the actual measured frequency response data is under closed-loop test conditions, it needs to be converted into frequency response data under open-loop test conditions using a mapping function such as the second mapping function. Similarly, when the frequency response data under open-loop test conditions is predicted, if the desired frequency response data is to be obtained under closed-loop test conditions, it is also necessary to convert the open-loop test response data into closed-loop test response data using a mapping function such as the first mapping function.

[0078] Through the above technical solution, this application achieves accurate prediction of the performance parameters of the controlled object when both controller parameters and test conditions change simultaneously. Furthermore, by introducing intermediate performance parameters, the problems of fragmented steps and inconsistent parameter transmission in the prediction process are avoided, thus improving the accuracy of the prediction results. Specifically, this method can quickly obtain the performance parameters of the controlled object under new controller parameters and test conditions without conducting actual testing, thereby significantly reducing testing time and costs and improving the efficiency of system debugging and optimization.

[0079] In some embodiments, Figure 1 Step S104 includes: calling the model correction function in the performance parameter correlation relationship, which includes the correlation between the theoretical model and the real model of the controlled object. Specifically, the model correction function includes the correlation between the theoretical model, the real model, and the modeling error function of the controlled object; outputting the modeling error function of the controlled object based on the theoretical model of the controlled object, the first controller parameter and the first test condition of the first test parameter, and the first performance parameter; outputting the real model of the controlled object based on the model correction function, the modeling error function, and the theoretical model of the controlled object; and outputting the second performance parameter based on the real model of the controlled object, the second controller parameter and the second test condition of the second test parameter.

[0080] The model correction function is configured in transfer function or state-space form to establish the dynamic relationship between the theoretical model and the real model. The modeling error function is calculated using frequency response data obtained from actual measurements in the frequency domain or theoretical frequency response data in the time domain; specifically, it represents the difference between the theoretical model output and the measured performance parameters. The real model is generated through algebraic operations or optimization algorithms, superimposing the modeling error function onto the theoretical model or correcting its parameters. The prediction of the second performance parameter is based on simulation calculations of the real model in the frequency or time domain, combined with dynamic response analysis using the second controller parameters and test conditions.

[0081] Specifically, under the first test parameters, the modeling error function is calculated by comparing the predicted performance parameters output by the theoretical model with the actually measured first performance parameters. This error function reflects the degree of deviation between the theoretical model and the actual system in terms of dynamic response. Furthermore, the error function is fused with the theoretical model using a model correction function to generate a real model. The real model, through the corrected transfer function or state equation, more accurately characterizes the dynamic characteristics of the controlled object. When it is necessary to predict the performance parameters under the second test parameters, the controller parameters are replaced and the test conditions are adjusted based on the real model. The second performance parameters are obtained through frequency domain simulation or time domain integration. For example, in the frequency domain, the transfer function of the real model is substituted into the controller parameters, and the open-loop or closed-loop response curves are calculated through the amplitude-frequency and phase-frequency characteristics; in the time domain, the response waveform of the system under step input or sinusoidal excitation is simulated using a state equation solver. Thus, by dynamically correcting the model error, the prediction deviation problem caused by the inaccuracy of the theoretical model in traditional methods is solved, making the performance prediction results under different combinations of controller parameters closer to the actual test values, while reducing the time and cost of repeated testing.

[0082] As a preferred embodiment, the solution of this application is specifically implemented as follows: First, the model correction function is invoked. This function includes the relationship between the theoretical model, the real model, and the modeling error function of the controlled object. For example, the model correction function can be expressed by the following relationship, as shown in formula (6).

[0083] (6) in For the real model of the controlled object, For theoretical models, To model the error function.

[0084] Next, based on the theoretical model of the controlled object, the first controller parameters and first test conditions of the first test environment, and the first performance parameters, the modeling error function of the controlled object is output. Specifically, this can be achieved through the following steps: using the theoretical model... and first controller parameters Calculate the open-loop transfer function response in the frequency domain, i.e., the theoretical frequency response data under open-loop test conditions. Specifically, the model error function can be determined based on the actual measured frequency response data and the theoretical frequency response data.

[0085] Specifically, the model error function can be expressed as shown in formula (7).

[0086] (7) Then, based on the model correction function, and according to the modeling error function and theoretical model of the controlled object, the true model of the controlled object is output. This can be achieved by adjusting the modeling error function... With theoretical models Adding or multiplying yields the true model. This refers to the open-loop true response parameters (open-loop true frequency response) of the controlled object. Specifically, the true model... It can be obtained through formula (6).

