Multi-strategy segmented fusion adaptive SPGD algorithm for laser coherent combination system

Abstract

The invention belongs to the field of coherent combination, and discloses a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combination system. The method is used for solving the problems that in a traditional SPGD algorithm, due to the fact that a fixed gain coefficient and disturbance amplitude exist, the convergence speed and stability of the algorithm are contradictory, and the optimal performance is difficult to achieve in different environments and system states. According to the method, the algorithm is divided into three stages in the iteration process, and self-adaptive adjustment of the gain coefficient and the disturbance amplitude is realized according to the characteristics of a hardware system in combination with three different iteration strategies. According to the method, the convergence speed of the algorithm can be remarkably improved, meanwhile, the stability of the algorithm can be effectively improved, the algorithm has higher robustness in consideration of hardware system characteristics, and the good application prospect and feasibility of the algorithm are shown.

Description

Technical Field

[0001] The present invention relates to the technical field of laser coherent combining, and in particular to a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system. Background Art

[0002] High-energy lasers are widely used in numerous fields, including industry, aerospace, and military. However, due to their inherent physical properties, the output power of a single laser is also limited. Laser synthesis technology, which coherently combines multiple laser beams, can effectively increase the output power of a laser.

[0003] The main methods for coherent laser combining include heterodyne algorithms, multi-dither algorithms, particle swarm optimization, genetic algorithms, stochastic parallel gradient descent (SPGD), and machine learning. The SPGD algorithm, proposed by Vorontsov of the U.S. Army Research Institute in 1997, has been widely improved and applied in coherent laser combining systems due to its simple logical structure and ease of implementation. However, the traditional SPGD algorithm uses a fixed gain coefficient and perturbation amplitude, resulting in a conflict between the algorithm's convergence speed and effectiveness. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent synthesis system. The multi-strategy segmented fusion adaptive SPGD algorithm can efficiently control the phase of the laser beam, improve the convergence speed and stability of the algorithm, and when the algorithm is applied to a large-scale laser coherent synthesis system, it still has good performance.

[0005] The technical solution of the present invention to solve the above technical problems is:

[0006] A multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system includes the following steps:

[0007] S1. Set the initial parameters of the algorithm and select the power in the bucket PIB as the performance evaluation function J;

[0008] S2. Generate a random sequence and use it to generate a disturbance voltage δu (n) , where n represents the number of iterations;

[0009] S3, apply forward disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value

[0010] S4, apply negative disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value

[0011] S5. Calculate the change in performance evaluation function

[0012] S6. Calculate the performance evaluation function J and the maximum value J that the performance evaluation function can reach in the current environment max The ratio k of

[0013] S7. Determine whether it is in a convergence state by using k. If k>0.9, it is in a convergence state and execute S9; otherwise, execute S8.

[0014] S8. Determine whether it is in a quasi-convergence state by k. If k>0.75, it is in a quasi-convergence state and execute S10; otherwise, execute S11.

[0015] S9, execute strategy 3 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0016] S10, execute strategy 2 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0017] S11, execute strategy 1 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0018] S12, update the current iteration phase control voltage u (n) =u (n-1) +γ (n) δu (n) δJ (n) , where γ (n) is the gain coefficient;

[0019] S13. Determine whether the stop command sent by the host computer is received. If it is received, end the algorithm; otherwise jump to S2 and execute the above steps in a loop.

[0020] Preferably, in step S1, the initial algorithm parameters include: initial gain coefficient γ (0) , initial disturbance voltage δu (0) , the minimum step size of gain coefficient iteration γ step , the minimum step length of disturbance voltage iteration δu step , the maximum value J that the performance evaluation function can reach in the current environment max , the minimum voltage δu that the hardware system can effectively output min , the change threshold value δJ of the performance evaluation function in the convergence state thres , the maximum threshold value of the change of the performance evaluation function in the quasi-convergence state δJ thmax, minimum threshold δJ thmin ,The above parameters are obtained through multiple experimental tests.

[0021] Preferably, in step S9, the calculation steps of strategy 3 are as follows:

[0022] δu (n) =δu min (1)

[0023]

[0024] Where: δu min is the minimum voltage that the hardware system can effectively output, γ step is the minimum step size for gain coefficient iteration, δJ thres It is the threshold value of the change of the performance evaluation function in the convergence state.

[0025] Preferably, in step S10, the calculation steps of strategy 2 are as follows:

[0026]

[0027] Where: γ step is the minimum step size of gain coefficient iteration, δu step is the minimum step size of the perturbation voltage iteration, δJ thmax ,δJ thmin are the maximum threshold and minimum threshold of the change of the performance evaluation function under the quasi-convergence state respectively.

