Ship-borne optoelectronic equipment turning time optimization method based on particle swarm optimization
By combining particle swarm optimization with Simulink models, the position loop control and system structure parameters of shipborne optoelectronic equipment were optimized, solving the problem of low efficiency in turnaround time optimization. This enabled fast and efficient turnaround time optimization and improved the equipment's responsiveness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-04-10
AI Technical Summary
Existing methods for optimizing the turnaround time of shipborne optoelectronic equipment rely on traditional experience and trial-and-error approaches, resulting in long iteration cycles, low efficiency, high costs, and difficulty in quickly optimizing design parameters.
An optimization program was written in Matlab using the particle swarm optimization algorithm and combined with the Simulink model. The shortest turn-in time was used as the fitness function to optimize the position loop control parameters and system structure parameters of the optoelectronic device, achieving rapid iterative optimization.
It significantly shortens the adjustment time of optoelectronic equipment, improves rapid response capability and system engineering practicality, and avoids the problem of inconsistency between error integral indicators and actual performance in traditional methods.
Smart Images
Figure CN121832279A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of shipborne optoelectronic equipment control, and more specifically, to a method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization. Background Technology
[0002] Shipborne optoelectronic equipment possesses multi-directional, all-weather, high-tech reconnaissance and precision strike capabilities, and is widely used in military reconnaissance, disaster reconnaissance, resource forecasting, and other fields, serving as a key tool for rapidly and intuitively acquiring information. Because it frequently requires large-angle turns during daily operation, the turn-in time becomes a crucial indicator of the optoelectronic equipment's rapid response capability. However, existing turn-in time optimization methods in the design phase mainly rely on traditional experience and trial-and-error approaches, which have significant drawbacks: firstly, the optimization process depends on extensive manual debugging and repeated experiments, resulting in long iteration cycles and low efficiency; secondly, it is difficult to accurately find the optimal design parameters during the optimization process, leading to high development costs. Therefore, existing methods cannot meet the demand for rapid and efficient turn-in time optimization, necessitating a more intelligent and efficient optimization strategy. Summary of the Invention
[0003] The purpose of this invention is to propose a method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization, thereby solving the problems of long cycle, low efficiency, high cost, and difficulty in quickly optimizing design parameters in the process of shipborne optoelectronic equipment turning at large angles.
[0004] The technical solution of this invention is: to provide a method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization algorithm. This method is implemented by writing particle swarm optimization program code using m-functions in the Matlab environment and building a Simulink model of the optoelectronic equipment reaction time, including:
[0005] Step 1: Initialize the particle swarm and decode the individual particles in the initial particle swarm into the position loop control parameters and system structure parameters of the optoelectronic device;
[0006] Step 2: Input the position loop control parameters and system structure parameters into the Simulink model, use the Simulink model to simulate the photoelectric device to perform large-angle rapid turning and positioning simulation, and output the turning time corresponding to the initial particle swarm.
[0007] Step 3: Select the shortest arrival time criterion as the fitness function of the iterative optimization process. Based on the fitness function, determine the individual historical best position and the global historical best position corresponding to the initial particle swarm, and assign the turn-in time corresponding to the individual historical best position and the global historical best position to the preset variable.
[0008] Step 4: Based on the currently obtained individual historical best position and global historical best position, update the position loop control parameters and system structure parameters of each particle in the particle swarm.
[0009] Step 5: Input the updated position loop control parameters and system structure parameters into the Simulink model to obtain the turn-in time corresponding to the new generation of particle swarm.
[0010] Step 6: Determine the individual historical best position and global historical best position corresponding to the new generation of particle swarm based on the fitness function. Compare the turn-in time corresponding to the individual historical best position and global historical best position with the preset variable corresponding to the previous generation of particle swarm, and take the smaller value and assign it to the preset variable again.
[0011] Step 7: Determine whether the termination condition has been met based on the preset variables before and after the update. If so, end the iteration and take the position loop control parameters and system structure parameters corresponding to the current global historical best position as the optimal parameter values. Otherwise, return to step 4.
[0012] Furthermore, step 3 specifically includes:
[0013] The Simulink model outputs the turn-in time for each particle in the population. Based on the shortest turn-in time criterion, it determines the optimal position found by a single particle so far. And the best position found by the entire particle swarm so far ,Will and The corresponding adjustment time is assigned to the preset variable E, E=[ , ],in, Let be the turn-in time corresponding to the historical best position of the i-th particle obtained in the k-th iteration step. Let be the turn-in time corresponding to the historical best position of the entire particle swarm obtained in the k-th iteration step.
