A method for controlling transient wind load vibration of a reflector antenna support leg

Abstract

The application provides a kind of reflector antenna support leg transient wind load vibration control method, comprising: according to support leg parameter extraction modal matrix and establish state space model;Based on modal matrix configuration actuator, sensor and construct feedback state vector;Determine transient wind load time history and LQR control weight, determine LQR feedback control gain and exert control force, update system state to obtain controlled vibration response;According to state space model, determine uncontrolled vibration response and termination condition, according to uncontrolled vibration response, controlled vibration response and actuator control force determine vibration control index and antenna directional diagram index;When the index does not meet the termination condition, adjust LQR control weight and update feedback control gain, re-determine the index until the termination condition is met.The application can give consideration to reflector antenna support leg vibration suppression, antenna directional diagram improvement and beam pointing accuracy improvement under the action of transient wind load.

Description

Technical Field

[0001] This invention belongs to the field of structural vibration control technology, specifically relating to a method for controlling transient wind load vibration of a reflector antenna support leg. Background Technology

[0002] Large reflector antennas are widely used in deep space communication, remote sensing mapping, and radio astronomy. The antenna legs support and position the feed and related components. Gusts and turbulence can excite multi-order coupled vibrations in the legs, causing the feed to deviate from the ideal focus and resulting in lateral and axial defocusing. This, in turn, causes main lobe shift, main lobe broadening, gain reduction, and beam pointing error.

[0003] Existing outrigger vibration control typically employs empirically arranged actuators, fixed-parameter linear quadratic regulators (LQR) controllers, or full-structure virtual state feedback. Empirically arranged actuators struggle to ensure multi-mode controllability, fixed control weights are ill-suited to transient wind load variations, and virtual states are difficult to obtain directly from a limited number of actual sensors; furthermore, low-precision state integration accumulates errors in transient response calculations.

[0004] Furthermore, existing methods often use structural displacement reduction as the sole control objective, without incorporating leg displacement, feed defocus, and antenna pattern indicators into the same iterative chain. Consequently, it is difficult to determine whether the structural vibration reduction results meet the requirements for beam pointing, gain, and main lobe width. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a method for controlling transient wind load vibration of reflector antenna support legs. The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for controlling transient wind load vibration of a reflector antenna support leg, comprising: The modal matrix is ​​extracted and a state-space model is established based on the structural parameters and physical property parameters of the support legs. The target actuator configuration is determined based on the modality matrix and the state space model, and the target sensor configuration is determined based on the modality matrix; a feedback state vector is constructed based on the target sensor configuration, the modality matrix, and the state space model. A fluctuating wind speed time history is generated based on the wind field operating parameters. A transient wind load time history is determined based on the fluctuating wind speed time history. LQR control weights are determined based on the transient wind load time history. Feedback control gain is determined based on the state space model and the LQR control weights. Actuator control force is determined based on the feedback control gain and the feedback state vector. The actuator control force is applied to the position of the target actuator and the system state is updated to obtain a controlled vibration response. The uncontrolled vibration response and iteration termination condition are determined based on the state-space model and the transient wind load time history; the vibration control evaluation index is determined based on the uncontrolled vibration response, the controlled vibration response and the actuator control force; and the antenna pattern evaluation index is determined based on the controlled vibration response. If either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted and the feedback control gain is updated based on the adjusted LQR control weights. The vibration control evaluation index and the antenna pattern evaluation index are then redefined based on the updated feedback control gain until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition.

[0006] In one embodiment of the present invention, the step of extracting the modal matrix and establishing a state-space model based on the support structure parameters and physical property parameters includes: A parameterized model is established based on the support structure parameters and the physical property parameters to obtain the mass matrix and stiffness matrix; Modal analysis is performed on the parameterized model to obtain the first six natural frequencies and the modal matrix. The damping matrix is ​​then determined based on the first six natural frequencies. Based on the mass matrix, stiffness matrix, damping matrix, modal matrix, and actuator parameters, the physical coordinates are converted into modal coordinates to obtain the state-space model.

[0007] In one embodiment of the present invention, determining the target actuator configuration based on the modality matrix and the state space model includes: Extract the actuator input matrix from the state space model, and determine the modal input matrix based on the modal matrix and the actuator input matrix; The L2 controllability index is determined based on the modal input matrix and candidate degrees of freedom. The candidate position pool is then selected according to the L2 controllability index, and candidate actuator configurations that meet the controllability ratio requirements of each modality are determined from the candidate position pool. Based on the candidate actuator configurations, the target actuator configuration is determined using a neighborhood-constrained particle swarm optimization algorithm with the optimization objectives of improving the overall controllability index and reducing the standard deviation of actuator output.

[0008] In one embodiment of the present invention, determining the target sensor configuration based on the modality matrix includes: By filtering out the degrees of freedom in the preset filtering direction from the candidate degrees of freedom of the support legs, the candidate sensor positions in the lateral and axial directions are obtained; The modal output vector corresponding to each candidate sensor position is determined based on the modal matrix, and the H2 observability index is determined based on the modal output vector, the preset damping ratio, and the average of the first six natural frequencies extracted from the state space model. The high observability candidate locations are selected based on the observability index threshold, and the spatial distribution of the high observability candidate locations is selected based on the node spacing threshold to obtain the target sensor configuration.

[0009] In one embodiment of the present invention, constructing the feedback state vector based on the target sensor configuration, the modality matrix, and the state space model includes: Acquire the sensor displacement data collected by each sensor in the target sensor configuration along the lateral and axial directions, and construct a sensor-modal mapping matrix based on the modal matrix; The sensor displacement data is fitted by least squares using the pseudo-inverse of the sensor-modal mapping matrix to obtain the first six modal displacements. Based on the state-space model and the first six modal displacements, the first six modal velocities are determined by Kalman filtering, and the first six modal displacements and the first six modal velocities are combined into the feedback state vector.

