Method for optimizing and controlling vertical cable pretension of a mesh antenna and related product

Abstract

This invention discloses an optimization and control method for the pretension force of the vertical cables in a mesh antenna, and related products, belonging to the field of mesh antenna control technology. This method establishes a dynamic simulation model of the mesh antenna deployment process by constructing a co-simulation project on a simulation platform. The pretension force of the vertical cables is set as the independent variable, and sample data of multiple assembly performance indicators of the mesh antenna under different pretension force values ​​are generated through parameter correlation analysis. A regression model is trained based on the sample data, and a surrogate model is constructed from the pretension force of the vertical cables to multiple assembly performance indicators. Finally, a multi-objective optimization function is constructed and solved through the surrogate model to obtain the optimal pretension force configuration that can simultaneously optimize multiple assembly performance indicators. By optimizing multiple assembly performance indicators, this method can achieve efficient and precise control of the pretension force of the vertical cables, solving the problem of poor control accuracy of the vertical cable pretension force in existing technologies that only consider a single indicator.

Description

Technical Field

[0001] This invention relates to the field of mesh antenna control technology, specifically to an optimized control method for the preload of the vertical cable of a mesh antenna and related products. Background Technology

[0002] Large-scale deployable mesh-like annular truss antennas are a type of large-scale spatial deployable antenna mechanism composed of a deployable annular truss and a cable net, often simply called a mesh antenna. The truss section is composed of repeating quadrilateral units, with members hinged at the vertices. Thick and thin diagonal rods slide against each other, and the antenna's orderly deployment is achieved through the coordination of cables and synchronous gears. This type of antenna has advantages such as a large unfolding ratio, simple structure, and small mass, and has gradually become a focus of satellite antenna research, showing potential for development into ultra-large antennas. See also Figure 5 The mesh antenna consists of a front mesh, a rear mesh, a middle cable array, and a metal reflector attached to the front mesh. The surface accuracy of the metal reflector is controlled by the pre-tension of the cable array, which directly affects the electromagnetic performance of the antenna.

[0003] Currently, traditional methods for controlling the preload of vertical cable arrays rely heavily on the experience of engineers, involving repeated measurements and manual adjustments of cable force for fine-tuning of the surface, a cumbersome and inefficient process. While existing research has proposed improving adjustment efficiency through algorithm optimization, most studies only focus on a single indicator of surface accuracy, resulting in poor control effects.

[0004] Therefore, how to achieve efficient and precise control of the preload of vertical cables has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized control method for the pretension of the vertical cable of a mesh antenna and related products, so as to overcome the problem that the control accuracy of the pretension of the vertical cable is not good because only a single index is considered in the prior art.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution: This invention provides a method for optimizing and controlling the preload of the vertical cable in a mesh antenna, comprising the following steps: Establish interconnected transient structure analysis modules, static structure analysis modules, and modal analysis modules on the simulation platform to form a joint simulation project; In the joint simulation project, a dynamic simulation model of the deployment process of the mesh antenna is established by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. By setting the pretension of the vertical cable in the dynamic simulation model as the independent variable, and through parameter correlation analysis, sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable are generated. Based on the sample data, a regression model is trained. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

[0007] A further improvement of the present invention is that the connection relationship is configured as follows: the truss and the cable net are connected by bushing pairs, and the cable segments are connected at the cable net nodes by a degree-of-freedom coupling method.

[0008] A further improvement of the present invention is that the boundary conditions are configured as follows: in the transient structure analysis module, the mesh antenna is retracted and extended by remote displacement control, and gravity boundary conditions are added; in the static structure analysis module, fixed supports are added to constrain the truss movement.

[0009] A further improvement of the present invention is that the multiple assembly performance indicators include at least the peak value of the sudden change in driving torque during the deployment of the mesh antenna, the first-order natural frequency of the mesh antenna after deployment, and the surface accuracy of the mesh antenna reflector.

[0010] A further improvement of this invention lies in that, based on sample data, the training of the regression model specifically involves: The ridge regression model was trained based on sample data of the sudden peak of the driving torque during the deployment of the mesh antenna. A support vector machine model is trained based on sample data of the first-order natural frequency of the mesh antenna after it is deployed and the surface accuracy of the mesh antenna reflector.