[0014] Furthermore, step 4 specifically includes:
[0015] Using the current particle population as the parent population, the velocity of the offspring population is calculated based on inertia weights, the velocity and position of the parent population individuals, as well as the currently obtained historical best position and the global historical best position. The position of the offspring population is then calculated based on the offspring population velocity and the parent population position, expressed as:
[0016] ;
[0017] ;
[0018] In the formula, For inertial weights, Let be the velocity vector of the i-th particle in the k-th iteration step. Let i be the position vector of the i-th particle in the k-th iteration step. To obtain the historical best position of the i-th particle in the k-th iteration step, To obtain the historical best position of the entire particle swarm in the k-th iteration step, For individual learning factors, As a population learning factor, and For random variables,
[0019] Furthermore, step 4 also includes: decoding the offspring population individuals into the new position loop control parameters and system structure parameters of the optoelectronic device.
[0020] Furthermore, step 6 specifically includes:
[0021] Based on the shortest arrival time criterion, the optimal position searched so far for a single particle is determined. And the best position found by the entire particle swarm so far , will the current and The corresponding turn-in time is the same as the turn-in time in the preset variable E corresponding to the previous generation particle swarm. , The values are compared, and the smaller value is assigned to the preset variable E to form the preset variable E=[ for the new generation particle swarm.] , ].
[0022] Furthermore, in step 1, initializing the particle population specifically includes:
[0023] Set the population size, inertia weight, learning factor, particle velocity range, maximum number of iterations, and minimum precision value in the iteration termination condition; the position loop control parameters include the proportional parameter K. p Integral parameter K i Differential parameter K d The system structural parameters include the eccentricity d in the x-axis direction. x y-axis eccentricity d y eccentricity d in the z-axis direction z Given the frictional force F, the iterative optimization constraint is set as: K p K i K d The value range of d is [0, 300]. x d y d zThe value range of is [0.0001, 0.01], and the value range of F is [0.1, 0.5].
[0024] Furthermore, step 1 also specifically includes:
[0025] Based on iterative optimization constraints, a particle swarm is randomly initialized in the search space, and the individual particles in the initial swarm are decoded into the position loop control parameters K of the optoelectronic device. p K i K d and system structural parameters d x d y d z 、F.
[0026] Furthermore, step 7 specifically includes:
[0027] Compare the first preset variable corresponding to the new generation of particle swarm with the second preset variable corresponding to the previous generation of particle swarm. If the maximum number of iterations or the first preset variable is reached... and With the second preset variable and If the absolute difference is less than 0.1, the termination condition is met; otherwise, return to step 4.
[0028] Furthermore, in the first preset variable and With the second preset variable and The absolute differences are all less than 0.1, expressed as: and .
[0029] The beneficial effects of this invention are:
[0030] The technical solution of this invention uses the position loop control parameters and system structural parameters of the optoelectronic device as particle parameters. Parameter optimization is achieved through the joint operation of a particle swarm optimization algorithm and a Simulink model. In each particle iteration, the shortest dynamically changing turn-in time is used as the fitness of the optimization process. This shortest turn-in time guides the particle parameters to update in the direction of shortening the turn-in time, thereby gradually approaching the globally optimal parameter combination. This invention establishes a direct mapping relationship between particle parameters and the dynamic turning performance of the optoelectronic device. Unlike existing technologies that use error integral indices such as ITAE as the fitness function, this invention abandons the traditional classification performance index ITAE and uses the shortest dynamically changing turn-in time as the fitness of the optimization process. This allows for continuous adjustment of particle parameters during iteration with the goal of minimizing the turn-in time, ensuring that the optimization results directly reflect the system's true dynamic response capability under actual large-angle turning conditions, thus avoiding the inconsistency between error integral indices and actual turning performance.