[0010] In one embodiment of the present invention, the wind field operating parameters include a reference wind speed, a friction velocity coefficient, and a cutoff frequency; the step of generating a fluctuating wind speed time history based on the wind field operating parameters, and determining a transient wind load time history based on the fluctuating wind speed time history, includes: Based on the reference wind speed, friction velocity coefficient, and cutoff frequency, the wind speed spectrum is determined using the Davenport wind spectrum; the fluctuating wind speed time history is generated based on the wind speed spectrum and random phase using the inverse fast Fourier transform. The real-time wind speed is obtained by superimposing the base wind speed with the time history of the fluctuating wind speed. The transient wind load at each moment is determined based on the real-time wind speed, air density, wind pressure coefficient and windward area, and the transient wind load time history is obtained.

[0011] In one embodiment of the present invention, the steps of determining LQR control weights based on the transient wind load time history, determining feedback control gain based on the state space model and the LQR control weights, determining actuator control force based on the feedback control gain and the feedback state vector, applying the actuator control force to the position of the target actuator configuration and updating the system state to obtain a controlled vibration response include: Real-time wind load intensity is extracted from the transient wind load time history. The state weight matrix and control force weight matrix are determined based on the real-time wind load intensity. The LQR control weight is determined based on the state weight matrix and control force weight matrix. The feedback control gain is obtained by solving the continuous-time algebraic Riccati equation based on the state-space model and the LQR control weights. The actuator control force is determined and limited based on the feedback control gain and the feedback state vector. The limited actuator control force is applied to the position of the target actuator. The system state is updated by the fourth-order Runge-Kutta integral to obtain the controlled vibration response.

[0012] In one embodiment of the present invention, determining the uncontrolled vibration response and iteration termination condition based on the state-space model and the transient wind load time history includes: Based on the state-space model and the transient wind load time history, numerical simulation is performed on the reflector antenna support leg without applied control force, and the displacement time history of the preset structural characterization node is extracted to obtain the uncontrolled vibration response. Based on the uncontrolled vibration response, preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss are determined respectively; the iteration termination condition is formed according to the preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss.

[0013] In one embodiment of the present invention, determining the vibration control evaluation index based on the uncontrolled vibration response, the controlled vibration response, and the actuator control force, and determining the antenna pattern evaluation index based on the controlled vibration response, includes: The average vibration suppression rate is determined based on the displacement of the preset structural characterization node in the uncontrolled vibration response and the controlled vibration response, and the standard deviation of the actuator output force is determined based on the actuator control force to obtain the vibration control evaluation index. The maximum lateral displacement and the maximum axial displacement are extracted from the controlled vibration response to obtain the feed lateral defocusing amount and the feed axial defocusing amount. The lateral and axial offset patterns are determined based on the feed lateral offset and the feed axial offset, and the antenna pattern evaluation index is determined based on the main lobe offset, antenna gain loss, and half-power beamwidth in the lateral and axial offset patterns.

[0014] In one embodiment of the present invention, when either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted and the feedback control gain is updated based on the adjusted LQR control weights. The vibration control evaluation index and the antenna pattern evaluation index are then re-determined based on the updated feedback control gain, until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition. This includes: If any of the average vibration suppression rate and actuator output standard deviation in the vibration control evaluation index, or the main lobe offset and antenna gain loss in the antenna pattern evaluation index, fails to meet the iteration termination condition, the tuning parameters of the LQR control weights are adjusted to obtain the adjusted LQR control weights; the tuning parameters include the state weight base value, the control force weight base value, and the adaptive amplification factor or adaptive attenuation factor. The feedback control gain is updated according to the adjusted LQR control weights. The actuator control force is re-determined based on the updated feedback control gain and the feedback state vector. The system state is then updated to obtain the updated controlled vibration response. The vibration control evaluation index is re-determined based on the uncontrolled vibration response, the updated controlled vibration response, and the re-determined actuator control force; and the antenna pattern evaluation index is re-determined based on the updated controlled vibration response. When the average vibration suppression rate, the actuator output standard deviation, the main lobe offset, and the antenna gain loss all satisfy the iteration termination condition, the target actuator configuration, the target sensor configuration, and the current state weight base value, the control force weight base value, the adaptive amplification coefficient, and the adaptive attenuation coefficient are determined as the optimal control scheme.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The transient wind load vibration control method for reflector antenna support legs provided by this invention extracts the modal matrix and establishes a state-space model based on the support leg structural parameters and physical property parameters, ensuring that actuator configuration, sensor configuration, feedback state construction, and system state update all adopt the same dynamic basis. It determines the target actuator configuration based on the modal matrix and state-space model, determines the target sensor configuration based on the modal matrix, and constructs a feedback state vector based on the target sensor configuration, modal matrix, and state-space model, ensuring that the control force application position and displacement feedback position match the modal controllability and observability. Furthermore, it generates the pulsating wind speed time history based on wind field operating parameters and determines the transient wind load time history, determines the LQR control weights based on the transient wind load time history, determines the feedback control gain based on the state-space model and LQR control weights, and determines the feedback control gain based on the feedback control gain and feedback state. The vector determines the actuator control force and applies it to the target actuator configuration position, enabling the controller to update its output force and obtain a controlled vibration response in response to transient wind load changes. The uncontrolled vibration response and iteration termination condition are determined based on the state-space model and the transient wind load time history. Vibration control evaluation indices are determined based on the uncontrolled vibration response, controlled vibration response, and actuator control force. Antenna pattern evaluation indices are determined based on the controlled vibration response, allowing the structural vibration reduction results to be evaluated by both vibration control and antenna electrical performance. If either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted, and the feedback control gain is updated based on the adjusted LQR control weights. The vibration control evaluation index and the antenna pattern evaluation index are then redefined, allowing the control parameters to iterate in the same closed loop until the iteration termination condition is met. Ultimately, this invention can simultaneously suppress the vibration of the reflector antenna legs, improve the antenna pattern, and enhance beam pointing accuracy under transient wind loads, thereby improving the service reliability of the reflector antenna in wind-affected environments.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of a transient wind load vibration control method for a reflector antenna support leg provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the cross-section and dimensions of a hollow circular tube with a relatively thick support leg, provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the cross-section and dimensions of a thinner hollow circular tube for supporting legs provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the cross-section and dimensions of a hollow square tube at the feed end provided in an embodiment of the present invention; Figure 5 This is a time history diagram of pulsating wind speed provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the coordinates of the reflector antenna support legs and the position of the preset structure characterizing the node provided in an embodiment of the present invention; Figure 7 This is a schematic diagram comparing the directional patterns before and after lateral defocusing control, provided by an embodiment of the present invention. Figure 8 This is a schematic diagram comparing the directional patterns before and after axial defocusing control according to an embodiment of the present invention; Figure 9 This is a schematic diagram comparing the displacement response of a preset structure node under uncontrolled and controlled states, provided by an embodiment of the present invention. Figure 10 This is a schematic diagram of the structure of a transient wind load vibration control device for a reflector antenna support leg provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the hardware entity of an electronic device to which embodiments of the present invention are applied. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0019] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation process of a transient wind load vibration control method for a reflector antenna support leg provided in an embodiment of the present invention. Figure 1 As shown, the transient wind load vibration control method for reflector antenna support legs provided in this embodiment of the invention may include the following steps: Step S101: Extract the modal matrix and establish a state-space model based on the structural parameters and physical property parameters of the support leg.