[0028] Preferably, in step S11, the calculation steps of strategy 1 are as follows:

[0029] δu (n) =δu (0) (5)

[0030]

[0031] Where: γ (0) is the initial gain coefficient, δu (0) is the initial disturbance voltage, J max It is the maximum value that the performance evaluation function can reach in the current environment.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention proposes a multi-strategy segmented fusion adaptive SPGD algorithm for laser coherent combining systems, which improves the shortcomings of traditional SPGD algorithms, which often use fixed gain coefficients and perturbation voltages. Specifically, during the algorithm's iterations, the algorithm is divided into converged, quasi-converged, and non-converged states, using a performance evaluation function and the maximum optical power achievable in the current environment. Different iteration strategies are employed in each state. This adaptive adjustment can better adapt to system changes and improve the algorithm's convergence speed and stability.

[0034] 2. The present invention proposes a multi-strategy segmented fusion adaptive SPGD algorithm for laser coherent synthesis system. The algorithm takes into account the maximum optical power value that can be achieved in the current environment and the minimum voltage that the hardware system can effectively output, effectively improving the robustness of the algorithm in the actual hardware environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a block diagram of the laser coherent combining system of the present invention using a multi-strategy segmented fusion adaptive SPGD algorithm for the laser coherent combining system.

[0036] Figure 2 This is a flowchart of the multi-strategy segmented fusion adaptive SPGD algorithm for the laser coherent synthesis system of the present invention.

[0037] Figure 3 This is a comparison graph of the photodetector voltage change when the multi-strategy segmented fusion adaptive SPGD algorithm for the laser coherent synthesis system of the present invention and the traditional SPGD algorithm converge from open loop to closed loop.

[0038] Figure 4 This is a graph comparing the photodetector voltages of the convergence of the multi-strategy segmented fusion adaptive SPGD algorithm for the laser coherent combining system of the present invention and the traditional SPGD algorithm. DETAILED DESCRIPTION

[0039] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0040] See also Figure 1The laser coherent synthesis system of the present invention, which uses a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent synthesis system, is shown in the figure. The laser coherent synthesis system mainly includes the following components: a seed laser, a beam splitter, a phase modulator, a collimator, a focusing lens, a polarizer, a beam splitter, a photodetector, an FPGA board, and a computer. The phase correction process of the laser coherent synthesis system is as follows: the seed laser is divided into M beams by the beam splitter, and the M beams are sequentially passed through the phase modulator, the collimator, the focusing lens, the polarizer, and then the beam splitter to divide the beam into two beams, one of which is input to the photodetector and the other is input to the camera. The photodetector feeds back the detected voltage signal to the ADC module in the FPGA board, and then the algorithm module in the FPGA calculates the disturbance voltage and outputs it to the phase modulator through the DAC module. In addition, the computer is connected to the FPGA via an Ethernet interface, and the host computer software is used to control the algorithm start, algorithm stop, and algorithm parameter setting.

[0041] See also Figure 2 The present invention provides a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system, comprising the following steps:

[0042] S1. The computer sets the algorithm parameters and initializes the algorithm parameters by communicating with the FPGA through Ethernet;

[0043] S2. Generate a random sequence and use it to generate a disturbance voltage δu (n) , where n represents the number of iterations;

[0044] S3, apply forward disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value

[0045] S4, apply negative disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value

[0046] S5. Calculate the change in performance evaluation function

[0047] S6. Calculate the performance evaluation function J and the maximum value J that the performance evaluation function can reach in the current environment max The ratio k of

[0048] S7. Determine whether it is in a convergence state by using k. If k>0.9, it is in a convergence state and execute S9; otherwise, execute S8.

[0049] S8. Determine whether it is in a quasi-convergence state by k. If k>0.75, it is in a quasi-convergence state and execute S10; otherwise, execute S11.

[0050] S9, execute strategy 3 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0051] S10, execute strategy 2 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0052] S11, execute strategy 1 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed;

[0053] S12, update the current iteration phase control voltage u (n) =u (n-1) +γ (n) δu (n) δJ (n) , where γ (n) is the gain coefficient;

[0054] S13. Determine whether the stop command sent by the host computer is received. If it is received, end the algorithm; otherwise jump to S2 and execute the above steps in a loop.

[0055] See also Figure 2 In step S1, the initial parameter settings of the algorithm include: initial gain coefficient γ (0) Set to 260, the initial disturbance voltage δu (0) Set to 30mv, the minimum step size of the gain coefficient iteration is γ step Set to 2, the minimum step size of the disturbance voltage iteration δu step Set to 1, the maximum value J that the performance evaluation function can reach in the current environment max Set to 760mv, the minimum voltage δu that the hardware system can effectively output min Set to 5mv, the change threshold of the performance evaluation function in the convergence state δJ thres Set to 3, the maximum threshold value of the change of the performance evaluation function in the quasi-convergence state δJ thmax Set to 3, minimum threshold δJ thmin Set to 1. The above parameters are obtained through multiple experimental tests.

[0056] See also Figure 2 In step S9, the calculation steps of strategy 3 are as follows:

[0057] δu (n) =δu min (1)

[0058]

[0059] Where: δu min is the minimum voltage that the hardware system can effectively output, γ step is the minimum step size for gain coefficient iteration, δJ thres It is the threshold value of the change of the performance evaluation function in the convergence state.