[0011] A further improvement of this invention is that the simulation platform is Ansys Workbench.

[0012] This invention also provides an optimization and control system for the preload of the vertical cable of a mesh antenna, comprising: The first module is used to establish interrelated transient structure analysis modules, static structure analysis modules and modal analysis modules on the simulation platform to form a joint simulation project; The second module is used in joint simulation projects to build a dynamic simulation model of the deployment process of the mesh antenna by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. The third module is used to set the pretension of the vertical cable in the dynamic simulation model as an independent variable, and generate sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable through parameter correlation analysis. The fourth module is used to train a regression model based on sample data. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for optimizing and controlling the pretension of the vertical cable of a mesh antenna.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for optimizing and controlling the preload of the vertical cable of a mesh antenna.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for optimizing and controlling the preload of the vertical cable of a mesh antenna.

[0016] Compared with the prior art, the positive and progressive effects of the present invention are as follows: The present invention provides an optimized control method for the pretension of the vertical cable in a mesh antenna. By building a multi-module joint simulation project, it realizes the full-process simulation of the mesh antenna from deployment to locking performance. The pretension of the vertical cable is parameterized, and sufficient sample data is generated through parameter sampling and simulation calculation. A high-precision surrogate model is trained based on the sample data to replace the time-consuming simulation iteration calculation. Then, a multi-objective optimization algorithm is used to solve the problem, realizing the coordinated optimization of multiple assembly performance indicators, and finally obtaining the optimal pretension configuration that takes into account multiple dimensions of performance. This completely eliminates the dependence on manual experience in traditional pretension control, significantly shortens the pretension control cycle, and improves control efficiency. At the same time, it breaks through the limitations of single-index optimization, and can simultaneously take into account multiple key performances such as antenna deployment stability, structural dynamic characteristics, and surface accuracy. It realizes efficient, accurate, and multi-objective coordinated optimization control of the vertical cable pretension, and comprehensively improves the assembly performance and operational reliability of the mesh antenna. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is a flowchart illustrating the optimized control method for the preload of the vertical cable of the mesh antenna according to the present invention. Figure 2 This is a schematic diagram of the simulation process of the present invention; Figure 3 This is a schematic diagram of the multi-objective optimization process of the present invention; Figure 4 This is a schematic diagram of the simulation platform; Figure 5 This is a schematic diagram of a mesh antenna structure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0023] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This is an explanation of the present invention and not a limitation thereof.

[0025] See Figure 5 The deployable ring truss antenna (mesh antenna) consists of a deployable truss and a cable net array. The deployable truss is composed of several identical deployable unit cells connected end to end. Each deployable unit cell includes an upper horizontal bar, a lower horizontal bar, adjacent vertical bars, and thick and thin diagonal bars arranged in a cross-diagonal pattern to form a parallelogram structure. Gears ensure the synchronization of adjacent unit cells, and pulley cables drive the truss deployment. The cable net consists of three layers and adopts a symmetrical structure. The internal nodes of the cable net on one side are arranged in a hierarchical hexagonal grid based on equilateral triangles. The boundary nodes are evenly distributed on the truss hinges along the outer circumference of the antenna. The vertical cables of the middle layer axially connect the internal nodes of the front cable net to the internal nodes of the rear cable net one by one. To address the problems in existing technologies, the control of the preload of the vertical cables in mesh antennas often relies on manual experience, which is inefficient. Furthermore, existing optimization methods often only focus on a single assembly performance index, resulting in poor control effects. See [reference needed]. Figure 1 This invention provides a method for optimizing and controlling the preload of the vertical cable in a mesh antenna, comprising the following steps: Establish interconnected transient structure analysis modules, static structure analysis modules, and modal analysis modules on the simulation platform to form a joint simulation project; In the joint simulation project, a dynamic simulation model of the deployment process of the mesh antenna is established by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. By setting the pretension of the vertical cable in the dynamic simulation model as the independent variable, and through parameter correlation analysis, sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable are generated. Based on the sample data, a regression model is trained. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