[0020] In one possible implementation, a parametric model can be established based on the structural parameters and physical properties of the support legs to obtain the mass matrix and stiffness matrix; modal analysis can be performed on the parametric model to obtain the first six natural frequencies and modal matrices, and the damping matrix can be determined based on the first six natural frequencies; based on the mass matrix, stiffness matrix, damping matrix, modal matrix and actuator parameters, the physical coordinates can be converted into modal coordinates to obtain the state-space model.

[0021] The support structure parameters can include the length, cross-sectional dimensions, and wall thickness of the reflector antenna support legs. Physical property parameters can include the density, elastic modulus, and Poisson's ratio of the support material. A parametric model is used to convert the support structure parameters and physical property parameters into a finite element structure and its mass and stiffness matrices. The state-space model is a dynamic model using modal displacements and modal velocities as state variables and wind loads and actuator control forces as inputs.

[0022] In one embodiment, the thicker hollow cylindrical tube is approximately 8 meters long, has an outer diameter of 40 millimeters, and a wall thickness of 2 millimeters. Figure 2 As shown; the thinner hollow cylindrical tube has an outer diameter of 30 mm and a wall thickness of 2 mm, as... Figure 3 As shown; the hollow square tube at the feed end has an outer width of 60 mm and a wall thickness of 4 mm, as... Figure 4 As shown. The density of the support leg material is 7850 kg / m³, the elastic modulus is 206 GPa, and the Poisson's ratio is 0.3.

[0023] The dynamic relationships of the parametric model are characterized by the mass matrix, damping matrix, stiffness matrix, physical coordinate displacement, and wind load vector. The first six natural frequencies obtained from modal analysis are approximately 7.0694 Hz, 7.1013 Hz, 7.4844 Hz, 11.77 Hz, 13.415 Hz, and 13.559 Hz, respectively. Rayleigh damping coefficients are determined based on these first six natural frequencies, with the mass proportionality coefficient α set to 0.02 and the stiffness proportionality coefficient β set to 0.001, and the damping matrix is ​​formed accordingly.

[0024] The state-space model can include a 12-dimensional state vector, a system state matrix, an actuator input matrix, and modal wind load input relationships. The actuator input matrix describes the effect of the control force at each candidate actuator position on the modal states. For example, a parametric model can be established using the following formula: ; Where [M] is the mass matrix, [C] is the damping matrix, [K] is the stiffness matrix, and {u} is the physical coordinate displacement vector. For velocity vectors, For acceleration vectors, This is the wind load vector.

[0025] The formula for calculating the wind load vector is: ; in, Based on the time-domain function of wind speed, ρ is the turbulent wind speed, Cp is the air density, and A is the windward area.

[0026] The first six modal matrices Φ are extracted through modal analysis, and the physical coordinates are converted into modal coordinates to obtain the state-space model in modal coordinates. : ; ; in, It is a 12-dimensional state vector (composed of 6-dimensional modal displacements + 6-dimensional modal velocities; the modal velocities can be derived from the modal displacements, therefore...). It can be equivalent to a 6-dimensional state vector), where A is the system state matrix, Blight is the actuator input matrix, F is the modal wind load vector, and C is the output matrix.

[0027] Step S102: Determine the target actuator configuration based on the modal matrix and state space model, and determine the target sensor configuration based on the modal matrix; construct the feedback state vector based on the target sensor configuration, modal matrix and state space model.

[0028] In one possible implementation, the target actuator configuration is determined based on the modal matrix and the state-space model, including: extracting the actuator input matrix from the state-space model; determining the modal input matrix based on the modal matrix and the actuator input matrix; determining the L2 controllability index based on the modal input matrix and candidate degrees of freedom; screening a candidate position pool according to the L2 controllability index; and determining candidate actuator configurations from the candidate position pool that meet the controllability ratio requirements of each modality; and determining the target actuator configuration based on the candidate actuator configurations using a neighborhood-constrained particle swarm optimization algorithm, with the optimization objectives of improving the overall controllability index and reducing the actuator output standard deviation.

