Closed-loop solution method for active optical correction based on iterative update of influence function

Abstract

The present invention relates to a closed-loop solution method for active optical correction based on iterative updating of influence functions. The method collects the initial influence functions of each actuating unit and replaces outliers. A wavefront sensing system detects and obtains the system wavefront aberration. The voltage to be applied to each actuating unit is calculated based on the relationship between the system wavefront aberration and the initial standard influence function under an ideal state, and the voltage is applied to the corresponding actuating unit. The system wavefront aberration is detected again, and the offset coefficient of each influence function is calculated based on the difference between the control surface shape residual under an actual state and the control surface shape residual under an ideal state. Each influence function is updated once according to the offset coefficient to obtain a new influence function. The new influence function is used as the initial standard influence function in the next round of regulation to achieve closed-loop solution of the correction amount of the wavefront corrector. The present invention realizes real-time iterative updating of the influence function and improves the accuracy of solving the active optical correction amount.

Description

Technical Field

[0001] The present invention relates to the technical field of optical system aberration correction, and in particular to a closed-loop solution method for active optical correction values ​​based on iterative updating of influence functions. Background Art

[0002] As requirements for the resolution and light-gathering capabilities of space telescopes continue to increase, the aperture of the primary mirrors in optical systems is growing. Compared to smaller-aperture mirrors, large-aperture mirror assemblies are more sensitive to environmental disturbances such as gravity fluctuations, temperature variations, and transportation and launch, and are more difficult to manufacture and assemble. This makes it difficult to maintain the surface accuracy of the primary mirrors, leading to a decrease in the imaging performance of the optical system. Ensuring high-performance on-orbit applications of space optical systems is a key issue in the development of large-aperture space optical systems, and maintaining the surface accuracy of the primary mirrors is a key component.

[0003] Active optical technology is achieved by adding an actuator unit to the back of the mirror body, which can achieve nanometer-level surface precision adjustment of the mirror, thereby compensating for manufacturing and adjustment residuals and environmental errors.

[0004] Active optical systems generally consist of three components: a wavefront sensor, a wavefront controller, and a wavefront corrector. The working process is as follows: the wavefront sensor is used to measure the aberration of the incident wavefront, the wavefront controller converts the aberration into a corresponding control signal and transmits it to the wavefront corrector. After the wavefront corrector receives the signal, it controls the output of the actuator to apply a correction force to the mirror surface to correct the wavefront aberration. Therefore, the wavefront corrector is the core component of the active optical system. The accuracy of the correction amount or adjustment amount of the wavefront corrector directly affects the accuracy of the target aberration correction. The schematic diagram of the active deformable mirror composition principle is as follows Figure 1 As shown, Figure 1 The deformable mirror structure shown is only one of many structures, and other deformable mirror structures may also be used.

[0005] There are two main algorithms for calculating the correction or adjustment amount of a wavefront corrector:

[0006] Algorithm 1: Single open-loop solution algorithm:

[0007] The main principle of the algorithm is as follows: Gaussian function is used to simulate and generate influence function, and open-loop solution algorithm is used to solve the voltage value of the actuator. The principle of the correction amount solution algorithm is: after measuring the influence function of each actuator, we can obtain the correction target aberration W(x, y) and the voltage v that should be applied to each actuator. n The relationship between them.

[0008]

[0009] Among them, I n(x, y) is the influence function of the nth actuator, and N is the number of actuators;

[0010] If the number of sampling points of the deformable mirror shape is M, the coordinates of the sampling points are (x m ,y m ), m=1,2,…,M, the wavefront aberration value at the mth point is W(x m ,y m ), the matrix equation shown in formula (2) can be listed and solved using the least squares method to obtain the voltage matrix V. This process is an open-loop process, and its influence function matrix is ​​measured one by one after the mirror components are installed. During the control process after a measurement is completed, the influence function is no longer updated.