[0060] See also Figure 2 In step S10, the calculation steps of strategy 2 are as follows:

[0061]

[0062] Where: γ step is the minimum step size of gain coefficient iteration, δu step is the minimum step size of the perturbation voltage iteration, δJ thmax ,δJ thmin are the maximum threshold and minimum threshold of the change of the performance evaluation function under the quasi-convergence state respectively.

[0063] See also Figure 2 In step S11, the calculation steps of strategy 1 are as follows:

[0064] δu (n) =δu (0) (5)

[0065]

[0066] Where: γ (0) is the initial gain coefficient, δu (0) is the initial disturbance voltage, J max It is the maximum value that the performance evaluation function can reach in the current environment.

[0067] In order to verify the performance of the multi-strategy segmented fusion adaptive SPGD algorithm of the present invention, a 7-channel laser coherent synthesis comparison experiment was conducted on the algorithm of the present invention and the traditional SPGD algorithm. The convergence time of the traditional SPGD algorithm is 1.70ms, the variance after phase locking is 194.35, and the average voltage of the photodetector after phase locking is 689.5mv; the convergence time of the algorithm of the present invention is 0.54ms, the variance after phase locking is 108.6, and the average voltage of the photodetector after phase locking is 693.4mv. Compared with the traditional SPGD algorithm, the convergence speed of the algorithm of the present invention is increased by 68.23% and the variance is reduced by 44.12%. The comparison curve of the photodetector voltage change from open loop to closed loop convergence of the algorithm of the present invention and the traditional SPGD algorithm is shown in the figure below. Figure 3 As shown, the photodetector voltage comparison curve of the algorithm of the present invention and the traditional SPGD algorithm is shown in FIG. Figure 4 The results show that the algorithm of the present invention has faster convergence speed and better stability than the traditional SPGD algorithm.

Claims

1. A multi-strategy segmented fusion adaptive SPGD algorithm for laser coherent combining system, characterized by: The algorithm includes the following steps: S1. Set the initial parameters of the algorithm and select the power in the bucket PIB as the performance evaluation function J; S2. Generate a random sequence and use it to generate a disturbance voltage δu (n) , where n represents the number of iterations; S3, apply forward disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value S4, apply negative disturbance voltage to the phase modulator The optical power value of the M laser beam coherent synthesis detected by the photodetector is used as the corresponding performance evaluation function value S5. Calculate the change in performance evaluation function S6. Calculate the performance evaluation function J and the maximum value J that the performance evaluation function can reach in the current environment max The ratio k of S7. Determine whether it is in a convergence state by using k. If k>0.9, it is in a convergence state and execute S9; otherwise, execute S8. S8. Determine whether it is in a quasi-convergence state by k. If k>0.75, it is in a quasi-convergence state and execute S10; otherwise, execute S11. S9, execute strategy 3 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed; S10, execute strategy 2 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed; S11, execute strategy 1 to update the gain coefficient and disturbance amplitude, and execute S12 after the update is completed; S12, update the current iteration phase control voltage u (n) =u (n-1) +γ (n) δu (n) δJ (n) , where γ (n) is the gain coefficient; S13. Determine whether the stop command sent by the host computer is received. If it is received, end the algorithm; otherwise jump to S2 and execute the above steps in a loop.

2. The multi-strategy segmented fusion adaptive SPGD algorithm for laser coherent combining system according to claim 1, characterized in that: In step S1, the initial algorithm parameters include: initial gain coefficient γ (0) , initial disturbance voltage δu (0) , the minimum step size of gain coefficient iteration γ step , the minimum step length of disturbance voltage iteration δu step , the maximum value J that the performance evaluation function can reach in the current environment max , the minimum voltage δu that the hardware system can effectively output min , the threshold value of the change of the performance evaluation function in the convergence state δJ thres , the maximum threshold value δJ of the performance evaluation function change in the quasi-convergence state thmax , minimum threshold δJ thmin ,The above parameters are obtained through multiple experimental tests.

3. The multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system according to claim 1, characterized in that: In step S9, the calculation steps of strategy 3 are as follows: δu (n) =δu min (1) Where: δu min is the minimum voltage that the hardware system can effectively output, γ step is the minimum step size for gain coefficient iteration, δJ thres It is the threshold value of the change of the performance evaluation function in the convergence state.

4. The multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system according to claim 1, characterized in that: In step S10, the calculation steps of strategy 2 are as follows: Where: γ step is the minimum step size of gain coefficient iteration, δu step is the minimum step size of the perturbation voltage iteration, δJ thmax ,δJ thmin are the maximum threshold and minimum threshold of the change of the performance evaluation function under the quasi-convergence state respectively.

5. The multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combining system according to claim 1, characterized in that: In step S11, the calculation steps of strategy 1 are as follows: δu (n) =δu (0) (5) Where: γ (0) is the initial gain coefficient, δu (0) is the initial disturbance voltage, J max It is the maximum value that the performance evaluation function can reach in the current environment.

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