[0087] Finally, based on the real model of the controlled object, and according to the second controller parameters and second test conditions of the second test environment, the second performance parameters are output. Specific steps include: utilizing the real model... Second controller parameters (Frequency domain) Calculate the new frequency response data; based on the second test conditions (e.g., open-loop or closed-loop) and the new frequency response data, the second performance parameter can be obtained. For example, using a real model. With the second controller parameters (frequency domain) Multiplying these parameters yields the second performance parameter under open-loop test conditions, namely the open-loop frequency response, which can be specifically shown in formula (8).

[0088] (8) This embodiment specifies how to obtain the real model of the controlled object, making the model parameters used more accurate, thereby improving the accuracy and efficiency of determining the second performance parameter.

[0089] It is understandable that when using the real model in this embodiment to predict the second performance parameter under the second test parameter (second controller parameter and second test condition) based on the first performance parameter under the first test parameter (first controller parameter and first test condition), the only difference is the way of determining the real model of the controlled object. The rest is the same as the content involved in any of the embodiments above.

[0090] For example, when the second test parameter differs from the controller parameter in the first test parameter but the test conditions are the same, if both the first and second test conditions are open-loop test conditions, the frequency response data of the controlled object under the first controller parameter and the first test condition (open-loop test condition) is used to predict the frequency response data of the controlled object under the second controller parameter and the second test condition (open-loop test condition). Correspondingly, Figure 1 Step S104 includes: calling the model correction function in the performance parameter association relationship; outputting the modeling error function of the controlled object based on the theoretical model of the controlled object, the first controller parameter of the first test parameter, the first test condition (open-loop test condition), and the first performance parameter; outputting the true model of the controlled object based on the model correction function, the modeling error function of the controlled object, and the theoretical model; and outputting the second performance parameter under the second test parameter based on the true model of the controlled object, the second controller parameter of the second test parameter, and the second test condition (open-loop test condition).

[0091] For example, when the second test parameter is different from the controller parameter in the first test parameter but the test conditions are the same, if both the first and second test conditions are closed-loop test conditions, the frequency response data of the controlled object under the first controller parameter and the first test condition (closed-loop test condition) is used to predict the frequency response data of the controlled object under the second controller parameter and the second test condition (closed-loop test condition). Correspondingly, Figure 1 Step S104 includes: calling the mapping function and model correction function in the performance parameter association relationship; based on the mapping function, converting the first controller parameters and the first performance parameters (frequency response data) under the first test conditions (closed-loop test conditions) into the first controller parameters and the first performance parameters under the open-loop test conditions; based on the theoretical model of the controlled object, the first controller parameters of the first test parameters, and the first performance parameters under the open-loop test conditions, outputting the modeling error function of the controlled object; based on the model correction function, according to the modeling error function and the theoretical model of the controlled object, outputting the true model of the controlled object under the first controller parameters and the open-loop test conditions; based on the true model of the controlled object, according to the second controller parameters of the second test parameters, outputting the second control parameters and the second performance parameters under the open-loop test conditions; and based on the mapping function, converting the second control parameters and the second performance parameters under the open-loop test conditions into the second control parameters and the second performance parameters under the closed-loop test conditions.

[0092] For example, when the second test parameter differs from the controller parameter in the first test parameter and the test conditions are different, if the first test condition is an open-loop test condition and the second test condition is a closed-loop test condition, then correspondingly, Figure 1Step S104 includes: calling the mapping function and model correction function in the performance parameter association relationship; outputting the modeling error function of the controlled object based on the theoretical model of the controlled object, the first controller parameter of the first test parameter, the first test condition (open-loop test condition), and the first performance parameter; outputting the true model of the controlled object based on the model correction function, the modeling error function of the controlled object, and the theoretical model; outputting the second controller parameter and the second performance parameter under the open-loop test condition based on the true model of the controlled object and the second controller parameter of the second test parameter; and converting the second controller parameter and the second performance parameter under the open-loop test condition into the second performance parameter under the second controller parameter and the second test condition (closed-loop test condition) based on the mapping function.

[0093] For example, when the controller parameters in the second test parameter are different from those in the first test parameter and the test conditions are different, if the first test condition is a closed-loop test condition and the second test condition is an open-loop test condition, then, Figure 1 Step S104 includes: calling the mapping function and model correction function in the performance parameter association relationship; based on the mapping function, converting the first performance parameter under the first test parameters (first controller parameters and closed-loop test conditions) into the first controller parameters and the first performance parameter under the open-loop test conditions; based on the theoretical model of the controlled object, the first controller parameters of the first test parameters, the first performance parameter under the open-loop test conditions, and the first controller parameters, outputting the modeling error function of the controlled object; based on the model correction function, outputting the true model of the controlled object according to the modeling error function and the theoretical model of the controlled object; based on the true model of the controlled object, outputting the second controller parameters and the second performance parameter under the second test conditions according to the second controller parameters of the second test parameters.