[0011]

[0012] Nian Wei, Liu Zhaojun and others from the Beijing Institute of Space Mechanics and Electronics used this algorithm to analyze the correction capabilities of deformable mirrors in large-aperture space telescopes.

[0013] The adjustment amount calculation algorithm of Algorithm 1 mainly includes three steps: measuring the influence function, measuring the wavefront aberration, and calculating the adjustment amount.

[0014] Step 1: Measure the influence function of each actuator. After the deformable mirror is processed and assembled, the influence function of each actuator needs to be calibrated and measured. The measurement steps are as follows: ① The deformable mirror and the interferometer form a measurement optical path, and record the surface shape of the deformable mirror under the reference state; ② Apply a unit voltage to each actuator unit separately, and measure the surface shape change caused by the unit control voltage. The measurement result is the influence function I of this actuator unit. n (x, y); ③ The influence functions of all the actuator units measured are combined into an influence function matrix and stored as known parameters;

[0015] Step 2: Under actual working conditions, the wavefront aberration of the deformable mirror is measured as W(x, y), and the aberration information is transmitted to the wavefront controller component;

[0016] Step 3: Calculate the voltage v of each actuator unit through the correction amount solution algorithm n , and then the wavefront corrector controls the surface shape to produce corresponding deformation, thereby achieving the purpose of offsetting the aberration.

[0017] In Algorithm 1, the linear superposition relationship between the wavefront aberration and the influence function is adopted, and the voltage to be applied to each actuator is solved by the least squares method. However, Algorithm 1 has the following four major shortcomings:

[0018] 1. In existing methods, the influence function of the actuator is usually calibrated once and reused multiple times. However, in actual operation, the influence function of the actuator will cause offset changes due to external disturbances and return errors. Therefore, continuing to use the previously measured influence function to calculate the adjustment amount will no longer be accurate, resulting in the inability to accurately compensate for aberrations.

[0019] 2. When the influence function shifts in actual use, it needs to be recalibrated. Since the traditional calibration method requires measuring and calibrating the influence function of each actuator one by one, the calibration process is complicated and time-consuming. The calibration time increases linearly with the number of actuators.

[0020] 3. The existing calibration process for measuring the influence function of the actuator does not perform secondary processing on the measured influence function. The influence function contains burrs and anomalies caused by the internal noise and resolution of the interferometer, resulting in reduced solution accuracy.

[0021] 4. The process of calculating the actuator voltage from the target surface shape is a high-dimensional (multivariable) optimization problem. The algorithm generally uses the least squares method. In this solution model, the adjustment voltage of each actuator is an optimization variable. In the current calculation, only one round of calculation is performed, and the result obtained is not the optimal solution.

[0022] Algorithm 2: An adaptive optical deformable mirror control method based on the attention mechanism disclosed in the invention patent publication number CN117970799A;

[0023] This algorithm, proposed by the Institute of Optoelectronics Technology of the Chinese Academy of Sciences, uses open-loop control in a simulation environment to obtain high-performance control commands as a reinforcement learning dataset. Through transfer learning, the reinforcement learning model can be quickly transferred to different turbulent environments and improve the robustness of the model control. Compared with traditional wavefront correction, this method can overcome the problem that traditional proportional-integral control methods cannot effectively cope with turbulent changes. The main process of the algorithm is as follows:

[0024] A reinforcement learning model based on the actor-critic algorithm is constructed; the optimal control command sequence and corresponding PSF image sequence for adaptive optical open-loop control in a simulation environment are collected and placed in an experience pool; the reinforcement learning model is trained based on the obtained image sequence and the optimal control command sequence; image sequence information of a real-time optical environment is collected and control commands are calculated; the deformable mirror is controlled based on the calculated control commands; the obtained control commands and PSF image sequence information are placed in the experience pool; and the reinforcement learning model is updated based on the control sequence data set placed in the experience pool.

