Solid surface deployable antenna limit envelope design method based on improved particle swarm optimization

Abstract

The invention discloses a solid-surface deployable antenna limit envelope design method based on an improved particle swarm algorithm, and the method comprises the steps: dividing the radius, height and volume indexes related to the storage envelope performance of an antenna into different levels and weights through an analytic hierarchy process according to the structural characteristics of a solid-surface deployable antenna, and building a single target optimization task; constructing an optimization constraint function based on an unfolded state geometric profile constraint, a folded state envelope profile constraint, a rigid body constraint, a single-degree-of-freedom folding and unfolding constraint, a non-interference constraint and a structural parameter boundary constraint, and determining structural parameters which can be used for optimization according to requirements in an antenna design process; on this basis, a cross-platform multi-thread parallel solving process is constructed, algorithm cross iteration is realized through information transmission among different computing platforms, and ultimately, calculation of the limit envelope design of the solid-surface deployable antenna is completed.

Description

Technical Field

[0001] The invention relates to the field of satellite-borne antennas, and in particular to a limit envelope design method for a solid-surface deployable antenna based on an improved particle swarm algorithm. Background Art

[0002] As humans continue to explore the mysteries of space, the development of ultra-large, high-precision satellites and space stations has become increasingly urgent and necessary. As a carrier for receiving signals, satellite-borne antennas play a vital role in wireless communications, deep space exploration, satellite remote sensing, and radio astronomy.

[0003] Due to the space limitations of the carrier, large-caliber space antennas usually adopt a working mode of being folded up on the ground and launched into orbit before being deployed. Therefore, space antennas are usually designed to be deployable structures. Compared with mesh reflector antennas and film reflector antennas, solid reflector antennas have higher surface accuracy and can better meet the performance requirements of high-precision and wide-bandwidth antennas in fields such as space exploration and microwave remote sensing. Figure 2 As shown in the figure, the solid surface deployable antenna is mainly composed of a feed system, a reflector panel, a drive and transmission assembly, a support and connection assembly, etc. The antenna is installed on the satellite through the support and connection assembly, and is stored in the launch vehicle in a folded state. After the satellite is launched into orbit, the reflector panel is deployed to the service posture under the action of the drive and transmission assembly, and cooperates with the feed system to achieve the predetermined service performance. However, there are few related studies on large-caliber solid surface deployable antennas, especially the storage problem that restricts its application, which urgently needs to be studied to provide technical reserves for solid surface deployable antennas. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art, provide a limit envelope design method for a solid surface deployable antenna based on an improved particle swarm algorithm, realize the structural parameter design and limit storage performance evaluation of the solid surface deployable antenna, and solve the problem of improving the storage ratio of the solid surface deployable antenna.

[0005] The technical solution of the present invention is: a limit envelope design method for a solid surface deployable antenna based on an improved particle swarm algorithm, comprising the following steps:

[0006] S1. Multiple indicators related to antenna storage envelope performance are weighted according to task requirements using the analytic hierarchy process, and a single-objective optimization model is constructed to obtain the global optimization target of the ultimate envelope of the solid surface deployable antenna.

[0007] S2. Establish optimization model constraint conditions, including unfolded geometric surface constraint, folded envelope surface constraint, rigid body constraint, single degree of freedom folding and unfolding constraint, non-interference constraint and structural parameter boundary constraint;

[0008] S3. Determine the structural parameters to be solved in the optimization model according to the antenna design requirements;

[0009] S4, initializing the structural parameters, determining the dimension and scale of the particle swarm according to the number of structural parameters, and initializing the parameters of each particle swarm;

[0010] S5. According to the global optimization goal, the fitness of each particle group under the initial parameters is calculated, and the particle speed and spatial position are updated; during the updating process, the inertia coefficient is a non-fixed value;

[0011] S6. Correcting the velocity and spatial position of particles exceeding the boundary conditions;

[0012] S7, constructing a kinematic model of the antenna folding and unfolding process according to the structural parameters corresponding to the current position of the particle, judging whether the established constraint conditions are met through kinematic analysis, and executing S8 when the constraint conditions are met;

[0013] S8, update the particle swarm optimal solution and the global optimal solution;

[0014] S9, repeat S5 to S8 until the set iteration condition is reached, and the global optimal solution finally obtained is the structural parameters corresponding to the limit envelope of the solid surface deployable antenna.