[0094] Through the above technical solution, this application can effectively solve the inherent deviation problem between the theoretical model and the actual system. By introducing a model correction function, the error between the theoretical model and the actual model is quantified and corrected, thereby improving prediction accuracy. This method solves the prediction deviation problem caused by the inaccuracy of the theoretical model in traditional methods by dynamically correcting model errors, making the performance prediction results under different controller parameter combinations closer to the actual test values.

[0095] In some embodiments, Figure 1 Step S104 in the method shown includes: based on the correlation of performance parameters, the first amplitude and the first phase of the first performance parameter in the frequency domain, outputting the second amplitude and the second phase of the second performance parameter in the frequency domain.

[0096] The actual measured / acquired open-loop frequency response data can be recorded as amplitude (in dB) and phase (or phase angle), or it can be understood as representing the open-loop frequency response data through amplitude and phase.

[0097] Specifically, let's take predicting the open-loop frequency response data under the parameters of the second controller using the first controller model as an example. First, obtain the open-loop frequency response data under the parameters of the first controller, that is, the actual measured value of the open-loop frequency response. C1 represents the first controller parameter. Then, for the second controller parameter C2, its theoretical frequency response data is calculated. The open-loop frequency response data, etc., are recorded as amplitude values. and phase Therefore, the amplitude and phase under the second test parameters are calculated based on the amplitude and phase under the open-loop test conditions. The unit of amplitude is dB, and the unit of phase is angle.

[0098] Specifically, the amplitude and / or phase corresponding to the frequency response data under the second controller parameters and the open-loop test conditions can be determined based on the amplitude and phase corresponding to the actual measured value of the open-loop frequency response under the first controller parameters, the amplitude and phase corresponding to the theoretical frequency response data under the first controller parameters, and the amplitude and / or phase corresponding to the theoretical frequency response data of the second controller parameters.

[0099] The amplitude and phase corresponding to the second controller parameters and the frequency response data under open-loop test conditions can be calculated by the following formulas (9) and (10), respectively.

[0100] (9) (10) in, To predict the amplitude corresponding to the open-loop frequency response data under the second control parameter, The amplitude corresponding to the theoretical frequency response data under the second controller parameters. The amplitude corresponding to the theoretical frequency response data under the first controller parameters. The amplitude corresponding to the actual measured value of the open-loop frequency response under the first controller parameters.

[0101] in, To predict the phase corresponding to the open-loop frequency response data under the second control parameter, The phase corresponding to the theoretical frequency response data under the second controller parameters. The phase corresponding to the theoretical frequency response data under the first controller parameters. The phase corresponding to the actual measured value of the open-loop frequency response under the first controller parameters.

[0102] The amplitude and / or phase of the open-loop response of the second controller at a given frequency can be calculated, thus allowing the acquisition of amplitude and phase data at different frequency points.

[0103] Through the above technical solution, this application achieves the decomposition calculation of frequency domain response, avoiding complex number operations and simplifying the calculation process. Furthermore, this method improves prediction efficiency, enabling independent analysis and adjustment of amplitude-frequency and phase-frequency characteristics. By separating the calculation of amplitude and phase, computational complexity is reduced and calculation speed is improved. Simultaneously, independent amplitude and phase data facilitate intuitive analysis of system characteristics, enabling engineers to quickly adjust control parameters. In addition, this method predicts system response under multiple controller parameters using a single measurement, reducing the need for repetitive testing, improving production efficiency, and lowering testing costs.

[0104] The following section will describe the method provided in the embodiments of this application, taking the acquisition of two performance parameters by an optical image stabilization system as an example.

[0105] Optical image stabilization (OIS) technology has become a key component for improving image quality in consumer electronics, especially in smartphone cameras. On automated production lines, OIS systems typically consist of a controller and a plant. For each plant, a frequency response analysis (FRA) of the open-loop / closed-loop model is often required to match the controller parameters. This process is usually time-consuming and labor-intensive, impacting production efficiency. Although the controlled plant can be approximated by a theoretical model, differences still exist between the model and actual measurements, resulting in a significant gap between the theoretical and actual performance of the open-loop / closed-loop frequency response.

[0106] This invention is used to quickly predict the system response under other controller parameter combinations based on the test results of a single frequency response analysis (of one controller parameter), thereby reducing debugging costs while ensuring a certain level of reliability.

[0107] like Figure 2 As shown, the method provided in this application includes the following steps: S201: Obtain the known parameters of the test parameters.

[0108] In this step, we only need to know the parameters of the first controller as the controller model. It can also be known at the same time that the parameters of the first controller are the controller model. And the theoretical model of the controlled object (such as a motor). .