[0025] Algorithm 2 uses a pre-trained reinforcement learning model to calculate the control voltage, but it has the following two main shortcomings:

[0026] 1. This algorithm is mainly used in the field of adaptive optics to combat atmospheric turbulence, but is not fully applicable to active optics in the field of space optics;

[0027] 2. Existing deep learning methods require a large amount of data as training sets, and model training takes a long time. In addition, deep learning models are black box models with no mathematical basis and lack practical application reliability. Summary of the Invention

[0028] In response to the problems existing in the existing wavefront corrector correction amount calculation algorithm, the present invention proposes a closed-loop solution method for active optical correction amounts based on iterative updating of influence functions. This method improves the problems existing in the algorithm in the prior art by correcting the residual solution actuator influence function deviation and calibrating it in real time, thereby realizing real-time iterative updating of the influence function and further realizing accurate calculation of active optical correction amounts.

[0029] In order to solve the above problems, the present invention adopts the following technical solutions:

[0030] A closed-loop solution method for active optical correction based on iterative updating of influence functions, the method comprising the following steps:

[0031] Step 1: Collect the initial influence function I0 of each actuator unit in the current wavefront corrector n (x, y), and the initial influence function I0 n (x, y) is used to replace the abnormal points and obtain the initial standard influence function I of each actuator unit n (x, y), where n is the number of the actuator unit, n = 0, 1, 2, ..., N;

[0032] Step 2: Use the wavefront sensing system to detect the system wavefront aberration W1(x, y) before correction, and according to the ideal system wavefront aberration W1(x, y) and the initial standard influence function I of each actuator unit n (x, y) and the voltage v applied to each actuator unit n The matrix equation between is solved to obtain the voltage v1 that should be applied to each actuator unit. n , and then obtain the control surface shape and control surface shape residual ΔW in the ideal state respectively ideal (x,y), and according to the voltage v1 obtained by the solution n Apply to the corresponding actuating unit to control the mirror surface to undergo corresponding deformation;

[0033] Step 3: The wavefront sensing system detects the system wavefront aberration again to obtain the actual control surface shape and the control surface shape residual ΔW real (x, y), and the control surface shape residual ΔW in the actual statereal The residual error ΔW between (x, y) and the ideal control surface shape ideal (x, y) is subtracted to obtain ΔW p (x,y), the formula is as follows:

[0034] ΔW p (x,y)=ΔW real (x,y)-ΔW ideal (x,y) (13)

[0035] Substitute the calculation expression of each part of the control surface residual into it and get:

[0036]

[0037] Among them, k n is the offset coefficient of the nth influence function;

[0038] Step 4: According to ΔW shown in formula (14) p The relationship between (x, y) and the influence function used in this round of regulation is solved to calculate the offset coefficient of each influence function, and each influence function is updated once according to the offset coefficient to obtain a new influence function. Then, return to step 2 and use the new influence function as the initial standard influence function in the next round of regulation. Repeat steps 2 to 4 to achieve a closed-loop solution of the correction amount of the wavefront corrector.

[0039] The closed-loop solution method for active optical correction based on iterative updating of influence functions proposed in the present invention has the following advantages:

[0040] (1) To address the problem that the influence function of the actuator unit may shift during use, the present invention adopts an iteratively updated influence function. After each control process, the influence function is iteratively updated in real time based on the control residual to ensure the accuracy of the influence function, thereby improving the accuracy of the active optical correction calculation.

[0041] (2) In order to solve the problem that the traditional influence function measurement and calibration method is complicated and time-consuming, the present invention reversely calculates the influence function offset by the difference between the actual control residual and the ideal control residual, so as to achieve the effect of calculating all influence function offsets at one time, thus avoiding the time consumption caused by the need to recalibrate the influence function during use;

[0042] (3) The present invention adopts a method of obtaining the gradient of the influence function, screens out points where the gradient produces a sudden change, and fills the abnormal points with the closest non-outlier value, thereby reducing the error introduced in the actual measurement of the influence function;

[0043] (4) To solve the problem that the solution result is not the optimal solution, the present invention adopts a secondary solution strategy to achieve the optimal solution based on the relationship curve between the number of solutions and the solution accuracy;

[0044] (5) The present invention takes into account the laws of environmental disturbances in space optical systems and has a good correction effect on the deviation of the influence function caused by regular temporal low-frequency disturbances, such as orbital periodicity and radiation periodicity;

[0045] (6) Compared with deep learning methods, the model does not require a large amount of data set training. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without exceeding the scope of protection required by the present invention.