[0031] Meanwhile, the technical solution of this invention further redefines the convergence criterion of the particle swarm optimization algorithm based on the shortest turn-in time of dynamic transformation. The change in the shortest turn-in time obtained from two adjacent iterations is used as the basis for accuracy judgment. When the change is less than a preset threshold, the algorithm is determined to have converged, thereby replacing the traditional termination condition based on error integral or particle parameter difference. This allows the particle swarm optimization method, which was originally only applicable to the optimization of error integral indexes, to be directly applied to the dynamic optimization problem of the turn-in time of optoelectronic equipment. It enables rapid collaborative search and global optimization of position loop control parameters and system structural parameters, significantly shortens the turn-in time of optoelectronic equipment, and improves the system's rapid response capability and engineering practicality. Attached Figure Description
[0032] The advantages of the above and additional aspects of the present invention will become apparent and readily understood in the description of the embodiments in conjunction with the following drawings, wherein:
[0033] Figure 1 This is a schematic flowchart of a method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization according to an embodiment of the present invention;
[0034] Figure 2 This is the control parameter optimization curve according to Example 1 of the present invention;
[0035] Figure 3 This is the structural parameter optimization curve according to Example 1 of the present invention;
[0036] Figure 4 This is the optimal individual fitness value optimization curve according to Example 1 of the present invention;
[0037] Figure 5This is a comparison diagram of the existing turning time optimization method according to Example 1 of the present invention and the turning in place error corresponding to the method of the present invention. Detailed Implementation
[0038] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.
[0039] In the following description, many specific details are set forth in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.
[0040] like Figure 1 As shown, this embodiment provides a method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization. This method is implemented by writing particle swarm optimization program code using m-functions in the Matlab environment and building a Simulink model of the optoelectronic equipment's reaction time. Specifically, it includes:
[0041] Step 1: Initialize the particle swarm. Decode the individual particles in the initial particle swarm into the position loop control parameters and system structural parameters of the optoelectronic device. The system structural parameters include eccentricity and friction.
[0042] Specifically, the population size is set to 5, the inertia weight to a fixed value of 0.6, the learning factor to a fixed value of 2, the particle velocity range to [-1, 1], and the maximum number of iterations and minimum precision in the iteration termination condition to be 10 and 0.1, respectively; the position loop control parameters include the proportional parameter K. p Integral parameter K i Differential parameter K d The system structural parameters include the eccentricity d in the x-axis direction. x y-axis eccentricity d y eccentricity d in the z-axis direction z (Eccentricity refers to the mechanical eccentricity of the photoelectric device in the mechanical transmission) and friction force F (the equivalent value of the nonlinear friction torque in the position ring transmission system of the photoelectric device), the iterative optimization constraint is set as: K p K i K d The value range of d is [0, 300]. x d y d z The value range of is [0.0001, 0.01], and the value range of F is [0.1, 0.5].
[0043] Based on the above parameters and their value ranges, a particle swarm is randomly initialized in the search space, and the individual particles in the initial swarm are decoded into the position loop control parameters K of the optoelectronic device. p K i K d and system structural parameters d x d y d z F (which decodes the specific parameter values of a single particle).
[0044] It's important to note that the decoding process essentially maps the particle's dimension values to specific parameter values. In particle swarm optimization, particles represent solutions using dimensional vectors, with each dimension corresponding to a parameter. In this embodiment, the particle's position is a 7-dimensional vector. During particle initialization, these dimensional values are randomly generated within a pre-defined range. The particle decoding process involves directly assigning these values to the parameter K. p K i K d d x d y d z F, to form a specific control scheme.
[0045] Step 2: Input the position loop control parameters and system structure parameters into the Simulink model, and use the Simulink model to simulate the large-angle rapid turning and positioning of the photoelectric device (i.e., the simulation runs to obtain the time required for the position loop transmission system of the photoelectric device to complete the large-angle turning and positioning from receiving the command) and output the turning time corresponding to the initial particle swarm.
[0046] It should be noted that after selecting a specific optoelectronic device, a Simulink model can be built using standard modules based on existing control theory, according to the operation process of the optoelectronic device. The Simulink model can receive the position loop control parameters and system structure parameters after particle swarm decoding, and simulate the complete large-angle rapid turning and positioning process of the optoelectronic device based on the turning control logic under a given target position command. After the simulation is run, the time required for turning into position is output.