[0026] This method constructs a multi-module joint simulation project to simulate the entire process of a mesh antenna from deployment to post-locking performance. It parameterizes the pretension force of the vertical cables, generates sufficient sample data through parameter sampling and simulation calculations, trains a high-precision surrogate model based on this sample data to replace time-consuming iterative simulation calculations, and then solves the problem using a multi-objective optimization algorithm to achieve synergistic optimization of multiple assembly performance indicators, ultimately obtaining the optimal pretension configuration that takes into account multi-dimensional performance. This invention completely eliminates the reliance on manual experience in traditional pretension control, significantly shortens the pretension control cycle, and improves control efficiency. Simultaneously, it overcomes the limitations of single-indicator optimization, simultaneously considering multiple key performance aspects such as antenna deployment stability, structural dynamics, and surface accuracy. This achieves efficient, precise, and multi-objective synergistic optimization control of the vertical cable pretension force, comprehensively improving the assembly performance and operational reliability of the mesh antenna.

[0027] Specifically, the connection configuration is as follows: the truss and the cable net are connected by bushing pairs, and the cable segments are connected at the cable net nodes by degree-of-freedom coupling.

[0028] By using bushing connections to recreate the flexible connection characteristics between the truss and the cable net, the mechanical transmission laws of the actual assembled structure are matched. Through degree-of-freedom coupling, the motion coordination of each cable segment at the cable net nodes is achieved, accurately simulating the overall stress and deformation characteristics of the cable net. This ensures that the connection relationships in the dynamic simulation model are completely consistent with the actual assembly state of the antenna. This implementation method improves the calculation accuracy of the dynamic simulation model, ensures the consistency between the simulation results and the actual working conditions of the antenna, provides a reliable model foundation for the subsequent generation of pre-tension sample data, and avoids deviations in optimization results caused by simulation distortions in the connection relationships.

[0029] Specifically, the boundary conditions are configured as follows: in the transient structure analysis module, the mesh antenna is retracted and extended by remote displacement control, and gravity boundary conditions are added; in the static structure analysis module, fixed supports are added to constrain the truss motion.

[0030] In the transient structure analysis module, the antenna deployment process is precisely and controllably simulated through remote displacement, and the actual stress environment of ground debugging is restored through gravity boundary conditions. In the static structure analysis module, the installation constraint state after antenna deployment and locking is restored through fixed support constraints, matching the simulation targets of the two analysis modules with the actual working conditions. This implementation method achieves accurate simulation of the antenna deployment process and its static performance after deployment in different scenarios, ensuring that the simulation boundary conditions are completely matched with the actual working and debugging conditions of the antenna, further improving the accuracy of the simulation data and providing reliable input data for preload optimization.

[0031] Specifically, multiple assembly performance indicators include at least the peak value of the sudden change in driving torque during the deployment of the mesh antenna, the first-order natural frequency of the mesh antenna after deployment, and the surface accuracy of the mesh antenna reflector.

[0032] The three performance indicators correspond to the three core dimensions of antenna deployment smoothness, post-deployment structural dynamic stability, and antenna electromagnetic performance. By simultaneously optimizing all three indicators, multi-dimensional synergistic optimization of the vertical cable pretension can be achieved, ensuring that the optimized pretension configuration fully meets the antenna's operational requirements. This implementation method overcomes the limitations of traditional single-indicator optimization, simultaneously covering the key performance aspects of the three core application scenarios: antenna deployment process, post-deployment structural characteristics, and electromagnetic performance. This allows the optimized pretension configuration to simultaneously achieve multiple effects, including reducing deployment impact, increasing structural stiffness, and ensuring surface accuracy, thus comprehensively improving the overall performance of the mesh antenna.

[0033] Specifically, based on the sample data, the training of the regression model is as follows: The ridge regression model was trained based on sample data of the sudden peak of the driving torque during the deployment of the mesh antenna. A support vector machine model is trained based on sample data of the first-order natural frequency of the mesh antenna after it is deployed and the surface accuracy of the mesh antenna reflector.

[0034] To address the varying correlation characteristics between different performance indicators and preload, a suitable regression algorithm is matched. For the abrupt peak of the driving torque with strong linear characteristics, a ridge regression model is used to ensure fitting efficiency and anti-overfitting ability. For the natural frequency and surface accuracy with strong nonlinear characteristics, a support vector machine model is used to ensure fitting accuracy. The final multi-model surrogate model can simultaneously balance computational efficiency and prediction accuracy. This implementation improves the prediction accuracy of the surrogate model for various assembly performance indicators, reduces model prediction errors, and ensures the computational efficiency of the surrogate model. It can replace time-consuming finite element simulation iterations, significantly shorten the calculation cycle of multi-objective optimization, and further improve the efficiency of preload control.