[0047] Figure 1 This is a schematic diagram of the active deformable mirror construction principle;

[0048] Figure 2 This is a flow chart of a closed-loop solution method for active optical correction based on iterative updating of influence functions according to an embodiment of the present invention;

[0049] Figure 3 The nonlinear “displacement-voltage” response curve of the piezoelectric ceramic of the actuator unit;

[0050] Figure 4 Comparison chart of the RMSE of the correction residual after multiple rounds of aberration correction under different conditions. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] In one embodiment, Figure 2 As shown, the present invention provides a closed-loop solution method for active optical correction based on iterative update of influence function, which includes the following steps 1 to 4.

[0053] Step 1: Acquisition and processing of the initial influence function. In this step, a unit voltage is applied to each actuator unit in the wavefront corrector (taking the deformable mirror as an example), and the initial influence function of each actuator unit is measured by interferometer. The gradient of the initial influence function is then solved, and the coordinates of the mutation points in the gradient map are marked. The values ​​at the mutation point coordinates in the influence function map are then replaced using nearest point interpolation to eliminate abnormal values ​​in the initial influence function caused by the interferometer, and finally the initial standard influence function of each actuator unit is obtained.

[0054] Specifically, assuming that there are N actuator units in the deformable mirror, the initial influence function of each actuator unit is obtained by interferometer acquisition and measurement. The initial influence function can be expressed as I0 n (x, y), where n is the number of the actuator unit, I0 n (x, y) represents the initial influence function of the nth actuator unit. Next, the initial influence function I0 n (x, y) is used to replace outliers.

[0055] Step 1.1: During the acquisition of the initial influence function, due to factors such as internal noise in the interferometer, burrs will appear at the edge of the detection, resulting in the appearance of abnormal values ​​in the initial influence function. If not processed, it will affect the solution result. Therefore, the initial influence function I0 is calculated first. n The image gradient information of (x, y) is calculated as follows:

[0056] (Gx, Gy) = Gradient(I0 n (x, y)) (3)

[0057] Among them, Gx and Gy are the gradients in the x-direction and y-direction respectively.

[0058] Step 1.2: Identify the mutation point in the gradient map as an outlier and record the coordinates of the outlier (x0, y0), as shown in formula (4). Use the find() function to find the coordinates of the outlier, and then use the nearest point interpolation to replace the value at the outlier, as shown in formula (5):

[0059]

[0060] I0 n (x0,y0)=Nearest(x0,y0) (5)

[0061] T0 is the outlier threshold, generally defined as a value that deviates from the mean by more than three standard deviations. Nearest(x0,y0) is the nearest point interpolation function, which fills outliers with the nearest non-outlier value. This means that the value at the coordinate (x0,y0) in the initial influence function graph is replaced with the nearest point interpolation value, eliminating outliers introduced by the interferometer in the initial influence function.

[0062] Step 1.3: After all abnormal points are replaced, the initial standard influence function I of the actuator unit is obtained. n (x, y).

[0063] Step 2: The wavefront sensing system measures the system wavefront aberration and performs the first correction. This step uses the wavefront sensing system to detect the system wavefront aberration. The wavefront sensing system mentioned in this invention does not only refer to actual devices. Any physical device capable of obtaining system wavefront information, including but not limited to wavefront sensors or image processing algorithms, is considered a wavefront sensing system.