[0047] Specifically, the Simulink model includes at least a position loop control module (which controls the motor output and adjusts the device's position by calculating the position error), a drive module (which converts the control signal output from the position loop control module into the motor's angular velocity and angular displacement), an eccentricity impact module (which corrects the motor's control output signal based on the system's structural eccentricity to ensure motion accuracy), a feedback section (which provides feedback on the motor's current position to ensure the position loop control module can adjust its output in real time), and a turning completion judgment module (which determines whether the photoelectric device has completed a large-angle turning and positioning and outputs the turning time). The position loop control module is used to determine the error between the target position and the current position of the photoelectric device. Calculate control signals (The position loop control module can use a PID controller), represented as:
[0048] ;
[0049] In the formula, t represents time; the drive module is used to convert the control signal output by the PID controller into the angular velocity of the motor. and angular displacement , is represented as:
[0050] ;
[0051] ;
[0052] In the formula, J is the moment of inertia. This is the torque coefficient of the motor. The time step is d; the module for the influence of eccentricity on the system is used to determine the eccentricity d. x d y d z Correcting the motor control signal in the drive module will cause an additional torque due to eccentricity: M e =d x F force =d y F force =d z F force The corrected control signal is: u c =u(t)+∑M e / J; The feedback section is used to update the motor's current position. Feedback is sent to the PID controller to calculate the new error e(t), which in turn updates the control signal. Through closed-loop control, this ensures that the motor position reaches the expected target; the rotation completion judgment module detects the angular displacement in real time. If the target position is reached, the simulation ends. The time from receiving the position loop control parameters and system structure parameters to the end of the simulation is counted and used as the turn-in time output.
[0053] Step 3: Select the shortest arrival time criterion as the fitness function of the iterative optimization process (i.e., the optimization objective is to minimize the turn-in time). Based on this fitness function, determine the individual historical best position and the global historical best position corresponding to the initial particle swarm, and assign the turn-in time corresponding to the individual historical best position and the global historical best position to preset variables for subsequent comparison.
[0054] Specifically, the Simulink model outputs the turn-in time for each particle in the population, and determines the optimal position found by a single particle so far based on the shortest arrival time criterion. And the best position found by the entire particle swarm so far ,Will and The corresponding adjustment time is assigned to the preset variable E, E=[ , ],in, Let be the turn-in time corresponding to the historical best position of the i-th particle obtained in the k-th iteration step. Let be the turn-in time corresponding to the historical best position of the entire particle swarm obtained in the k-th iteration step.
[0055] In this embodiment, since the initial particle swarm only has one set of individual historical best positions and the global historical best position, its initial... and The corresponding adjustment time can be assigned to the preset variable E.
[0056] Step 4: Based on the currently obtained individual historical best position and global historical best position, update the position loop control parameters and system structure parameters of each particle in the particle swarm.
[0057] Specifically, taking the current particle population as the parent population, the velocity of the offspring population is calculated based on the inertia weight, the velocity and position of the parent population individuals, as well as the currently obtained historical best position and the global historical best position. Then, the position of the offspring population is calculated based on the offspring population velocity and the parent population position, expressed as:
[0058] ;
[0059] ;
[0060] In the formula, For inertial weights, It is a fixed value of 0.6. Let be the velocity vector of the i-th particle in the k-th iteration step, corresponding to its parent. Let be the velocity vector of the i-th particle in the (k+1)-th iteration step, and its corresponding offspring. Let be the position vector of the i-th particle in the k-th iteration step, corresponding to its parent. Let be the position vector of the i-th particle in the (k+1)-th iteration step, and its corresponding offspring. To obtain the historical best position of the i-th particle in the k-th iteration step, To obtain the historical best position of the entire particle swarm in the k-th iteration step, The individual learning factor represents the degree to which the best position experienced by an individual particle influences the position of the next generation of the population. The population learning factor represents the degree to which the best position experienced by the particle swarm influences the position of the next generation of the population. and is a random variable, representing a value that is randomly and uniformly distributed in the range [0,1]; the offspring population individuals are decoded into the position loop control parameters and system structure parameters of the new optoelectronic device.
[0061] The instruction manual is required because individual particles in the particle swarm are updated by changing their position and velocity. Decoding the updated individual particles in the particle swarm can yield position loop control parameters and system structure parameters.
[0062] Step 5: Input the updated position loop control parameters and system structure parameters into the Simulink model, use the Simulink model to simulate the photoelectric device to perform large-angle rapid turning and positioning simulation, and output the turning and positioning time corresponding to the new generation of particle swarm.
[0063] Step 6: Determine the individual historical best position and global historical best position corresponding to the new generation of particle swarm based on the fitness function. Compare the turn-in time corresponding to the current individual historical best position and global historical best position with the preset variable corresponding to the previous generation of particle swarm, and take the smaller value and assign it to the preset variable again.