[0029] For example, the target actuator configuration is a set of positions used to apply actuator control forces after controllability screening and particle swarm optimization. The L2 controllability index is used to characterize the strength of the control effect of candidate degrees of freedom on the first six modes; the candidate position pool is a set of positions selected from candidate degrees of freedom according to the L2 controllability index.

[0030] The L2 controllability index can be determined using the following formula: ; in, The modal input matrix, The modal input matrix is ​​the first... List, The candidate degree of freedom number, It is a controllability indicator of the L2 norm.

[0031] After determining the L2 controllability index, the top 100 optimal positions can be selected in descending order according to the L2 controllability index to form a candidate position pool, and the controllability ratio of each mode should not be less than 20%.

[0032] The modal controllability ratio requirement can be expressed as: ; in, The modal input matrix is ​​the first... Line number The elements of the column, where i is the modal order.

[0033] The velocity and position update formulas for the neighborhood-constrained particle swarm optimization (PSO) algorithm are as follows: ; ; Where ω is the inertia weight, For the first The particle's velocity vector at the next iteration. For the first The particle's velocity vector at the next iteration. This represents the optimal position in the individual history of the particle. For the first The particle's position vector at the next iteration. For the first The particle's position vector at the next iteration. Let c1 and c2 be learning factors, and r1 and r2 be random numbers between [0,1].

[0034] When determining the target actuator configuration, the neighborhood constraint can be set centered on the initial L2 norm selection result, limiting the search range to ±50 indices to ensure the engineering feasibility of the optimization result; the optimization objective is to maximize the comprehensive controllability index. Minimize the standard deviation of actuator output force σu.

[0035] In one possible implementation, determining the target sensor configuration based on the modal matrix includes: filtering out degrees of freedom in a preset filtering direction from the candidate degrees of freedom of the support legs to obtain candidate sensor positions in the lateral and axial directions; determining the modal output vector corresponding to each candidate sensor position based on the modal matrix, and determining the H2 observability index based on the modal output vector, a preset damping ratio, and the average of the first six natural frequencies extracted from the state-space model; filtering high observability candidate positions based on the observability index threshold, and performing spatial distribution filtering on the high observability candidate positions based on the node spacing threshold to obtain the target sensor configuration.

[0036] For example, the preset filtering direction is the Y direction, with the horizontal and axial directions being the X and Z directions, respectively. The modal output vector is the row vector in the modality matrix corresponding to the candidate sensor position; the H2 observability index is used to characterize the observability of that position for the first six modal states.

[0037] The H2 observability index can be determined using the following formula: ; in, Let ξ0 be the modal output vector, and let ξ0 be the preset damping ratio. This represents the average of the first six natural frequencies; Candidate locations with high observability index of not less than 2.2×104 can be screened, and then uniformly distributed based on the node spacing threshold (such as 50, or other values ​​set according to actual conditions) to finally obtain 20 sensor nodes as the target sensor configuration.

[0038] In one possible implementation, a feedback state vector is constructed based on the target sensor configuration, modal matrix, and state space model. This includes: acquiring sensor displacement data collected by each sensor in the target sensor configuration along the lateral and axial directions; constructing a sensor-modal mapping matrix based on the modal matrix; performing least-squares fitting on the sensor displacement data using the pseudo-inverse of the sensor-modal mapping matrix to obtain the first six modal displacements; determining the first six modal velocities using Kalman filtering based on the state space model and the first six modal displacements; and combining the first six modal displacements and the first six modal velocities into a feedback state vector.

[0039] For example, the feedback state vector is a modal state vector used by the LQR controller, consisting of the first six modal displacements and the first six modal velocities.

[0040] The displacements in the X and Z directions of 20 sensor nodes can be extracted from the current physical coordinate displacement to simulate the actual sensor measurement data and construct a sensor-modal mapping matrix. The first six modal displacements were solved using the Moore-Penrose pseudoinverse. : ; in, for The Moore-Penrose pseudo-inverse, This is the displacement vector measured by the sensor.

[0041] Step S103: Generate the pulsating wind speed time history based on the wind field operating parameters, and determine the transient wind load time history based on the pulsating wind speed time history; determine the LQR control weights based on the transient wind load time history, and determine the feedback control gain based on the state space model and the LQR control weights; determine the actuator control force based on the feedback control gain and the feedback state vector, apply the actuator control force to the position of the target actuator configuration and update the system state to obtain the controlled vibration response.

[0042] In one possible implementation, the wind field operating parameters include a reference wind speed, friction velocity coefficient, and cutoff frequency. A fluctuating wind speed time history is generated based on these parameters, and a transient wind load time history is determined based on this time history. This includes: determining the wind speed spectrum using the Davenport wind spectrum based on the reference wind speed, friction velocity coefficient, and cutoff frequency; generating the fluctuating wind speed time history based on the wind speed spectrum and random phase using inverse fast Fourier transform; superimposing the base wind speed with the fluctuating wind speed time history to obtain the real-time wind speed; and determining the transient wind load at each moment based on the real-time wind speed, air density, wind pressure coefficient, and windward area to obtain the transient wind load time history.

[0043] In one embodiment, the air density ρ = 1.225 kg / m³, the wind pressure coefficient Cp = 1.0, and the windward area A = 0.01 m². The Davenport wind spectrum can be used to generate the wind speed spectrum, with a reference wind speed v at a height of 10 m. 10 =2 m / s, friction velocity coefficient k = 0.00215, cutoff frequency 14.4 Hz, the fluctuating wind speed time history can be generated by inverse fast Fourier transform random phase, see [link to relevant documentation]. Figure 5 .

[0044] Transient wind load time history The calculation formula is: ; in, ρ is the real-time wind speed, Cp is the air density, and A is the windward area.