[0035] Specifically, the simulation platform is Ansys Workbench.

[0036] Currently, common simulation methods for ring truss antennas are based on Adams, which uses the absolute node coordinate method to build a flexible cable net for dynamic analysis. This method is slow to build the cable net model, difficult to parameterize, requires the use of other software to achieve flexibility of rigid components, and is cumbersome in post-processing. This application uses Ansys Workbench software. Ansys Workbench is a multiphysics co-simulation platform that enables the visual construction and parameterization of transient structural analysis, static structural analysis, and modal analysis modules. Modules are linked in real-time via engineering data links to transfer parameters and calculation results, eliminating the need for manual data import / export and enabling one-click iterative simulation calculation of preload parameters. The platform has a built-in complete structural element library, material library, and solver, enabling accurate mechanical simulation calculations of cable nets and truss structures. It also supports joint calls with programming languages ​​such as Python, enabling fully automated execution of the entire process, from automatic sample data generation and regression model training to multi-objective optimization solutions. In practical applications, other finite element analysis platforms with multi-module co-simulation capabilities can also be selected; this application does not limit this choice.

[0037] By building a co-simulation project using the Ansys Workbench platform, seamless integration and automatic data transfer across multiple analysis modules are achieved. This provides stable platform support for the construction of dynamic simulation models and the automated generation of sample data. Furthermore, through joint calls with programming languages, the entire preload optimization process is automated. This implementation method reduces the difficulty of building co-simulation projects, ensures the stability and accuracy of simulation data transfer, and enables the automated execution of the entire preload optimization process without repeated manual intervention, further improving the efficiency and convenience of preload control.

[0038] Based on the same inventive concept, this invention also provides an optimization and control system for the preload of the vertical cable of a mesh antenna, comprising: The first module is used to establish interrelated transient structure analysis modules, static structure analysis modules and modal analysis modules on the simulation platform to form a joint simulation project; The second module is used in joint simulation projects to build a dynamic simulation model of the deployment process of the mesh antenna by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. The third module is used to set the pretension of the vertical cable in the dynamic simulation model as an independent variable, and generate sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable through parameter correlation analysis. The fourth module is used to train a regression model based on sample data. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

[0039] This system achieves integrated execution of the entire process of optimizing and controlling the pretension of the vertical cable of the mesh antenna through the sequential connection and data exchange of four functional modules. From the construction of the joint simulation project and the dynamic simulation model to the automatic generation of sample data and multi-objective optimization solution, the entire process can automatically complete the optimization configuration calculation of the pretension without repeated manual intervention. It realizes the integrated and automated execution of the entire process of pretension optimization and control, completely solving the problems of fragmented operation and cumbersome operation of traditional control, significantly reducing the operation threshold of pretension control, while ensuring the accuracy of optimization results and multi-objective synergy, and comprehensively improving the control efficiency and effect of the pretension of the vertical cable of the mesh antenna.

[0040] See Figure 2 and Figure 3 An optimized control method for the preload of the vertical cable of a mesh antenna, specifically including the following steps: Step 1: Create an analysis project and set up the project data: 1. Create an analysis project: Launch Ansys Workbench software, select the transient structure module, static structure module, and modal module, and drag them to the project interface. Connect the three to form the project schematic. See [link to documentation]. Figure 4 .

[0041] 2. Set engineering data: Configure the cable net material properties, including material density, Young's modulus, and Poisson's ratio.

[0042] Step 2: Establish a simulation model: 1. Truss structure model: Import the truss solid structure model in .step format.

[0043] 2. Model Refinement: Simplify and refine the model in SpaceClaim, and merge parts, etc.

[0044] 3. Cable Net Model: Construct a parametric cable net line model in DesignModeler.

[0045] 4. Material parameters and unit types: Assign material parameters and unit types to each component in Mechanical.

[0046] Step 3: Set up connection relationships: 1. Truss section: It is equipped with fixed joints, rotating joints, and cylindrical joints for connection.