[0064] Specifically, based on the shortest arrival time criterion, the optimal position searched so far for a single particle is determined. And the best position found by the entire particle swarm so far , will the current and The corresponding turn-in time is the same as the turn-in time in the preset variable E corresponding to the previous generation particle swarm. , The values are compared, and the smaller value is assigned to the preset variable E to form the preset variable E=[ for the new generation particle swarm.] , ],in, Let be the turn-in time corresponding to the historical best position of the i-th particle obtained in the (k+1)-th iteration step. Let be the turn-in time corresponding to the historical best position of the entire particle swarm obtained in the (k+1)th iteration step, and let be the time corresponding to the next generation particle swarm.
[0065] Step 7: Determine whether the termination condition has been met based on the preset variables before and after the update. If so, end the iteration and take the position loop control parameters and system structure parameters corresponding to the current global historical best position as the optimal parameter values. Otherwise, return to step 4.
[0066] Specifically, the first preset variable corresponding to the new generation of particle swarm is compared with the second preset variable corresponding to the previous generation of particle swarm. If the maximum number of iterations or the first preset variable is reached... and With the second preset variable and If the absolute difference is less than 0.1, then the termination condition is met, expressed as: and Otherwise, return to step 4 and perform a new round of iterative calculations until the termination condition is met again.
[0067] Example 1:
[0068] In the Matlab environment, particle swarm optimization program code was written using m-functions, and a Simulink model of the reaction time of optoelectronic devices was built. The particle population was randomly initialized in the preset search space and decoded into initial parameters K. p K i K d d x d y d z Taking the 90° large-angle turning condition as an example, both existing turning time optimization methods and the method of this invention are used for iterative optimization to finally obtain the corresponding optimal parameter combination. The method of this invention uses minimizing the turning time in place as the optimization objective function.
[0069] The initial and optimal parameter values for the method of this invention are shown in Table 1; the optimization curves for the position loop control parameters and system structure parameters are shown in Table 1. Figure 2 and Figure 3 As shown; the optimization process curve of the optimal individual fitness value (i.e., the minimum turn-in time calculated by the fitness function) is as follows. Figure 4As shown, the method of this invention can stably and quickly search for and obtain the optimal parameter values under preset iterative optimization constraints. With only 10 iterations, the total optimization time is 118 seconds, and the fitness function value shows a continuous and stable decreasing trend, indicating that the optimization process of the design parameters can achieve stable convergence. Furthermore, a comparison is made between the existing turn-off time optimization methods and the method of this invention in a simulation environment, and the results are as follows. Figure 5 As shown, the rotation error of the method of the present invention is stably controlled within the range of 0 to 1.7, while the rotation error of the existing method exceeds 2, reaching a maximum of 2.2, showing obvious fluctuations and instability. Therefore, the method of the present invention is superior to the existing rotation time optimization method in terms of rotation accuracy and system stability.
[0070] Table 1
[0071]
[0072] The above optimization process was repeated to obtain multiple sets of experimental data, and the average time for 90° large-angle turning was calculated based on these data. The results show that the average turning time for existing turning time optimization methods is 185ms, while the average turning time for the present invention is reduced to 130ms. Compared with existing turning time optimization methods, the present invention can significantly shorten the turning time, effectively improving the large-angle turning response speed and control performance of optoelectronic devices while ensuring system stability.
[0073] The steps in this invention can be adjusted, combined, or deleted according to actual needs.
[0074] The units in the device of the present invention can be merged, divided, or reduced according to actual needs.
[0075] In this invention, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; "linking" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of these terms in this invention according to the specific circumstances.
[0076] The shapes of the components in the accompanying drawings are schematic and may differ from their actual shapes. The drawings are only used to illustrate the principles of the present invention and are not intended to limit the present invention.
[0077] Although the invention has been disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and not intended to limit the application of the invention. The scope of protection of the invention is defined by the appended claims and may include various modifications, alterations, and equivalents made to the invention without departing from the scope and spirit of the invention.