[0045] In one possible implementation, the LQR control weights are determined based on the transient wind load time history, and the feedback control gain is determined based on the state-space model and the LQR control weights. The actuator control force is determined based on the feedback control gain and the feedback state vector. The actuator control force is applied to the position of the target actuator and the system state is updated to obtain the controlled vibration response. This includes: extracting the real-time wind load intensity from the transient wind load time history; determining the state weight matrix and the control force weight matrix based on the real-time wind load intensity; determining the LQR control weights based on the state weight matrix and the control force weight matrix; solving the continuous-time algebraic Riccati equation based on the state-space model and the LQR control weights to obtain the feedback control gain; determining and limiting the actuator control force based on the feedback control gain and the feedback state vector; applying the limited actuator control force to the position of the target actuator; and updating the system state through a fourth-order Runge-Kutta integral to obtain the controlled vibration response.

[0046] The continuous-time algebraic Riccati equation can be expressed as: ; in, The system state matrix, For the actuator input matrix, For the Riccati system matrix, The state weight matrix is... This is the control force weight matrix. The state weight matrix and the control force weight matrix can be determined using the following formulas: ; ; in, These are the base values ​​for the state weights. For adaptive amplification factor, The real-time wind load intensity has a value range of [0.01, 1]. Here, I is a 12-dimensional state vector of the state-space model, and I is the identity matrix. This is the base value for the control force weight. For adaptive attenuation coefficients, This is the lower limit of the control force weight, used to ensure that the control force weight does not decrease indefinitely, which would cause the actuator to output excessive force.

[0047] The feedback control gain can be determined using the following formula. : ; The actuator control force can be determined using the following formula. : ; in, This is the state vector fed back by the sensor.

[0048] The actuator control force can be limited to ±5N and applied to the installation positions of the 6 optimized actuators.

[0049] The process of updating the state of the fourth-order Runge-Kutta integral system is shown in the following formula: ; ; ; ; ; in, The control output calculated by LQR at time k. Let be the external wind disturbance load at time k. Let k be the system state vector at the k-th sampling time. The system state matrix, For the actuator input matrix, To simulate / control the sampling step size, , , , For slope estimation, Let be the system state vector at the (k+1)th sampling time.

[0050] Step S104: Determine the uncontrolled vibration response and iteration termination condition based on the state-space model and transient wind load time history; determine the vibration control evaluation index based on the uncontrolled vibration response, controlled vibration response and actuator control force, and determine the antenna pattern evaluation index based on the controlled vibration response.

[0051] In one possible implementation, the uncontrolled vibration response and iteration termination conditions are determined based on the state-space model and the transient wind load time history. This includes: performing numerical simulations on the reflector antenna legs without applied control force based on the state-space model and the transient wind load time history; extracting the displacement time history of preset structural characterization nodes to obtain the uncontrolled vibration response; determining preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss based on the uncontrolled vibration response; and forming iteration termination conditions based on the preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss.

[0052] For example, the preset structural characterization nodes are pre-selected nodes used to characterize the vibration and control effect of the support leg. In one embodiment of the present invention, nodes 2176, 2272, 2340, and 2417 are selected as preset structural characterization nodes. See [link to relevant documentation]. Figure 6 .

[0053] Vibration suppression rate can be calculated using the following formula. : ; Where m is the number of nodes representing the preset structure. The displacement time history of the uncontrolled vibration response at the predefined structural characterization nodes is given. The displacement time history of the controlled vibration response at the preset structural characterization node.

[0054] For example, based on the uncontrolled vibration response, preset performance targets can be set for vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss, respectively. Specifically, the preset performance target for vibration suppression rate is determined based on the peak displacement of the uncontrolled vibration response at a preset structural characterization node; the preset performance target for actuator output standard deviation is set according to the amplitude of the uncontrolled vibration response and the rated output range of the actuator, combined with engineering stability requirements; the preset performance targets for antenna pattern main lobe shift and antenna gain loss are set according to the degree of antenna pattern distortion mapped from the uncontrolled vibration response, combined with engineering requirements for antenna beam pointing accuracy and radiation efficiency. In one embodiment, the iteration termination condition can be: vibration suppression rate not less than 30%, actuator output standard deviation not greater than 1N, antenna pattern main lobe shift not greater than 0.5°, and residual antenna gain loss not greater than 0.5dB.

[0055] In one possible implementation, vibration control evaluation indicators are determined based on uncontrolled vibration response, controlled vibration response, and actuator control force, and antenna pattern evaluation indicators are determined based on controlled vibration response. This includes: determining the average vibration suppression rate based on the displacement of the pre-defined structural characterization node in the uncontrolled and controlled vibration responses; determining the actuator output standard deviation based on the actuator control force to obtain vibration control evaluation indicators; extracting the maximum lateral displacement and maximum axial displacement from the controlled vibration response to obtain the feed lateral offset and feed axial offset; determining the lateral offset pattern and axial offset pattern based on the feed lateral offset and feed axial offset; and determining the antenna pattern evaluation indicators based on the main lobe offset, antenna gain loss, and half-power beamwidth in the lateral offset pattern and axial offset pattern.

[0056] For example, the feed lateral offset can be calculated using the following formula: ; in, For phase difference, λ is the lateral defocusing amount of the feed source, and λ is the operating wavelength of the antenna. Let be the angle between a reflecting surface of the parabola and the focal point.

[0057] The feed axial defocusing amount can be calculated using the following formula: ; in, For phase difference, λ is the axial defocusing amount of the feed source, and λ is the operating wavelength of the antenna. Let be the angle between a reflecting surface of the parabola and the focal point.

[0058] Antenna pattern evaluation metrics include main lobe shift in the lateral focus pattern, main lobe shift in the axial focus pattern, antenna gain loss, and half-power beamwidth. The main lobe shift in the lateral focus pattern can be expressed as: ; in, This is a lateral focus pattern. For the independent variable of the radiation pattern, This represents the main lobe offset in the lateral focus pattern. and This represents the degenerate tuning coefficient in the transverse direction.