[0109] S202: The first performance parameter was obtained from the test.

[0110] In this step, the test conditions can be either open-loop or closed-loop.

[0111] For example, this step can collect a set of open-loop frequency response data as the first performance parameter: perform an open-loop FRA (Frequency Response Analysis) test, and record the measured open-loop frequency response (frequency response data under open-loop test conditions) as follows. .

[0112] For example, this step can collect a set of closed-loop frequency response data as the first performance parameter: perform a closed-loop FRA (Frequency Response Analysis) test, and record the closed-loop frequency response (frequency response data under closed-loop test conditions) as... .

[0113] S203: Predict the second performance parameter.

[0114] In one embodiment of this step, based on step S202, when the controller parameters of the second test parameter and the first test parameter are the same but the test conditions are different, a mapping function can be called. The mapping function includes the correlation between different performance parameters of the controlled object under the same controller parameter and different test conditions. Based on the mapping function, the second performance parameter is output according to the first test condition of the first test parameter, the second test condition of the second test parameter, and the first performance parameter.

[0115] Specifically, if the open-loop frequency response of the controlled object under the first controller parameters is obtained by testing... The closed-loop frequency response under the first controller parameters can be obtained based on the mapping function (formula (1) above). : .

[0116] That is, based on this mapping function (i.e., the above formula (1)), according to the first test parameter (the controller parameter is...) The first test condition (open loop), the second test parameter (controller parameter is) The second test condition (closed loop) and the first performance parameter (i.e., open-loop frequency response) Output the second performance parameter (i.e., closed-loop frequency response) ).

[0117] Similarly, if the closed-loop frequency response of the controlled object under the first controller parameters is obtained by testing... The open-loop frequency response under the first controller parameters can be obtained based on the mapping function (formula (2) above). : .

[0118] That is, based on this mapping function (i.e., the above formula (2)), according to the first test parameters (where the controller parameters are...) The first test condition (closed loop), the second test parameters (where the controller parameters are...) The second test condition (open loop) and the first performance parameter (i.e., closed loop frequency response) Output the second performance parameter (i.e., open-loop frequency response) ).

[0119] In another embodiment of this step, based on step S202, it is possible to implement a conversion function when the controller parameters of the second test parameter and the first test parameter are different but the test conditions are the same, such as when the test condition is open-loop. The conversion function includes the correlation between the actual response parameters and performance parameters of the controlled object under known test parameters. Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and the first test conditions of the first test parameter and the first performance parameter. The second performance parameter is output according to the actual response parameters of the controlled object, the second controller parameters and the second test conditions of the second test parameter.

[0120] Specifically, the first controller parameter is the controller model. Under the first test condition of open loop, the open-loop frequency response of the controlled object under the first controller parameters. (i.e., the first performance parameter) can be used to determine the true response parameter of the controlled object according to the following transformation function (i.e., the above formula (4)). : .

[0121] That is, the actual measured value of the open-loop frequency response of the controlled object under the first controller parameters. and the theoretical frequency response parameters of the first controller The true frequency response parameters of the controlled object are derived.

[0122] Specifically, the open-loop frequency response data obtained from actual measurement / acquisition can be recorded as amplitude and phase respectively. Correspondingly, this application can output the second amplitude and second phase of the second performance parameter in the frequency domain based on the first amplitude and first phase of the first performance parameter in the frequency domain; that is, if the acquired open-loop FRA is recorded as amplitude (dB) respectively. and phase Alternatively, the calculation of the true frequency response parameters of the controlled object can be written as shown in formulas (11) and (12).

[0123] (11) (12) Formulas (11) and (12) can be based on the actual response parameters of the controlled object under open-loop test conditions. That is, we get from formula (4).

[0124] Furthermore, the actual frequency response parameters of the controlled object have been obtained through the above process, so the open-loop (second test condition is open-loop) frequency response of the controlled object under the new controller parameters (i.e., the second controller parameters) can be predicted, so as to obtain the second performance index under the new controller parameters and the second test condition.

[0125] That is, based on the transformation function (such as formula (5) above): As shown in formula (13) here, formula (5) is essentially the same as formula (13), based on the actual response parameters of the controlled object. and the second controller parameters of the second test parameters. And the second test condition (open loop), output the second performance parameter (i.e., the open loop response of the controlled object under the second controller parameters). ).

[0126] (13) Amplitude (dB) and phase can also be calculated separately: ; As shown in formulas (9) and (10) respectively. Formulas (9) and (10) can be obtained based on formula (13).