Claims
1. A method for optimizing the turning time of shipborne optoelectronic equipment based on particle swarm optimization, characterized in that, The method is implemented by writing particle swarm optimization program code using m-functions in the Matlab environment and building a Simulink model of the reaction time of optoelectronic devices, including: Step 1: Initialize the particle swarm and decode the individual particles in the initial particle swarm into the position loop control parameters and system structure parameters of the optoelectronic device; Step 2: Input the position loop control parameters and system structure parameters into the Simulink model, use the Simulink model to simulate the photoelectric device to perform large-angle rapid turning and positioning simulation, and output the turning time corresponding to the initial particle swarm. Step 3: Select the shortest arrival time criterion as the fitness function of the iterative optimization process. Based on the fitness function, determine the individual historical best position and the global historical best position corresponding to the initial particle swarm, and assign the turn-in time corresponding to the individual historical best position and the global historical best position to the preset variable. Step 4: Based on the currently obtained individual historical best position and global historical best position, update the position loop control parameters and system structure parameters of each particle in the particle swarm. Step 5: Input the updated position loop control parameters and system structure parameters into the Simulink model to obtain the turn-in time corresponding to the new generation of particle swarm. Step 6: Determine the individual historical best position and global historical best position corresponding to the new generation of particle swarm based on the fitness function. Compare the turn-in time corresponding to the individual historical best position and global historical best position with the preset variable corresponding to the previous generation of particle swarm, and take the smaller value and assign it to the preset variable again. Step 7: Determine whether the termination condition has been met based on the preset variables before and after the update. If so, end the iteration and take the position loop control parameters and system structure parameters corresponding to the current global historical best position as the optimal parameter values. Otherwise, return to step 4.
2. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 1, characterized in that, Step 3 specifically includes: The Simulink model outputs the turn-in time for each particle in the population. Based on the shortest turn-in time criterion, it determines the optimal position found by a single particle so far. And the best position found by the entire particle swarm so far ,Will and The corresponding adjustment time is assigned to the preset variable E, E=[ , ],in, Let be the turn-in time corresponding to the historical best position of the i-th particle obtained in the k-th iteration step. Let be the turn-in time corresponding to the historical best position of the entire particle swarm obtained in the k-th iteration step.
3. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 2, characterized in that, Step 4 specifically includes: Using the current particle population as the parent population, the velocity of the offspring population is calculated based on inertia weights, the velocity and position of the parent population individuals, as well as the currently obtained historical best position and the global historical best position. The position of the offspring population is then calculated based on the offspring population velocity and the parent population position, expressed as: ； ； In the formula, For inertial weights, Let be the velocity vector of the i-th particle in the k-th iteration step. Let i be the position vector of the i-th particle in the k-th iteration step. To obtain the historical best position of the i-th particle in the k-th iteration step, To obtain the historical best position of the entire particle swarm in the k-th iteration step, For individual learning factors, As a population learning factor, and It is a random variable.
4. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 3, characterized in that, Step 4 further includes: decoding the offspring population individuals into the new position loop control parameters and system structure parameters of the optoelectronic device.
5. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 3, characterized in that, Step 6 specifically includes: Based on the shortest arrival time criterion, the optimal position searched so far for a single particle is determined. And the best position found by the entire particle swarm so far , will the current and The corresponding turn-in time is the same as the turn-in time in the preset variable E corresponding to the previous generation particle swarm. , The values are compared, and the smaller value is assigned to the preset variable E to form the preset variable E=[ for the new generation particle swarm.] , ].
6. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 5, characterized in that, Step 1, initializing the particle population, specifically includes: Set the population size, inertia weight, learning factor, particle velocity range, maximum number of iterations, and minimum precision value in the iteration termination condition; the position loop control parameters include the proportional parameter K. p Integral parameter K i Differential parameter K d The system structural parameters include the eccentricity d in the x-axis direction. x y-axis eccentricity d y eccentricity d in the z-axis direction z Given the frictional force F, the iterative optimization constraint is set as: K p K i K d The value range of d is [0, 300]. x d y d z The value range of is [0.0001, 0.01], and the value range of F is [0.1, 0.5].
7. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization as described in claim 6, characterized in that, Step 1 further includes: Based on iterative optimization constraints, a particle swarm is randomly initialized in the search space, and the individual particles in the initial swarm are decoded into the position loop control parameters K of the optoelectronic device. p K i K d and system structural parameters d x d y d z 、F.
8. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization algorithm as described in claim 6, characterized in that, Step 7 specifically includes: Compare the first preset variable corresponding to the new generation of particle swarm with the second preset variable corresponding to the previous generation of particle swarm. If the maximum number of iterations or the first preset variable is reached... and With the second preset variable and If the absolute difference is less than 0.1, the termination condition is met; otherwise, return to step 4.
9. The optimization of shipborne optoelectronic equipment turning time based on particle swarm optimization as described in claim 8, characterized in that, The first preset variable and With the second preset variable and The absolute differences are all less than 0.1, expressed as: and .