[0015] Further, in step S1, the indicator is the storage diameter D max , Storage height H max And storage volume V max , construct a single objective optimization model as:

[0016] min:ζ(u)=α1D max +α2H max +α3V max

[0017] Wherein, the parameters [α1, α2, α3] are the storage diameter D determined by the analytic hierarchy process. max , Storage height H max And storage volume V max The specific data needs to be selected according to the actual task index requirements; u is the structural parameter to be solved, and ζ() represents the antenna convergence rate.

[0018] Furthermore, in step S2, the specific form of the constraint condition is:

[0019] st:G D (x D y D z D )=0

[0020] G F (xF y F z F )≤0

[0021] δ=0

[0022] DOF = 1

[0023] d min ≥d0

[0024] u min ≤u≤u max

[0025] In the formula, G D is the unfolded antenna profile function, x D ,y D 、z D The coordinates of the expanded panel are set by G D =0 means that the unfolded surface satisfies the unfolded geometric surface constraints;

[0026] G F is the envelope profile function of the collapsed state, x F ,y F 、z F To fold the panel coordinates, set G F ≤0 means that the collapsed state surface satisfies the collapsed state envelope surface constraint;

[0027] δ represents the deformation of the antenna panel. By setting δ = 0, it means that the antenna panel has no deformation and satisfies the rigid body constraint;

[0028] DOF represents the degree of freedom of antenna folding and unfolding. Setting DOF = 1 means that the antenna folding and unfolding process has only one degree of freedom, satisfying the single degree of freedom constraint;

[0029] d min Indicates the minimum distance between adjacent panels during antenna movement. d0 is the distance threshold. min ≥d0 means that the distance between adjacent panels is always greater than the distance threshold during the folding and unfolding of the antenna, satisfying the non-interference constraint;

[0030] u max and u min Indicates the upper and lower limits of the structural parameters to be solved. By setting u min ≤u≤u max It means that the antenna structure parameters are always within the design range and meet the structure parameter boundary constraints.

[0031] Further, in step S3, the structural parameter u includes the unfolded diameter D a , center panel diameter D i, focal length f, number of petals n, driving transmission rod structure parameter L, reflector panel and back frame structure parameter B, feed assembly structure parameter F.

[0032] Furthermore, in step S4, when the parameters of each particle group are initialized, the initial position and initial velocity of each particle group are randomly assigned within the constraint range.

[0033] Further, in step S5, the particle velocity and spatial position are updated in the following manner:

[0034] v i (k+1)=wv i (k)+c1r1(P Best,i (k)-s i (k))+c2r2(G Best (k)-s i (k))

[0035] s i (k+1)=s i (k)+v i (k+1)

[0036] Where v is the particle speed; s is the particle's spatial position; k is the number of iterations; w is the inertia coefficient; c1, c2 are acceleration coefficients; r1, r2 are random numbers; P Best,i is the optimal solution of the particle swarm; G Best is the global optimal solution;

[0037] Among them, the inertia coefficient is a non-fixed value and is involved in the iterative calculation according to the following formula:

[0038]

[0039] In the formula, w max is the maximum inertia coefficient; w min is the minimum inertia coefficient; N is the maximum number of iterations.

[0040] Further, in step S8, the particle swarm optimal solution and the global optimal solution are updated in the following manner:

[0041] P Best,i (k+1)=min(P Best,i (k),s i (k+1))

[0042] G Best (k+1)=min(P Best,i (k+1)) i＝1.2....

[0043] In the formula, i is the number of particle swarms, and the specific value is selected by comprehensively considering the calculation accuracy and platform computing power.