[0127] In another embodiment of this step, based on step S202, when the controller parameters of the second test parameter and the first test parameter are different but the test conditions are the same (e.g., the test condition is closed-loop), the mapping function and conversion function in the performance parameter association relationship can be called; based on the mapping function, the first performance parameter under the first test condition is converted into the first performance parameter under the open-loop test condition; based on the conversion function, the true response parameter of the controlled object is determined according to the first controller parameter of the first test parameter and the first performance parameter under the open-loop test condition; based on the true response parameter of the controlled object and the second controller parameter of the second test parameter, the second performance parameter under the open-loop test condition is output; based on the mapping function, the second performance parameter under the open-loop test condition is converted into the second performance parameter under the second test condition.

[0128] Specifically, the first controller parameter is the controller model. Under the first test condition of closed loop, the closed-loop frequency response of the controlled object under the first controller parameters. (i.e., the first performance parameter) can be used to determine the open-loop frequency response of the controlled object under the first controller parameter based on the mapping function (i.e., the second mapping function, as shown in formula (2) above). : Then, following the steps above, determine the actual response parameters and the open-loop frequency response under the second controller parameters. Please refer to the above description, which will not be repeated here. After obtaining the open-loop frequency response under the second controller parameters, the closed-loop frequency response under the second controller parameters can be obtained according to the mapping function (i.e., the first mapping function, as shown in the above formula (1)).

[0129] In another embodiment of this step, based on step S202, when the controller parameters of the second test parameter and the first test parameter are different and the test conditions are different (e.g., the first test condition is open-loop and the second test condition is closed-loop), a mapping function and a conversion function can be invoked; based on the conversion function, the true response parameters of the controlled object are determined according to the first controller parameters and the first test conditions of the first test parameter and the first performance parameter; based on the conversion function, intermediate performance parameters are output according to the true response parameters of the controlled object, the second controller parameters of the second test parameter, and the first test conditions; based on the mapping function, the second performance parameter is output according to the second test conditions of the second test parameter, the first test conditions, and the intermediate performance parameters.

[0130] Specifically, the first controller parameter is the controller model. Under the first test condition of open loop, the open-loop frequency response of the controlled object under the first controller parameters. (i.e., the first performance parameter) can be used to determine the true response parameter of the controlled object based on the transformation function (formula (4) above). .

[0131] Then, based on the transformation function (formula (5) or formula (13)), according to the actual response parameters of the controlled object... and the second controller parameters of the second test parameters. And the first test condition (open loop), output intermediate performance parameters (i.e., the open loop response of the controlled object under the second controller parameters). ).

[0132] Finally, based on the mapping function (as in formula (14)), the second performance parameter (i.e., the closed-loop frequency response of the controlled object under the second controller parameters) is obtained. ).

[0133] (14) The amplitude (dB) and phase can also be calculated separately, as shown in formulas (15) and (16) below.

[0134] (15) (16) Formulas (15) and (16) are derived from formula (14).

[0135] In another embodiment of this step, based on step S202, if the theoretical model of the controlled object is known, the following operations can be performed: A model correction function is invoked, which includes the theoretical model of the controlled object, the true model, and the correlation after the modeling error function; based on the theoretical model of the controlled object, the first controller parameter and the first test condition of the first test parameter, and the first performance parameter, the modeling error function of the controlled object is output; based on the model correction function, according to the modeling error function and the theoretical model of the controlled object, the true model of the controlled object is output; based on the true model of the controlled object, according to the second controller parameter and the second test condition of the second test parameter, the second performance parameter is output.

[0136] Specifically, the parameters of the first controller are known. Theoretical model of the controlled object Under these conditions, the theoretical value of the open-loop transfer function of the OIS system can be determined. for: .

[0137] According to the first performance parameter The modeling error function of the controlled object can be determined. (See formula (7) for details): .

[0138] If the amplitude (dB) and phase of the open-loop frequency response are obtained separately, they are recorded as follows: and In this application, the modeling error function can be decomposed into the modeling error amplitude and the modeling error phase, and the calculation methods are shown in the following formulas (17) and (18).

[0139] (17) (18) Formulas (17) and (18) can be obtained from formula (7).

[0140] Furthermore, it can be based on the model correction function and the modeling error function. and theoretical models To obtain the corrected real model (See formula (6) for reference): .

[0141] Thus, the frequency response amplitude and phase of the corrected real model can also be obtained, as shown in Equations (19) and (20) below. Equations (19) and (20) can be obtained based on Equation (6).

[0142] (19) (20) When it is necessary to obtain the model parameters of the actual controlled object, one can... By using fitting methods (such as TrustRegion Reflection and Levenberg-Marquardt method), the corrected true model parameters are obtained.

[0143] Obtaining the real model of the controlled object Then, the controlled object is predicted in the second controller parameters. Open-loop response (That is, one of the second performance parameters), see formula (8) for details: .