[0064] After the first system wavefront aberration W1(x, y) to be corrected is obtained by measuring the wavefront sensing system, the system wavefront aberration W1(x, y) and the initial standard influence function I of each actuator unit are combined. n (x, y) and the voltage v applied to each actuator unit n The relationship between them is listed, and the control voltage matrix V of this round of regulation is obtained by solving the relationship between the system wavefront aberration and the influence function.

[0065] In an ideal state (i.e., when the influence function is not shifted), the system wavefront aberration W1(x, t), the initial standard influence function I n (x, y) and voltage v n The relationship between them is shown as follows:

[0066]

[0067] If the number of sampling points of the deformable mirror shape is M, the coordinates of the sampling points are (x m ,y m ), m=1,2,…,M, the wavefront aberration value at the mth point is W1(x m ,y m ), then according to formula (6), the matrix equation shown in formula (7) can be listed, and the matrix equation is solved using the twice least square method to obtain the voltage v1 that should be applied to each actuator unit. n , forming a control voltage matrix V. Optionally, the least squares method used in this step can also be replaced by other optimization algorithms, including but not limited to genetic algorithms, simulated annealing algorithms, etc.

[0068]

[0069] The ideal control surface shape is:

[0070]

[0071] The control surface shape residual under ideal conditions (i.e., ideal correction residual) is:

[0072] ΔW ideal (x,y)=W ideal (x,y)-W1(x,y) (9)

[0073] Calculate the voltage v1 that should be applied to each actuator unit n After that, the wavefront control system will solve the various voltages v1 n It is applied to the corresponding actuating unit on the wavefront corrector to control the corresponding deformation of the deformable mirror surface to achieve surface correction.

[0074] Step 3: Back-calculate the influence function deviation based on the control results.

[0075] In actual working conditions, the influence function is not stable and will shift due to the return error of the actuator unit and environmental factors. Let the influence function after shift be k n I n (x, y), k n is the offset coefficient of the nth influence function. Since the offset is unknown when calculating the voltage, the initial standard influence function under the ideal state is still substituted into the calculation. The voltage applied to each actuator unit is calculated to be v n However, the control surface generated in the actual state is:

[0076]

[0077] The control surface shape residual in the actual state (i.e. the actual correction residual) is:

[0078] ΔW real (x,y)=W real (x,y)-W1(x,y) (11)

[0079] After transforming formula (11), we can get:

[0080] ΔW real (x,y)=W real (x,y)-W ideal (x,y)+W ideal (x,y)-W1(x,y)=ΔW p (x,y)+ΔW ideal (x,y) (12)

[0081] Definition of ΔW p (x,y)=W real (x,y)-W ideal (x, y) is the difference between the ideal control surface and the actual control surface. According to formula (12),

[0082] ΔW p (x,y)=ΔW real (x,y)-ΔW ideal (x,y) (13)

[0083] Substituting the calculation expression of the residual error of each part of the control surface, we can get ΔW p The relationship between (x, y) and the influence function used in this round of regulation is shown in the following formula:

[0084]

[0085] Formula (14) is expressed in matrix form as:

[0086]

[0087] Where ΔW p (x,y) The residual error ΔW of the control surface shape in the actual state measured by the wavefront sensing system real (x, y) and the calculated control surface shape residual ΔW under the ideal state ideal (x,y) is obtained by subtracting them.

[0088] Step 4: According to the matrix equation of formula (15), the offset coefficient k of each influence function can be obtained by solving n , according to the offset coefficient k n Update each influence function once to obtain a new influence function. The update formula is as follows:

[0089] I2 n (x, y) = k n I n (x, y) (16)

[0090] Among them, I2 n (x, y) is the new influence function obtained after updating.

[0091] At this point, one round of control is complete, and the influence function is updated. Then, return to step 2 and use the new influence function as the initial standard influence function for the next round of control. Repeat steps 2 through 4 for each subsequent round of control, iterating the influence function after each control cycle to ensure the accuracy of the influence function and, consequently, the accuracy of the adjustment calculation, ultimately achieving a closed-loop solution for the wavefront corrector's corrections.