[0047] 2. Between the truss and the cable net: connected by bushing pairs.

[0048] Step 4: Transient Structure Analysis Settings: 1. Analysis Step: A total of six sub-steps are planned, with time divisions and sub-step lengths set.

[0049] 2. Weak spring and non-linear settings: Turn off the weak spring and adjust the non-linear settings as needed.

[0050] Step 5: Static Structure Analysis Settings 1. Analysis step: There is one sub-step, which sets the time division and sub-step length.

[0051] 2. Weak Spring and Nonlinear Setting: Turn on the weak spring and adjust the nonlinear setting.

[0052] Step 6, Modal Analysis Settings: Set the modal order.

[0053] Step 7: Transient structure boundary conditions: 1. Connection between cable nets: Connect the cable segments at the nodes of the cable net by coupling boundary conditions.

[0054] 2. Gravity: Add standard Earth gravity boundary conditions.

[0055] 3. Drive: The antenna extension and retraction are controlled by remote displacement.

[0056] Step 8: Static structural boundary conditions: 1. Connection between cable nets: Connect the cable segments at the nodes of the cable net by coupling boundary conditions.

[0057] 2. Gravity: Add standard Earth gravity boundary conditions.

[0058] 3. Degrees of freedom constraints: Set boundary conditions to constrain the truss motion.

[0059] Step 9: Modal prestress setting: Use the stress results of the static structural module as the prestress for modal analysis.

[0060] Step 10, Solve: Select the distributed option, set the number of computer cores, and start the solver.

[0061] Step 11: Post-processing 1. Total Deformation: Retrieve total deformation results and export motion video files.

[0062] 2. Torque: The torque response term of the inserted probe is retrieved, and the driving torque is obtained from the search results.

[0063] 3. First-order natural frequencies: Insert the total deformation results, retrieve the results, and obtain the first-order mode shape and natural frequencies.

[0064] Step 12: Parameter Correlation Analysis 1. Parameterization: Parameterize the maximum value and first-order natural frequency of the torque response result that is close to the unfolded position in the post-processing.

[0065] 2. Parameter Correlation Analysis Settings: Drag the Parameters Correlation module from the Design Exploration list in the right toolbar of the project schematic interface to the parameter set, and set the range of the vertical cable prestressing independent variable and the number of samples.

[0066] 3. Solve the parameter correlation analysis: Update the solution results of the design points.

[0067] 4. Obtain sample data: Sample data of the peak value of the driving torque change and the first natural frequency under the influence of the pretension of the vertical cable can be obtained by parameter correlation analysis. Sample data of surface accuracy need to be obtained by extracting the displacement of the cable net node and calculating it according to the RMS formula.

[0068] Step 13: Regression Model Training 1. Model Training: The regression model is trained using sample data as training data for machine learning. The training set and test set are divided, and regression models with three optimization objectives are trained. The peak data of the driving torque mutation is trained using the ridge regression model, and the data of the first-order natural frequency and the surface accuracy are trained using the support vector machine model.

[0069] 2. Model Validation: Input the predicted vertical cable tension into the parameter set of the parameter correlation optimization and solve it. Compare the obtained target value with the predicted target value to verify whether the model is accurate and reliable.

[0070] Step Fourteen: Multi-objective optimization: 1. Multi-objective optimization model: A multi-objective optimization model is constructed using a trained regression model. The optimization results of the three objectives are normalized, and a weighted average is used to control the optimization proportion of each objective and to calculate the integrated optimization index of the three objectives. The constraint condition is the range of values ​​for the vertical cable preload. The multi-objective optimization function is defined as follows:

[0071] in,

[0072] The independent variable takes values ​​within the range of the maximum and minimum values ​​of the sample, and the boundary constraints are...

[0073] Weight vector α satisfy α j >0, and .

[0074] Iterative optimization: By setting the number of iterations and continuously optimizing to obtain the minimum weighted value, the optimal values ​​of the three optimization objectives and the corresponding vertical cable pretension under the given weighted value can be obtained.