[0144] The amplitude (dB) and phase can also be calculated separately, as shown in formula (21) and formula (22) respectively.

[0145] (twenty one) (twenty two) Based on this, the controlled object is predicted in the second controller parameters. Closed-loop response (i.e., one of the second performance parameters), as shown in formula (23).

[0146] (twenty three) The amplitude (dB) and phase can also be calculated separately, as shown in formula (24) and formula (25) respectively, where formula (24) and formula (25) can be obtained based on formula (23).

[0147] (twenty four) (25) Here are two application examples to illustrate: Application Example 1: Known It is a standard PID controller, the controlled object is unknown, and a new PID controller is predicted based on open-loop and closed-loop FRA data. The open and closed loop frequency response.

[0148] PID controller C1, i.e., the first controller parameter and theoretical frequency response As shown in formulas (26) and (27) below.

[0149] (26) (27) Based on the measured open-loop frequency response The true frequency response of the controlled object is obtained through inversion calculation. As shown in formula (28).

[0150] (28) Thus, the predicted open-loop frequency response under the new controller C2 (i.e., the parameters of the second controller) is obtained. As shown in formula (29).

[0151] (29) and the predicted closed-loop frequency response As shown in formula (30).

[0152] (30) Performance metrics can be predicted based on the predicted open-loop and closed-loop frequency responses. Open-loop performance metrics may include gain crossover frequency, phase margin, and gain margin. Closed-loop performance metrics may include -3dB bandwidth and maximum peak value. This application example 1 does not require theoretical model parameters of the specific controlled object; instead, it directly inversely calculates the actual response parameters from the measured values ​​of the FRA and the theoretical values ​​of the controller C1. .

[0153] Application Example 2: Known These are the controller parameters of a standard PID controller, and the controlled object in a second-order system. Predicting a new PID controller based on a modeling error model The open and closed loop frequency response.

[0154] The theoretical model and theoretical frequency response of the controlled object are shown in Equation (31) and Equation (32), respectively.

[0155] (31) (32) The PID controller C1 and its theoretical frequency response are shown in formulas (33) and (34), respectively.

[0156] (33) (34) Based on the measured open-loop frequency response The corrected value of the controlled object model is calculated. As shown in formula (35).

[0157] (35) Thus, the predicted open-loop frequency response under the new controller is obtained. As shown in formula (36).

[0158] (37) and the predicted closed-loop frequency response As shown in formula (37).

[0159] (37) In summary, this application directly predicts the open-loop and closed-loop frequency response of a new controller by measuring the open-loop or closed-loop FRA under a set of controller parameters, which is reliable and efficient. Furthermore, if the theoretical controlled object model is known, it can be modified to obtain the real model. In this way, by predicting the frequency response performance of multiple controllers, there is no need to repeatedly measure the FRA, the process can be quickly deployed, and the performance evaluation of multiple controllers can be completed based on a single FRA, which improves production line efficiency and reduces labor and equipment costs.

[0160] According to a second aspect of this application, embodiments of this application also provide a performance parameter acquisition device 300, such as... Figure 3 As shown, the performance parameter acquisition device 300 provided in this application includes: The determining module 301 is used to determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different; Test module 302 is used to perform performance parameter tests based on the first test parameters to obtain the first performance parameters of the controlled object in the optical image stabilization system under the first test parameters; Module 303 is used to obtain the correlation between performance parameters; The prediction module 304 is used to predict the performance parameters based on the performance parameter correlation and the first performance parameter, and to determine the second performance parameter of the controlled object under the second test parameter.

[0161] The performance parameter acquisition device proposed in this application may include electronic equipment installed on the production line, such as testing equipment, external prediction servers, etc.

[0162] The prediction module is configured to call various relationships, such as mapping functions or transformation functions, to predict parameters. The mapping function contains the relationship between performance parameters under different test conditions under the same controller parameters, and the transformation function contains the conversion logic between the actual response parameters and the performance parameters.

[0163] Specifically, after receiving a new controller parameter configuration command, the production line testing equipment first loads the controller parameters for the second test parameter through the parameter interface, for example, adjusting the proportional gain from Kp=2.5 to Kp=3.8, and simultaneously switching the test conditions from closed-loop to open-loop. At this point, the prediction module calls a conversion function from the server that matches the current test parameters. This function contains the conversion relationship between the actual response parameters and performance parameters of the controlled object under the closed-loop condition of PID parameter Kp=2.5. The testing equipment then inputs the acquired first performance parameters, such as closed-loop bandwidth of 35Hz and phase margin of 50°, along with the difference between the old and new controller parameters ΔKp=1.3, into the prediction module.