[0092] The present invention improves upon the problems in the existing adjustment amount calculation algorithm and has the following advantages over Algorithm 1:

[0093] 1. To address the issue of the actuator influence function shifting during use, the present invention adopts an iteratively updated influence function. After each control process, the influence function is iteratively updated in real time based on the control residual, ensuring the accuracy of the influence function and thereby improving the accuracy of the active optical correction calculation.

[0094] 2. To address the problem of complex and time-consuming traditional influence function measurement and calibration methods, this invention reversely calculates the influence function offset by taking the difference between the actual control residual and the ideal control residual, achieving the effect of calculating all influence function offsets in one go. This avoids the time-consuming need to recalibrate the influence function during use.

[0095] 3. The present invention adopts a method of obtaining the gradient of the influence function, screens out points where the gradient produces mutations, and fills the abnormal points with the closest non-outlier value, thereby reducing the error introduced in the actual measurement of the influence function.

[0096] Compared with Algorithm 2, it has the following advantages:

[0097] 1. To solve the problem that the solution result is not the optimal solution, the present invention adopts a secondary solution strategy to achieve the optimal solution based on the relationship curve between the number of solutions and the solution accuracy;

[0098] 2. The present invention takes into account the regularity of environmental disturbances received by space optical systems and has an excellent correction effect on the deviation of the influence function caused by regular temporal low-frequency disturbances, such as orbital periodicity and radiation periodicity.

[0099] 3. Compared with deep learning methods, the model does not require a large amount of data set training.

[0100] The mirror of a space optical camera is subject to various disturbances in the environment. The characteristics of the disturbances can be divided into two categories: regular disturbances and random disturbances. Regular disturbances mainly include:

[0101] Thermal deformation: Temperature changes in the space environment can cause thermal expansion and contraction of optical components, thus affecting the shape of the mirror. This disturbance usually has a certain regularity, such as the temperature difference between day and night or the periodicity of the orbit;

[0102] Radiation pressure: Solar radiation and other electromagnetic radiation exert pressure on the mirror, causing the mirror to change shape. The intensity and direction of these radiations also have regularity.

[0103] Mechanical vibration: Satellite attitude control, launch process or other mechanical movements will cause vibrations in the mirror, which usually follow a specific frequency and amplitude.

[0104] Regular disturbances can be modeled as periodic aberrations of different frequencies.

[0105] Random disturbances are generally manifested as noise within the system and the effects of random airflow or stray radiation in the environment. They can be modeled as random noise disturbances.

[0106] Regarding the displacement error of the actuator unit with the voltage cycle, the present invention carries out the following simulation analysis of the correction capability:

[0107] The response curve of the actuator unit displacement to voltage is as follows: Figure 3 As shown, the piezoelectric ceramics in the actuator unit exhibit nonlinear response characteristics, causing a certain amount of drift in their standard position as the voltage increases and decreases during use. Because the form of aberrations is determined by the form of disturbances in the system, and the form of disturbances in the system exhibits a periodic pattern, the form of aberrations also exhibits a periodic pattern. When adjusting these specific combinations of aberrations, the resulting return error also varies in a regular pattern due to the nonlinear response characteristics of the actuator unit.

[0108] Therefore, the offset of the influence function is positively correlated with the offset of the error in the environment. The offset error of the influence function is also periodic, and its period is consistent with the aberration period. A periodic error similar to a trigonometric function is defined, and multiple rounds of aberration correction are performed. The RMSE of the correction residual is calculated under ideal conditions (no drift of the influence function), actual conditions (drift of the influence function), and after adjustment (using iteratively updated influence function). The results are Figure 4 As shown in Figure 3, it can be seen that the surface shape residual calculated using the iteratively updated influence function is very close to the ideal value, which is a significant improvement compared to existing methods.