[0044] The present invention also provides a limit envelope design system for a solid surface deployable antenna based on an improved particle swarm algorithm, comprising:

[0045] The main control module is used to execute S1: weight multiple indicators related to the antenna storage envelope performance, use the hierarchical analysis method according to the task requirements, build a single-objective optimization model, and obtain the global optimization target of the limit envelope of the solid surface deployable antenna; S2: establish the optimization model constraint conditions, including the unfolded state geometric surface constraint, the folded state envelope surface constraint, the rigid body constraint, the single degree of freedom folding constraint, the non-interference constraint and the structural parameter boundary constraint; S3: determine the structural parameters to be solved in the optimization model according to the antenna design requirements; after the execution of step S3, call the particle swarm optimization module; according to the feedback of the particle swarm optimization module, execute S9: repeat S5 to S8 until the set iteration conditions are met, and the global optimal solution finally obtained is the structural parameters corresponding to the limit envelope of the solid surface deployable antenna, until the end;

[0046] The particle swarm optimization module is used to execute S4: initialize the structural parameters, determine the dimension and scale of the particle swarm according to the number of structural parameters, and initialize the parameters of each particle swarm; S5: calculate the fitness of each particle swarm under the initial parameters according to the global optimization goal, and update the particle speed and spatial position. During the updating process, the inertia coefficient is a non-fixed value; S6: correct the speed and spatial position of particles that exceed the boundary conditions; after the execution of step S6, call the kinematics module; according to the feedback of the kinematics module, execute S8: update the optimal solution of the particle swarm and the global optimal solution; after the execution of step S8, return to the main control module;

[0047] The kinematics module is used to execute S7: construct a kinematic model of the antenna folding and unfolding process according to the structural parameters corresponding to the current position of the particle, and determine whether the established constraints are met through kinematic analysis. When the constraints are met, execute S8; after step S7 is executed, return to the particle swarm optimization module.

[0048] Furthermore, the main control module and particle swarm optimization module are implemented based on the Python platform, using multi-threaded parallel computing to achieve the parallel motion of multiple groups of particles; the kinematics module is implemented based on the ADAMS platform to complete the kinematic analysis.

[0049] The present invention also provides a computer program product, which implements the steps of any of the methods described above when executed by a processor.

[0050] The advantages of the present invention compared with the prior art are:

[0051] 1) The present invention proposes an extreme envelope design method for solid surface deployable antennas based on an improved particle swarm algorithm, which can realize the extreme storage performance evaluation and structural parameter design of the antenna.

[0052] 2) The present invention proposes a cross-platform multi-threaded optimization algorithm architecture, which can solve the problem of low serial computing efficiency in the limit envelope calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a limit envelope design method of a solid surface deployable antenna based on an improved particle swarm algorithm of the present invention;

[0054] Figure 2 It is a schematic diagram of the structure of the solid-surface deployable antenna involved in the present invention. DETAILED DESCRIPTION

[0055] In order to better understand the technical solution of the present invention, the specific implementation mode of the present invention is described in detail below with reference to the accompanying drawings.

[0056] Reference Figure 1 The present invention provides a limit envelope design method for a solid surface deployable antenna based on an improved particle swarm algorithm, and the specific steps are as follows:

[0057] Step 1: Determine the global optimization target of the limit envelope of the solid surface deployable antenna. Use the analytic hierarchy process to divide multiple indicators related to the antenna storage envelope performance into different levels and weights, and then establish a single target optimization task; for the antenna storage envelope, the key indicator is the storage diameter D max , Storage height H max 、Storage volume V max , weight planning is performed on the three indicators according to different task requirements, and a single-objective optimization model is constructed. Its mathematical expression is as follows:

[0058] min:ζ(u)=α1D max +α2H max +α3V max (1)

[0059] Wherein, the parameters [α1, α2, α3] are the weight vectors of the antenna envelope index determined by the hierarchical analysis method. The specific data needs to be selected according to the actual task index requirements; u is the structural parameter that affects the antenna storage envelope and is also the solution of the objective function.

[0060] Step 2: Establish optimization model constraints. For the solid surface deployable antenna described in this patent, the constraints are geometric surface constraints in the deployed state, envelope surface constraints in the folded state, rigid body constraints, single degree of freedom folding and unfolding constraints, non-interference constraints, and structural parameter boundary constraints.