[0164] The prediction module is based on the amplitude and phase separation calculation rules in the transfer function. For example, it first decomposes the first performance parameter into an amplitude component of 15dB and a phase component of -90°. Then, it performs linear compensation on the amplitude based on the controller parameter difference ΔKp=1.3, calculating an intermediate amplitude of 17.2dB. The phase component is calculated through transfer function inversion, taking into account the phase characteristic difference between the old and new controllers, such as -15°, to output an adjusted phase of -105°. Finally, the synthesized second performance parameter is transmitted back to the test equipment via industrial Ethernet, completing the prediction of the open-loop performance under the new controller parameters. The entire process is relatively time-efficient.

[0165] Through the coordinated operation of the above systems, the production line testing equipment only needs to actually test and collect performance data under a set of test parameters in the initial stage. Then, it can quickly generate performance prediction results under various controller configuration schemes through the distributed prediction network, effectively solving the technical problem of mismatch between hardware resources and algorithm efficiency in traditional solutions.

[0166] As a preferred embodiment, the solution of this application is implemented as follows: In the production line testing stage of the optical image stabilization control system, the performance parameter acquisition device consists of a test station and a cloud computing platform. The test station is equipped with a data acquisition interface and a signal processing unit. The data acquisition interface is connected to the controller port of the optical image stabilization module via a GPIB or PCIe bus. The signal processing unit has a built-in DSP chip for real-time acquisition of the step response data of the first controller in open-loop mode. The cloud computing platform is equipped with a parameter prediction engine. After receiving the time-domain response waveform uploaded by the test station, the engine converts it into a frequency-domain amplitude spectrum through a fast Fourier transform module, and calls a pre-stored transfer function correction model to generate a predicted frequency response curve by combining it with the gain parameters of the second controller. The test station and the cloud platform establish a secure communication link through an industrial Ethernet network. The local cache module of the test station can temporarily store configuration parameter tables for at least three different controllers.

[0167] Through the above technical solution, this application effectively solves the problem of low collaboration efficiency between testing equipment and prediction algorithms, and realizes collaborative data processing and dynamic allocation of computing resources among multiple devices. Specifically, the testing workstation focuses on raw data acquisition and format conversion, while the cloud platform performs large-scale matrix operations. The two are decoupled through a standardized data interface, allowing the prediction module to be deployed independently on different hardware environments. Furthermore, the device supports parallel prediction calculation of multiple controller parameters. When the production line switches test batches, only the configuration parameter table needs to be updated to automatically adapt to the new test parameters, significantly shortening the equipment reconfiguration time.

[0168] This performance parameter acquisition device has all the beneficial effects of the above-mentioned performance parameter acquisition method, which will not be repeated here.

[0169] According to a third aspect of this application, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the performance parameter acquisition method described above. This non-transitory computer-readable storage medium possesses all the beneficial effects of the performance parameter acquisition method described above, which will not be elaborated further here.

[0170] According to a fourth aspect of this application, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the performance parameter acquisition method described above. This computer program product possesses all the beneficial effects of the performance parameter acquisition method described above, which will not be elaborated upon further herein.

[0171] According to a fifth aspect of this application, embodiments of this application also provide an electronic device 400, such as... Figure 4As shown, the device includes a memory 401 and a processor 402. The memory 401 stores a computer program; the processor 402 executes the computer program in the memory to implement the steps of the performance parameter acquisition method described above. This electronic device has all the beneficial effects of the performance parameter acquisition method described above, which will not be elaborated further in this application.

[0172] Computer-readable storage media can be, for example, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof, without particular limitation herein. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0173] In some embodiments of this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used or combined with an instruction execution system, apparatus, or device.

[0174] The aforementioned computer-readable storage medium may be included in the aforementioned electronic device or may exist independently without being assembled into the electronic device.

[0175] Computer program code for performing operations of some embodiments of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function.

[0177] It should also be noted that in some alternative implementations, the functions marked in the box may occur in a different order than those marked in the attached figures.

[0178] For example, two consecutively represented blocks can actually be executed in substantially parallel order, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, as well as combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0179] The units described in some embodiments of this application can be implemented in software or in hardware. The described units can also be located in a processor.

[0180] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0181] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0182] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0183] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0184] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and parts not described in detail in a certain embodiment can be referred to the relevant descriptions of other embodiments, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for obtaining performance parameters, characterized in that, include: Determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different; Based on the first test parameters, a performance parameter test is performed to obtain the first performance parameter of the controlled object in the optical image stabilization system under the first test parameters. Obtain the correlation relationship of performance parameters, which includes the correlation relationship between the first performance parameter under the first test parameter and the second performance parameter under the second test parameter; Based on the correlation between the performance parameters and the first performance parameter, the performance parameters are predicted to determine the second performance parameters of the controlled object under the second test parameters.