[0109] The calculation algorithm of the present invention is used to perform real-time correction on the deviation of a certain influence function generated by simulation, and the correction result is ideal. The error model that can be actually applied by the present invention is not limited to the above-mentioned example model.

[0110] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A closed-loop solution method for active optical correction based on iterative updating of influence functions, characterized in that: The following steps are involved: Step 1: Collect the initial influence function I0 of each actuator unit in the current wavefront corrector n (x, y), and the initial influence function I0 n (x, y) is used to replace the abnormal points and obtain the initial standard influence function I of each actuator unit n (x, y), where n is the number of the actuator unit, n = 0, 1, 2, ..., N; Step 2: Use the wavefront sensing system to detect the system wavefront aberration W1(x, y) before correction, and according to the ideal system wavefront aberration W1(x, y) and the initial standard influence function I of each actuator unit n (x, y) and the voltage v applied to each actuator unit n The matrix equation between is solved to obtain the voltage v1 that should be applied to each actuator unit. n , and then obtain the control surface shape and control surface shape residual ΔW in the ideal state respectively ideal (x,y), and according to the voltage n1 obtained by the solution n Apply to the corresponding actuating unit to control the mirror surface to undergo corresponding deformation; Step 3: The wavefront sensing system detects the system wavefront aberration again to obtain the actual control surface shape and the control surface shape residual ΔW real (x, y), and the control surface shape residual ΔW in the actual state real The residual error ΔW between (x, y) and the ideal control surface shape ideal (x, y) is subtracted to obtain ΔW p (x,y), the formula is as follows: ΔW p (x,y)=ΔW real (x,y)-ΔW ideal (x,y) (13) Substitute the calculation expression of each part of the control surface residual into it and get: Among them, k n is the offset coefficient of the nth influence function; Step 4: According to ΔW shown in formula (14) p The relationship between (x, y) and the influence function used in this round of regulation is solved to calculate the offset coefficient of each influence function, and each influence function is updated once according to the offset coefficient to obtain a new influence function. Then, return to step 2 and use the new influence function as the initial standard influence function in the next round of regulation. Repeat steps 2 to 4 to achieve a closed-loop solution of the correction amount of the wavefront corrector.

2. The closed-loop solution method for active optical correction based on iterative update of influence function according to claim 1 is characterized in that: In step 1, the process of replacing outliers in the influence function includes the following steps: Step 1.1: Calculate each initial influence function I0 n The image gradient information of (x, y) is calculated as follows: (Gx,Gy)=Gradient(I0 n (x,y)) (3) Among them, Gx and Gy are the gradients in the x-direction and y-direction respectively; Step 1.2: Identify the mutation point in the gradient map as an outlier, record the coordinates of the outlier point (x0, y0), and use the nearest point interpolation to replace the value at the outlier point. The formula is as follows: I0 n (x0,y0)=Nearest(x0,y0) (5) Among them, T0 is the outlier threshold, Nearest(x0,y0) is the nearest point interpolation function of the outlier; Step 1.3: After all abnormal points are replaced, the initial standard influence function I of the actuator unit is obtained. n (x, y).

3. The closed-loop solution method for active optical correction based on iterative update of influence function according to claim 1 or 2, characterized in that: The initial influence function of each actuator unit is obtained by interferometer measurement.

4. The closed-loop solution method for active optical correction based on iterative update of influence function according to claim 1 or 2, characterized in that: The voltage v1 that should be applied to each actuator unit is obtained by using the least squares method twice n .

5. The closed-loop solution method for active optical correction based on iterative update of influence function according to claim 4 is characterized in that: Use genetic algorithm or simulated annealing algorithm to replace the least squares method.

6. The closed-loop solution method for active optical correction based on iterative update of influence function according to claim 1 or 2, characterized in that: The formula for updating each influence function according to the offset coefficient is as follows: I2 n (x,y)=k n I n (x,y) (16) Among them, I2 n (x, y) is the new influence function obtained after updating.

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