[0061]

[0062] In the formula, G D is the unfolded antenna profile function, x D ,y D 、z D The coordinates of the expanded panel are set by G D =0 means that the unfolded surface meets the preset, that is, meets the unfolded geometric surface constraints, such as Represents parabolic surface constraint;

[0063] G F is the envelope profile function of the collapsed state, x F ,y F 、z F To fold the panel coordinates, set G F ≤0 means that the collapsed state profile is within the preset envelope, that is, the collapsed state envelope profile constraint is satisfied, such as and They represent cylindrical envelope and conical envelope respectively;

[0064] δ represents the deformation of the antenna panel. Setting δ = 0 means that the antenna panel has no deformation, that is, it satisfies the rigid body constraint. DOF represents the degree of freedom of the antenna folding and unfolding. Setting DOF = 1 means that the antenna folding and unfolding process has only one degree of freedom, that is, it satisfies the single degree of freedom constraint.

[0065] d min Indicates the minimum distance between adjacent panels during antenna movement. d0 is the distance threshold. min ≥d0 means that the distance between adjacent panels is always greater than the distance threshold during the folding and unfolding of the antenna, that is, the non-interference constraint is satisfied;

[0066] u max and u min Indicates the upper and lower limits of the structural parameters to be solved. By setting u min ≤u≤u max It means that the antenna structure parameters are always within the design range, that is, the structure parameter boundary constraints are met.

[0067] Step 3: Determine the structural parameters to be solved. According to the requirements in the antenna design process, determine the structural parameters that can be used for optimization.

[0068] u(D a D i fn LBF)(3)In the formula, D a is the unfolded diameter, D i is the center panel diameter, f is the focal length, n is the number of petals, L is the structural parameter of the driving transmission rod, B is the structural parameter of the reflector panel and back frame component, and F is the structural parameter of the feed assembly.

[0069] Step 4: Initialize the particle swarm parameters. According to the task requirements, initialize the antenna structure parameters involved in step 3, determine the dimension and scale of the particle swarm according to the number of structure parameters, and assign the initial positions and initial velocities of various swarms randomly within the constraints.

[0070] Step 5: Start the particle swarm search process. According to the global optimization goal, calculate the fitness of various swarms under given initial parameters, and update the spatial position based on the calculation results.

[0071]

[0072] Where v is the particle speed; s is the particle's spatial position; k is the number of iterations; w is the inertia coefficient; c1, c2 are acceleration coefficients; r1, r2 are random numbers; P Best,i is the optimal solution for the population; G Best It is the global optimal solution.

[0073]

[0074] In the formula, w max is the maximum inertia coefficient; w min is the minimum inertia coefficient; k is the number of iterations; N is the maximum number of iterations.

[0075] Step 6: Particle velocity and position correction: Determine the velocity and spatial position of the particles, and correct the particles that exceed the boundary conditions.

[0076]

[0077] In the formula, [v min , v max ] and [s min ,s max ] are the boundaries of particle velocity and spatial position respectively.

[0078] Step 7, constraint determination. A kinematic model of the antenna folding and unfolding process is constructed based on the structural parameters corresponding to the particle positions. Kinematic analysis is used to determine whether the constraints are met. If the constraints are met, step 8 is executed. If the constraints are not met, the optimal solution of the population is not updated.

[0079] Step 8: Update the optimal solution. Update the population optimal solution and the global optimal solution based on the fitness function calculated from the particle position.

[0080]

[0081] Step 9: Iterate. Repeat steps 5 to 8 according to the set number of iterations. The final G bestIt is the global optimal solution, that is, the structural parameters corresponding to the limit envelope of the solid surface deployable antenna.

[0082] In one possible implementation, the aforementioned method is implemented by building a cross-platform multi-threaded optimization architecture. For the object of the present invention, kinematic analysis will be involved when determining the constraints, and the amount of calculation brought by the kinematic analysis itself will even exceed the optimization process. In the optimization process of the particle swarm optimization algorithm, various groups only exchange information after each optimization, and are independent of each other in the optimization process. Therefore, in order to solve the problem of low efficiency of serial calculations, the present invention builds a cross-platform multi-threaded optimization architecture to improve calculation efficiency. The specific implementation method is: using Python program as the main control program of the particle swarm algorithm, the parallel motion of multiple groups of particles can be realized; secondary program development is carried out based on the ADAMS platform for kinematic analysis, and the kinematic analysis is used to realize the determination of constraints such as single degree of freedom, geometric surface, and no interference.