[0075] This invention constructs a parameter correlation analysis based on simulation analysis results and performs multi-objective optimization through machine learning to obtain the optimal solutions for the peak value of the antenna deployment driving torque, the first-order natural frequency, and the surface accuracy under the influence of the vertical cable pretension. This solves the problem that the adjustment of the vertical cable force in antenna assembly is limited to surface accuracy and cannot comprehensively consider the antenna deployment process, electromagnetic performance, and structural dynamics performance. It has the following advantages: Based on the Workbench simulation platform, the simulation process is more convenient and concise, eliminating the need for co-simulation; flexible beam elements are used in the truss structure, reducing the requirements for degree-of-freedom settings and improving simulation convergence while saving time; using linear bodies instead of solid models greatly simplifies the model, saving simulation computational resources and time; Workbench post-processing functions are powerful, making it very convenient to observe various results; and the use of machine learning for multi-objective optimization allows for flexible optimization objects, avoiding the problem of some optimization objectives being unparalleled, thus saving simulation time.

[0076] In a specific embodiment of the present invention, a method for optimizing and controlling the preload of the vertical cable of a mesh antenna includes the following steps: Step 1: Create an analysis project and set up engineering data: Open the Workbench software. Click and drag the Transient Structural, Static Structure, and Modal modules from the Analysis System sub-column under the toolbox on the left side of the software to the project schematic diagram on the software project interface. Share the engineering data, geometry, and model of the three modules. Share the solution of the static structure with the modal settings to add a prestressed field for modal analysis. Double-click the Engineering Data, right-click the Structural Steel under the Material column and copy it. Set the cable net material accordingly, rename it to cable, and modify the material density, Young's modulus, and Poisson's ratio.

[0077] Step 2: Establish a simulation model: Truss Structure Model: Right-click the Geometry option in the project, click Import Geometry, and import the truss solid structure model in .step format; Model Refinement: Right-click the Geometry option in the project, click Edit Geometry in SpaceClaim, and simplify and refine the model, merge parts, etc. in the opened SpaceClaim interface; Cable Net Model: Right-click the Geometry option in the project, click Edit Geometry in DesignModeler, click Generate in the DesignModeler interface, then click File to run the DesignModeler parametric cable net linear body model building program; Material Parameters and Element Type: Click the Model option in the project to open the Mechanical interface. In the Geometry section of the project bar on the left side of the interface, set the material parameters and element type for each geometry. Set the material of the cable net to cable material from the engineering data and the element type to Cable element provided by Workbench. Set the material of the other geometry to structural steel and the element type to flexible beam element.

[0078] Step 3: Set up connection relationships: Truss section: In the Mechanical interface, select the Connections tab in the left-hand project bar to insert connection pairs. Set a fixed pair between one vertical bar and the ground, and connect the remaining vertical bars to the hinges as fixed pairs. Connect the horizontal and diagonal bars to the hinges as revolute pairs. Connect the thick and thin diagonal bars with cylindrical pairs. Truss and cable net: The truss and the part of the cable net attached to the truss are connected by bushing pairs. The stiffness of the bushing pairs is shown in the table below.

[0079]

[0080] Step 4: Transient Structure Analysis Settings: Analysis Step: In the Analysis Settings under the Transient tab in the Mechanical interface's left-hand panel, set up the simulation process. Plan six substeps with the following time divisions: first substep 0-24s, second substep 24-96s, third substep 96s-120s, fourth substep 120-144s, fifth substep 144-216s, and sixth substep 216-240s. Enable carry-over substeps. The minimum step size for all substeps is 50, and the maximum step size is 500. Weak Spring and Nonlinear Settings: In the Analysis Settings under the Transient tab, disable weak spring and enable large deflection. For poor convergence, enable line search in the nonlinear settings.

[0081] Step 5: Static Structure Analysis Settings Analysis Step: In the Analysis Settings under the Static category in the left-hand project bar of the Mechanical interface, set an analysis step with a duration of 1 second. The step size is the same as the transient structure analysis settings. Weak Spring and Nonlinear Settings: In the Analysis Settings under the Static category, turn on Weak Spring and Turn on Large Deflection.

[0082] Step 6, Modal Analysis Settings: Order: In the Analysis Settings section of the Modal category in the left-hand project bar of the Mechanical interface, set the maximum modal order to 6.