2. The performance parameter acquisition method according to claim 1, characterized in that, The second test parameter is the same as the controller parameter of the first test parameter but the test conditions are different. The step of predicting the performance parameters based on the performance parameter correlation and the first performance parameter, and determining the second performance parameter of the controlled object under the second test parameter, includes: Call the mapping function in the performance parameter association relationship, the mapping function including the association relationship between different performance parameters of the controlled object under the same controller parameter and different test conditions; Based on the mapping function, the second performance parameter is output according to the first test condition of the first test parameter, the second test condition of the second test parameter, and the first performance parameter.

3. The method for obtaining performance parameters according to claim 1, characterized in that, The second test parameter is different from the controller parameter of the first test parameter, but the test conditions are the same. The step of predicting the performance parameters based on the performance parameter correlation and the first performance parameter, and determining the second performance parameter of the controlled object under the second test parameter, includes: The conversion function in the performance parameter association relationship is invoked, and the conversion function includes the association relationship between the actual response parameters and performance parameters of the controlled object under known test parameters; Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and first test conditions of the first test parameters and the first performance parameters. The second performance parameter is output based on the actual response parameters of the controlled object, the second controller parameters of the second test parameter, and the second test conditions.

4. The method for obtaining performance parameters according to claim 1, characterized in that, The second test parameter is different from the controller parameter of the first test parameter but the test conditions are the same. The test conditions are closed-loop test conditions. The step of predicting the performance parameters based on the performance parameter correlation and the first performance parameter to determine the second performance parameter of the controlled object under the second test parameter includes: Call the mapping and transformation functions in the performance parameter association relationship; Based on the mapping function, the first controller parameter of the first test parameter and the first performance parameter under the first test condition are converted into the first controller parameter and the first performance parameter under the open-loop test condition. Based on the transformation function, the true response parameters of the controlled object are determined according to the first controller parameters of the first test parameters, the open-loop test conditions, and the first performance parameters. Based on the actual response parameters of the controlled object and the second controller parameters of the second test parameters, output the second controller parameters and the second performance parameters under the open-loop test conditions; Based on the mapping function, the second performance parameter under the second controller parameters and the open-loop test conditions is converted into the second controller parameters and the second performance parameter under the second test conditions.

5. The method for obtaining performance parameters according to claim 1, characterized in that, The second test parameter differs from the controller parameter of the first test parameter and the test conditions are different. The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter, and determining the second performance parameter of the controlled object under the second test parameter, includes: Call the mapping and transformation functions in the performance parameter association relationship; Based on the conversion function, the actual response parameters of the controlled object are determined according to the first controller parameters and first test conditions of the first test parameters and the first performance parameters. Based on the actual response parameters of the controlled object, the second controller parameters of the second test parameters, and the first test conditions, the first intermediate performance parameter is output; Based on the mapping function, the second performance parameter is output according to the second test condition, the first test condition, and the first intermediate performance parameter of the second test parameter.

6. The method for obtaining performance parameters according to claim 1, characterized in that, The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter, and determining the second performance parameter of the controlled object under the second test parameter, includes: Call the model correction function in the performance parameter correlation relationship; Based on the theoretical model of the controlled object, the first controller parameter and the first test condition of the first test parameter, and the first performance parameter, the modeling error function of the controlled object is output. Based on the model correction function, and according to the modeling error function and theoretical model of the controlled object, the true model of the controlled object is output; Based on the real model of the controlled object, the second performance parameter is output according to the second controller parameter and the second test conditions of the second test parameter.

7. The method for obtaining performance parameters according to any one of claims 1 to 6, characterized in that, The step of predicting performance parameters based on the performance parameter correlation and the first performance parameter, and determining the second performance parameter of the controlled object under the second test parameter, includes: Based on the correlation of the performance parameters, the first amplitude and the first phase of the first performance parameter in the frequency domain, the second amplitude and the second phase of the second performance parameter in the frequency domain are output.

8. A performance parameter acquisition device, characterized in that, include: A determination module is used to determine a first test parameter and a second test parameter, wherein at least one of the controller parameters and / or test conditions of the first test parameter and the second test parameter is different; The testing module is used to perform performance parameter testing based on the first test parameters to obtain the first performance parameters of the controlled object in the optical image stabilization system under the first test parameters. The acquisition module is used to obtain the correlation between performance parameters; The prediction module is used to predict performance parameters based on the correlation between the performance parameters and the first performance parameter, and to determine the second performance parameter of the controlled object under the second test parameter.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the performance parameter acquisition method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the performance parameter acquisition method according to any one of claims 1 to 7.

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