[0083] It is to be understood that the present invention is described by way of embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and embodiments that can fall within the scope of the claims of this application all fall within the scope protected by the present invention.

[0084] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A limit envelope design method for solid surface deployable antenna based on improved particle swarm algorithm, characterized in that: The steps include: S1. Multiple indicators related to antenna storage envelope performance are weighted according to task requirements using the analytic hierarchy process, and a single-objective optimization model is constructed to obtain the global optimization target of the ultimate envelope of the solid surface deployable antenna. S2. Establish optimization model constraint conditions, including unfolded geometric surface constraint, folded envelope surface constraint, rigid body constraint, single degree of freedom folding and unfolding constraint, non-interference constraint and structural parameter boundary constraint; S3. Determine the structural parameters to be solved in the optimization model according to the antenna design requirements; S4, initializing the structural parameters, determining the dimension and scale of the particle swarm according to the number of structural parameters, and initializing the parameters of each particle swarm; S5. According to the global optimization goal, the fitness of each particle group under the initial parameters is calculated, and the particle speed and spatial position are updated; during the updating process, the inertia coefficient is a non-fixed value; S6. Correcting the velocity and spatial position of particles exceeding the boundary conditions; S7, constructing a kinematic model of the antenna folding and unfolding process according to the structural parameters corresponding to the current position of the particle, judging whether the established constraint conditions are met through kinematic analysis, and executing S8 when the constraint conditions are met; S8, update the particle swarm optimal solution and the global optimal solution; S9, repeat S5 to S8 until the set iteration condition is reached, and the global optimal solution finally obtained is the structural parameters corresponding to the limit envelope of the solid surface deployable antenna.

2. The limit envelope design method for solid surface deployable antenna based on improved particle swarm algorithm according to claim 1, characterized in that: In step S1, the index is the storage diameter D max , Storage height H max And storage volume V max , construct a single objective optimization model as: min:ζ(u)=α1D max +α2H max +α3V max Wherein, the parameters [α1, α2, α3] are the storage diameter D determined by the analytic hierarchy process. max , Storage height H max And storage volume V max The specific data needs to be selected according to the actual task index requirements; u is the structural parameter to be solved, and ζ() represents the antenna convergence rate.

3. The limit envelope design method for solid surface deployable antenna based on improved particle swarm algorithm according to claim 2, characterized in that: In step S2, the specific form of the constraint condition is: s.t.:G D (x D y D z D )=0 G F (x F y F z F )≤0 δ=0 DOF = 1 d min ≥d0 in min Oh, oh. max In the formula, G D is the unfolded antenna profile function, x D ,y D 、z D The coordinates of the expanded panel are set by G D =0 means that the unfolded surface satisfies the unfolded geometric surface constraints; G F is the envelope profile function of the collapsed state, x F ,y F 、z F To fold the panel coordinates, set G F ≤0 means that the collapsed state surface satisfies the collapsed state envelope surface constraint; δ represents the deformation of the antenna panel. By setting δ = 0, it means that the antenna panel has no deformation and satisfies the rigid body constraint; DOF represents the degree of freedom of the antenna folding and unfolding. Setting DOF = 1 means that the antenna folding and unfolding process has only one degree of freedom, satisfying the single degree of freedom constraint; d min Indicates the minimum distance between adjacent panels during antenna movement. d0 is the distance threshold. min ≥d0 means that the distance between adjacent panels is always greater than the distance threshold during the folding and unfolding of the antenna, satisfying the non-interference constraint; u max and u min Indicates the upper and lower limits of the structural parameters to be solved. By setting u min ≤u≤u max It means that the antenna structure parameters are always within the design range and meet the structure parameter boundary constraints.