[0083] Step 7: Transient structure boundary conditions: Connections between cable nets: Insert "Coupling" in the "Transient" tab. Set three couplings for each cable net node to constrain the x, y, and z displacement degrees of freedom respectively. Select the vertices of all cable segments connected to the node for the geometry. Achieve mutual connection of each cable net at the node through coupling boundary conditions. Gravity: Right-click on "Transient" in the project bar and insert "Standard Earth Gravity" boundary condition. Gravity is applied along the vertical direction from the front cable net to the rear cable net. Drive: Select "Transient" in the left project bar of the "Mechanical" interface and insert "Remote Displacement". Set a remote displacement for each horizontal bar and a remote displacement for each substep. Use the remote displacement to add a rotation angle to the horizontal bar in the three-bar connecting hinge to control the antenna extension and retraction. The rotation curve equations for the six substeps are as follows.

[0084]

[0085] Step 8: Static structural boundary conditions: Connections between cable nets: Same as transient structural boundary conditions; Gravity: Same as transient structural boundary conditions; Degree of freedom constraints: Insert fixed support constraints on truss motion in the Static column.

[0086] Step 9: Modal prestressing setting: In the Mechanical interface, select the Modal section in the left-hand project bar, and then select Static Structure in the Prestressing Environment section.

[0087] Step 10, Solve: In the Homepage at the top, check the Distributed option, enter the number of computer cores allowed to be called, which is generally 4 to 8 cores. The more cores, the faster the solution. Click Solve to run the transient structure simulation project.

[0088] Step 11, Post-processing: Total Deformation: Right-click the "Solve" option in the transient structure module, insert the total deformation item of the deformation results, and search for the results. This will give you the overall movement of the antenna as it extends and retracts, and you can export the motion video file. Torque: Insert the torque response item of the probe in the "Solve" option. Select the boundary conditions as the positioning method, and select the remote displacement of the rod to be observed. Select the corresponding local coordinate system as the direction. Search for the results to get the reaction torque of the corresponding remote displacement segment. By setting all the remote displacements on a rod in this way, you can get the driving torque required for the rod to move according to the planned motion curve. First-order Natural Frequency: Insert the total deformation item of the deformation results in the "Solve" option of the mode, set the mode order to first-order, and search for the results to get the first-order mode shape and natural frequency.

[0089] Step 12: Parameter Correlation Analysis: Parameterization: Click on the torque response result corresponding to the near-fully unfolded part in the transient structure column, and check the small box before the total of the maximum time value in the torque response details window displayed below. Click on the total deformation result in the modal column, and check the small box before the frequency in the information column below the details window. This parameterizes the maximum value and first natural frequency of the near-fully unfolded part in the torque response result in the post-processing. Parameter Correlation Analysis Settings: Drag the Parameters Correlation module from the Design Exploration list in the right toolbar of the Workbench interface to the parameter set. Click on parameter correlation and set the range and number of samples for the vertical cable prestress independent variable in the right property window. Check the option to save the design points after running and to retain data for each retained design point. Parameter Correlation Analysis Solution: Right-click on parameter correlation analysis and select update. The software will solve all design points. Obtain Sample Data: After the solution is completed, double-click on the parameter set. A pop-up window records the sample data of the peak value of the driving torque mutation and the first natural frequency under the influence of the vertical cable prestress obtained through parameter correlation analysis. The sample data for surface accuracy needs to be calculated by extracting the displacement of the cable net nodes according to the RMS formula.

[0090] Step 13: Regression Model Training Model training: The regression model is trained using sample data as training data for machine learning. The training set and test set are divided, and regression models with three optimization objectives are trained. Among them, the peak data of the driving torque mutation is trained using the ridge regression model, and the first-order natural frequency and surface accuracy data are trained using the support vector machine model. Model validation: The predicted vertical cable tension is input into the parameter set of the parameter correlation optimization algorithm for solution. The obtained target value is compared with the predicted target value to verify the accuracy and reliability of the model. R 2 A value greater than 0.9 indicates that the model has good predictive performance and can be used for further optimization; otherwise, a new model needs to be selected and the parameters adjusted.

[0091] Step Fourteen: Multi-objective optimization: Multi-objective optimization model: A multi-objective optimization model is constructed using a trained regression model. The optimization results of the three objectives are normalized. Weighted control is adopted to control the optimization ratio of each objective and to calculate the optimization index of the three objectives. The constraint condition is the range of values ​​of the vertical cable pretension. Iterative optimization: The number of iterations is set to continuously optimize and obtain the minimum weighted value. The optimal values ​​of the three optimization objectives and the corresponding vertical cable pretension under the given weighted value can be obtained.