[0059] The main lobe offset in the axial focus pattern can be expressed as: ; in, for, The phase distortion factor. and For the axial degradation tuning factor, This represents antenna gain loss.

[0060] Antenna gain loss can be expressed as: ; in, For antenna gain loss, The operating wavelength of the antenna. This is the axial offset distance. It is half the angle of the parabola.

[0061] Half-power beamwidth can be expressed as: ; in, For half-power beamwidth, This is a lateral focus pattern. This is the axial defocusing direction diagram. Figure 7 and Figure 8 The control directions before and after the lateral and axial defocusing conditions are given respectively.

[0062] Step S105: If either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, adjust the LQR control weight and update the feedback control gain based on the adjusted LQR control weight. Then, redetermine the vibration control evaluation index and the antenna pattern evaluation index based on the updated feedback control gain, until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition.

[0063] In one possible implementation, if any one of the vibration control evaluation metrics, such as the average vibration suppression rate and actuator output standard deviation, or the antenna pattern evaluation metrics, such as the main lobe offset and antenna gain loss, fails to meet the iteration termination condition, the tuning parameters of the LQR control weights are adjusted to obtain the adjusted LQR control weights. The tuning parameters include the base values ​​of the state weights, the base values ​​of the control force weights, and an adaptive amplification factor or an adaptive attenuation factor. The feedback control gain is updated based on the adjusted LQR control weights. The actuator control force is then re-determined based on the updated feedback control gain and feedback state vector, and the system state is updated to obtain the updated controlled vibration response. The vibration control evaluation metrics are then re-determined based on the uncontrolled vibration response, the updated controlled vibration response, and the re-determined actuator control force. The antenna pattern evaluation metrics are also re-determined based on the updated controlled vibration response. When the average vibration suppression rate, actuator output standard deviation, main lobe offset, and antenna gain loss all meet the iteration termination condition, the target actuator configuration, the target sensor configuration, and the current base values ​​of the state weights, control force weights, adaptive amplification factor, and adaptive attenuation factor are determined as the optimal control scheme.

[0064] Taking the iteration termination conditions as follows: vibration suppression rate not less than 30%, actuator output standard deviation not greater than 1N, antenna pattern main lobe offset not greater than 0.5°, and antenna gain loss residual not greater than 0.5dB, if the vibration suppression rate is greater than or equal to 30%, the actuator output standard deviation is less than or equal to 1N, the antenna pattern main lobe offset is less than or equal to 0.5°, and the antenna gain loss residual is less than or equal to 0.5dB, then the iteration termination conditions are considered to be met. The target actuator configuration, target sensor configuration, and the current state weight base value, control force weight base value, adaptive amplification coefficient, and adaptive attenuation coefficient are determined as the optimal control scheme.

[0065] In one embodiment, after all the above indicators meet the iteration termination condition, the system outputs the positions of 6 target actuators, 20 target sensors, the current adaptive LQR control parameters, and the ±5N control force limiting parameters, and also outputs the comparison results of uncontrolled and controlled displacements of the preset structural characterization nodes. See [link to documentation]. Figure 9 , Figure 9 The uncontrolled and controlled displacement responses of the corresponding nodes are shown.

[0066] In one possible implementation, the positions of six target actuators are obtained using modal norm 2 selection and neighborhood-constrained particle swarm optimization. Table 1 shows the modal order and controllability percentage before and after optimization for each target actuator position.

[0067] Table 1

[0068] It can be seen that the controllability of each mode at each of the six positions exceeds 20%, and the controllability after particle swarm optimization is no less than the initial L2 selection result.

[0069] In one possible implementation, 20 target sensor configurations are obtained by filtering according to the H2 observability index and node spacing. Table 2 shows the target sensor configurations.

[0070] Table 2

[0071] Table 3 shows the vibration control results of the preset structural characterization nodes under transient wind loads.

[0072] Table 3

[0073] It can be seen that during the 0~2.5s pulsating wind load period, the average vibration suppression rate of the four preset structural characterization nodes all exceeded 34%, the actuator control force was limited to ±5N, and the actuator output standard deviation was 0.71N, which met the iteration termination condition.

[0074] Table 4 shows one antenna pattern evaluation result.

[0075] Table 4

[0076] As can be seen, the main lobe offset and maximum gain loss under controlled conditions are reduced to 0.02° and 0.03dB, respectively, both satisfying the iteration termination condition; the half-power beamwidth is reduced from 2.83° to 0.97°.

[0077] The transient wind load vibration control method for reflector antenna support legs provided by this invention extracts the modal matrix and establishes a state-space model based on the support leg structural parameters and physical property parameters, ensuring that actuator configuration, sensor configuration, feedback state construction, and system state update all adopt the same dynamic basis. It determines the target actuator configuration based on the modal matrix and state-space model, determines the target sensor configuration based on the modal matrix, and constructs a feedback state vector based on the target sensor configuration, modal matrix, and state-space model, ensuring that the control force application position and displacement feedback position match the modal controllability and observability. Furthermore, it generates the pulsating wind speed time history based on wind field operating parameters and determines the transient wind load time history, determines the LQR control weights based on the transient wind load time history, determines the feedback control gain based on the state-space model and LQR control weights, and determines the feedback control gain based on the feedback control gain and feedback state. The vector determines the actuator control force and applies it to the target actuator configuration position, enabling the controller to update its output force and obtain a controlled vibration response in response to transient wind load changes. The uncontrolled vibration response and iteration termination condition are determined based on the state-space model and the transient wind load time history. Vibration control evaluation indices are determined based on the uncontrolled vibration response, controlled vibration response, and actuator control force. Antenna pattern evaluation indices are determined based on the controlled vibration response, allowing the structural vibration reduction results to be evaluated by both vibration control and antenna electrical performance. If either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted, and the feedback control gain is updated based on the adjusted LQR control weights. The vibration control evaluation index and the antenna pattern evaluation index are then redefined, allowing the control parameters to iterate in the same closed loop until the iteration termination condition is met. Ultimately, this invention can simultaneously suppress the vibration of the reflector antenna legs, improve the antenna pattern, and enhance beam pointing accuracy under transient wind loads, thereby improving the service reliability of the reflector antenna in wind-affected environments.