4. The method for designing the limit envelope of a solid surface deployable antenna based on an improved particle swarm algorithm according to claim 2, characterized in that: In step S3, the structural parameter u includes the unfolded diameter D a , center panel diameter D i , focal length f, number of petals n, driving transmission rod structure parameter L, reflector panel and back frame structure parameter B, feed assembly structure parameter F.

5. The limit envelope design method for solid surface deployable antenna based on improved particle swarm algorithm according to claim 1, characterized in that: In step S4, when the parameters of each particle group are initialized, the initial position and initial velocity of each particle group are randomly assigned within the constraint range.

6. The limit envelope design method for solid surface deployable antenna based on improved particle swarm algorithm according to claim 1, characterized in that: In step S5, the particle velocity and spatial position are updated in the following manner: v i (k+1)=wv i (k)+c1r1(P Best,i (k)-s i (k))+c2r2(G Best (k)-s i (k)) with i (k+1)=s i (k)+v i (k+1) Where v is the particle speed; s is the particle's spatial position; k is the number of iterations; w is the inertia coefficient; c1, c2 are acceleration coefficients; r1, r2 are random numbers; P Best,i is the optimal solution of the particle swarm; G Best is the global optimal solution; Among them, the inertia coefficient is a non-fixed value and is involved in the iterative calculation according to the following formula: In the formula, w max is the maximum inertia coefficient; w min is the minimum inertia coefficient; N is the maximum number of iterations.

7. The method for designing the limit envelope of a solid surface deployable antenna based on an improved particle swarm algorithm according to claim 6, characterized in that: In step S8, the particle swarm optimal solution and the global optimal solution are updated in the following manner: P Best,i (k+1)=min(P Best,i (k),s i (k+1)) G Best (k+1)=min(P Best,i (k+1)) i＝1.2.... In the formula, i is the number of particle swarms, and the specific value is selected by comprehensively considering the calculation accuracy and platform computing power.

8. A limit envelope design system for solid surface deployable antenna based on improved particle swarm algorithm, characterized in that: include: The main control module is used to execute S1: weight multiple indicators related to the antenna storage envelope performance, use the hierarchical analysis method according to the task requirements, build a single-objective optimization model, and obtain the global optimization target of the limit envelope of the solid surface deployable antenna; S2: Establish optimization model constraints, including unfolded geometric surface constraints, folded envelope surface constraints, rigid body constraints, single-degree-of-freedom folding constraints, non-interference constraints and structural parameter boundary constraints; S3: Determine the structural parameters to be solved in the optimization model according to the antenna design requirements; After step S3 is executed, call the particle swarm optimization module; According to the feedback of the particle swarm optimization module, execute S9: Repeat S5 to S8 until the set iteration conditions are met, and finally obtain the global optimal solution, which is the structural parameters corresponding to the limit envelope of the solid surface deployable antenna, until the end; The particle swarm optimization module is used to execute S4: initialize the structural parameters, determine the dimension and scale of the particle swarm according to the number of structural parameters, and initialize the parameters of each particle swarm; S5: calculate the fitness of each particle swarm under the initial parameters according to the global optimization goal, and update the particle speed and spatial position. During the updating process, the inertia coefficient is a non-fixed value; S6: correct the speed and spatial position of particles that exceed the boundary conditions; after the execution of step S6, call the kinematics module; according to the feedback of the kinematics module, execute S8: update the optimal solution of the particle swarm and the global optimal solution; after the execution of step S8, return to the main control module; The kinematics module is used to execute S7: construct a kinematic model of the antenna folding and unfolding process according to the structural parameters corresponding to the current position of the particle, and determine whether the established constraints are met through kinematic analysis. When the constraints are met, execute S8; after step S7 is executed, return to the particle swarm optimization module.

9. The design system according to claim 8, characterized in that: The main control module and particle swarm optimization module are implemented based on the Python platform and use multi-threaded parallel computing to achieve parallel motion of multiple groups of particles; the kinematics module is implemented based on the ADAMS platform to complete kinematic analysis.

10. A computer program product, characterized in that: When the computer program product is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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