[0092] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of an optimized control method for the preload of the vertical cable of a mesh antenna. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0093] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the optimized control method for the preload of the vertical cable of the mesh antenna. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include RAM (Random Access Memory) and / or cache memory, etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0094] Based on the same inventive concept, this application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer device, cause the computer device to perform the steps of the above-described method for optimizing and controlling the pretension force of the vertical cable of the mesh antenna.

[0095] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program code.

[0096] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer apparatus or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer device or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions may also be loaded onto a computer device or other programmable data processing equipment to cause a series of operational steps to be performed on the computer device or other programmable equipment to produce a process implemented by the computer device, thereby providing instructions that execute on the computer device or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0100] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing and controlling the preload of the vertical cable in a mesh antenna, characterized in that, Includes the following steps: Establish interconnected transient structure analysis modules, static structure analysis modules, and modal analysis modules on the simulation platform to form a joint simulation project; In the joint simulation project, a dynamic simulation model of the deployment process of the mesh antenna is established by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. By setting the pretension of the vertical cable in the dynamic simulation model as the independent variable, and through parameter correlation analysis, sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable are generated. Based on the sample data, a regression model is trained. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

2. The method for optimizing and controlling the preload of the vertical cable of a mesh antenna according to claim 1, characterized in that, The specific connection configuration is as follows: the truss and the cable net are connected by bushing pairs, and the cable segments are connected at the cable net nodes by degree-of-freedom coupling.

3. The method for optimizing and controlling the preload of the vertical cable of a mesh antenna according to claim 1, characterized in that, Specifically, the boundary conditions are configured as follows: in the transient structure analysis module, the mesh antenna is retracted and extended by remote displacement control, and gravity boundary conditions are added; in the static structure analysis module, fixed supports are added to constrain the truss motion.

4. The method for optimizing and controlling the preload of the vertical cable of a mesh antenna according to claim 1, characterized in that, Multiple assembly performance indicators include at least the peak value of the sudden change in driving torque during the deployment of the mesh antenna, the first-order natural frequency of the mesh antenna after deployment, and the surface accuracy of the mesh antenna reflector.

5. The method for optimizing and controlling the preload of the vertical cable of a mesh antenna according to claim 4, characterized in that, The specific steps for training the regression model based on the sample data are as follows: The ridge regression model was trained based on sample data of the sudden peak of the driving torque during the deployment of the mesh antenna. A support vector machine model is trained based on sample data of the first-order natural frequency of the mesh antenna after it is deployed and the surface accuracy of the mesh antenna reflector.

6. The method for optimizing and controlling the preload of the vertical cable of a mesh antenna according to claim 1, characterized in that, The simulation platform is Ansys Workbench.

7. An optimized control system for the preload of the vertical cable of a mesh antenna, characterized in that, include: The first module is used to establish interrelated transient structure analysis modules, static structure analysis modules and modal analysis modules on the simulation platform to form a joint simulation project; The second module is used in joint simulation projects to build a dynamic simulation model of the deployment process of the mesh antenna by constructing a parameterized cable mesh body model and a flexible truss model, and configuring the connection relationships and boundary conditions. The third module is used to set the pretension of the vertical cable in the dynamic simulation model as an independent variable, and generate sample data of multiple assembly performance indicators of the mesh antenna corresponding to different values ​​of the pretension of the vertical cable through parameter correlation analysis. The fourth module is used to train a regression model based on sample data. Based on the trained regression model, a surrogate model is established from the pretension of the vertical cable to multiple assembly performance indicators. A multi-objective optimization function with the pretension of the vertical cable as the variable is constructed. The surrogate model is used to solve the multi-objective optimization function to obtain the optimal pretension configuration that can simultaneously optimize multiple assembly performance indicators.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the optimized control method for the pretension of the vertical cable of the mesh antenna as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the optimized control method for the pretension of the vertical cable of the mesh antenna as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the optimized control method for the pretension of the vertical cable of the mesh antenna as described in any one of claims 1 to 6.

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