[0078] Please see Figure 10 , Figure 10 This is a schematic diagram of a transient wind load vibration control device for a reflector antenna support leg provided in an embodiment of the present invention. The transient wind load vibration control device for a reflector antenna support leg provided by the present invention includes a spatial model establishment module 1001, a state vector construction module 1002, a vibration response determination module 1003, an evaluation index determination module 1004, and an evaluation index iteration module 1005.

[0079] Among them, the spatial model establishment module 1001 is used to extract the modal matrix and establish the state space model based on the strut structure parameters and physical property parameters. The state vector construction module 1002 is used to determine the target actuator configuration based on the modal matrix and the state space model, and to determine the target sensor configuration based on the modal matrix; and to construct a feedback state vector based on the target sensor configuration, the modal matrix and the state space model. The vibration response determination module 1003 is used to generate a fluctuating wind speed time history based on wind field operating parameters, determine a transient wind load time history based on the fluctuating wind speed time history, determine LQR control weights based on the transient wind load time history, determine feedback control gain based on the state space model and LQR control weights, determine actuator control force based on feedback control gain and feedback state vector, apply actuator control force to the position of the target actuator and update the system state to obtain the controlled vibration response. The evaluation index determination module 1004 is used to determine the uncontrolled vibration response and iteration termination condition based on the state space model and transient wind load time history; to determine the vibration control evaluation index based on the uncontrolled vibration response, the controlled vibration response and the actuator control force; and to determine the antenna pattern evaluation index based on the controlled vibration response. The evaluation index iteration module 1005 is used to adjust the LQR control weight and update the feedback control gain based on the adjusted LQR control weight when either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, and to redetermine the vibration control evaluation index and the antenna pattern evaluation index based on the updated feedback control gain, until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition.

[0080] Please see Figure 11 , Figure 11 This is a schematic diagram of the hardware entity of an electronic device to which an embodiment of the present invention applies. The electronic device provided in this embodiment includes a processor 1101, a communication interface 1102, a memory 1103, and a communication bus 1104, wherein the processor 1101, the communication interface 1102, and the memory 1103 communicate with each other via the communication bus 1104. Memory 1103 is used to store computer programs; When the processor 1101 executes the program stored in the memory 1103, it implements the steps in the above method embodiments.

[0081] The communication bus 1104 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1104 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0082] Communication interface 1102 is used for communication between the above-mentioned electronic device and other devices.

[0083] The memory 1103 may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory 1103 may also be at least one storage device located remotely from the aforementioned processor.

[0084] The processor 1101 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0085] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0086] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps provided in the above-described method embodiments.

[0087] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple. For specific details and beneficial effects, please refer to the description of the method embodiments.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus (devices), or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects, all of which are collectively referred to herein as "modules" or "systems." Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The computer program may be stored / distributed in a suitable medium, provided with or as part of other hardware, or may take other distribution forms, such as via the Internet or other wired or wireless telecommunications systems.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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 or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer 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.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer 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.

[0093] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for controlling transient wind load vibration of a reflector antenna support leg, characterized in that, include: The modal matrix is ​​extracted and a state-space model is established based on the structural parameters and physical property parameters of the support legs. The target actuator configuration is determined based on the modality matrix and the state-space model, and the target sensor configuration is determined based on the modality matrix. A feedback state vector is constructed based on the target sensor configuration, the modality matrix, and the state space model. A fluctuating wind speed time history is generated based on the wind field operating parameters, and a transient wind load time history is determined based on the fluctuating wind speed time history; LQR control weights are determined based on the transient wind load time history, and feedback control gain is determined based on the state space model and the LQR control weights. The actuator control force is determined based on the feedback control gain and the feedback state vector. The actuator control force is applied to the position of the target actuator and the system state is updated to obtain a controlled vibration response. The uncontrolled vibration response and iteration termination condition are determined based on the state-space model and the transient wind load time history; the vibration control evaluation index is determined based on the uncontrolled vibration response, the controlled vibration response and the actuator control force; and the antenna pattern evaluation index is determined based on the controlled vibration response. If either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted and the feedback control gain is updated based on the adjusted LQR control weights. The vibration control evaluation index and the antenna pattern evaluation index are then redefined based on the updated feedback control gain until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition.

2. The method for controlling transient wind load vibration of reflector antenna legs according to claim 1, characterized in that, The step of extracting the modal matrix and establishing a state-space model based on the support structure parameters and physical property parameters includes: A parameterized model is established based on the support structure parameters and the physical property parameters to obtain the mass matrix and stiffness matrix; Modal analysis is performed on the parameterized model to obtain the first six natural frequencies and the modal matrix. The damping matrix is ​​then determined based on the first six natural frequencies. Based on the mass matrix, stiffness matrix, damping matrix, modal matrix, and actuator parameters, the physical coordinates are converted into modal coordinates to obtain the state-space model.

3. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, Determining the target actuator configuration based on the modality matrix and the state-space model includes: Extract the actuator input matrix from the state space model, and determine the modal input matrix based on the modal matrix and the actuator input matrix; The L2 controllability index is determined based on the modal input matrix and candidate degrees of freedom. The candidate position pool is then selected according to the L2 controllability index, and candidate actuator configurations that meet the controllability ratio requirements of each modality are determined from the candidate position pool. Based on the candidate actuator configurations, the target actuator configuration is determined using a neighborhood-constrained particle swarm optimization algorithm with the optimization objectives of improving the overall controllability index and reducing the standard deviation of actuator output.

4. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, Determining the target sensor configuration based on the modality matrix includes: By filtering out the degrees of freedom in the preset filtering direction from the candidate degrees of freedom of the support legs, the candidate sensor positions in the lateral and axial directions are obtained; The modal output vector corresponding to each candidate sensor position is determined based on the modal matrix, and the H2 observability index is determined based on the modal output vector, the preset damping ratio, and the average of the first six natural frequencies extracted from the state space model. The high observability candidate locations are selected based on the observability index threshold, and the spatial distribution of the high observability candidate locations is selected based on the node spacing threshold to obtain the target sensor configuration.

5. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, The construction of the feedback state vector based on the target sensor configuration, the modality matrix, and the state space model includes: Acquire the sensor displacement data collected by each sensor in the target sensor configuration along the lateral and axial directions, and construct a sensor-modal mapping matrix based on the modal matrix; The sensor displacement data is fitted by least squares using the pseudo-inverse of the sensor-modal mapping matrix to obtain the first six modal displacements. Based on the state-space model and the first six modal displacements, the first six modal velocities are determined by Kalman filtering, and the first six modal displacements and the first six modal velocities are combined into the feedback state vector.

6. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, The wind field operating parameters include reference wind speed, friction velocity coefficient, and cutoff frequency; the step of generating a fluctuating wind speed time history based on the wind field operating parameters, and determining a transient wind load time history based on the fluctuating wind speed time history, includes: Based on the reference wind speed, friction velocity coefficient, and cutoff frequency, the wind speed spectrum is determined using the Davenport wind spectrum; the fluctuating wind speed time history is generated based on the wind speed spectrum and random phase using the inverse fast Fourier transform. The real-time wind speed is obtained by superimposing the base wind speed with the time history of the fluctuating wind speed. The transient wind load at each moment is determined based on the real-time wind speed, air density, wind pressure coefficient and windward area, and the transient wind load time history is obtained.

7. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, The process includes determining LQR control weights based on the transient wind load time history, determining feedback control gain based on the state-space model and the LQR control weights, determining actuator control force based on the feedback control gain and the feedback state vector, applying the actuator control force to the position of the target actuator and updating the system state to obtain a controlled vibration response, including: Real-time wind load intensity is extracted from the transient wind load time history. The state weight matrix and control force weight matrix are determined based on the real-time wind load intensity. The LQR control weight is determined based on the state weight matrix and control force weight matrix. The feedback control gain is obtained by solving the continuous-time algebraic Riccati equation based on the state-space model and the LQR control weights. The actuator control force is determined and limited based on the feedback control gain and the feedback state vector. The limited actuator control force is applied to the position of the target actuator. The system state is updated by the fourth-order Runge-Kutta integral to obtain the controlled vibration response.

8. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, The determination of the uncontrolled vibration response and iteration termination condition based on the state-space model and the transient wind load time history includes: Based on the state-space model and the transient wind load time history, numerical simulation is performed on the reflector antenna support leg without applied control force, and the displacement time history of the preset structural characterization node is extracted to obtain the uncontrolled vibration response. Based on the uncontrolled vibration response, preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss are determined respectively; the iteration termination condition is formed according to the preset performance targets corresponding to vibration suppression rate, actuator output standard deviation, antenna pattern main lobe shift, and antenna gain loss.

9. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 8, characterized in that, The step of determining vibration control evaluation indicators based on the uncontrolled vibration response, the controlled vibration response, and the actuator control force, and determining antenna pattern evaluation indicators based on the controlled vibration response, includes: The average vibration suppression rate is determined based on the displacement of the preset structural characterization node in the uncontrolled vibration response and the controlled vibration response, and the standard deviation of the actuator output force is determined based on the actuator control force to obtain the vibration control evaluation index. The maximum lateral displacement and the maximum axial displacement are extracted from the controlled vibration response to obtain the feed lateral defocusing amount and the feed axial defocusing amount. The lateral and axial offset patterns are determined based on the feed lateral offset and the feed axial offset, and the antenna pattern evaluation index is determined based on the main lobe offset, antenna gain loss, and half-power beamwidth in the lateral and axial offset patterns.

10. The method for controlling transient wind load vibration of reflector antenna support legs according to claim 1, characterized in that, When either the vibration control evaluation index or the antenna pattern evaluation index fails to meet the iteration termination condition, the LQR control weights are adjusted, the feedback control gain is updated based on the adjusted LQR control weights, and the vibration control evaluation index and the antenna pattern evaluation index are re-determined based on the updated feedback control gain, until both the vibration control evaluation index and the antenna pattern evaluation index meet the iteration termination condition, including: If any of the average vibration suppression rate and actuator output standard deviation in the vibration control evaluation index, or the main lobe offset and antenna gain loss in the antenna pattern evaluation index, fails to meet the iteration termination condition, the tuning parameters of the LQR control weights are adjusted to obtain the adjusted LQR control weights; the tuning parameters include the state weight base value, the control force weight base value, and the adaptive amplification factor or adaptive attenuation factor. The feedback control gain is updated according to the adjusted LQR control weights. The actuator control force is re-determined based on the updated feedback control gain and the feedback state vector. The system state is then updated to obtain the updated controlled vibration response. The vibration control evaluation index is re-determined based on the uncontrolled vibration response, the updated controlled vibration response, and the re-determined actuator control force; and the antenna pattern evaluation index is re-determined based on the updated controlled vibration response. When the average vibration suppression rate, the actuator output standard deviation, the main lobe offset, and the antenna gain loss all satisfy the iteration termination condition, the target actuator configuration, the target sensor configuration, and the current state weight base value, the control force weight base value, the adaptive amplification coefficient, and the adaptive attenuation coefficient are determined as the optimal control scheme.

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