Verification method, device and equipment of carrier rocket load shedding control algorithm, medium and product

Abstract

The invention discloses a carrier rocket load shedding control algorithm verification method and device, equipment, a medium and a product, and relates to the technical field of spacecraft control, and the method comprises the steps: building a carrier rocket entity model located in a wind field simulation system, and simulating an actual wind disturbance environment of a rising section; constructing a dynamic model based on the entity model; obtaining an initial position coordinate, and starting a wind field simulation system according to a preset wind speed parameter; using a load shedding control algorithm to control the position and attitude of the model so as to obtain motion response data; wind speed parameters are changed and iterated until a threshold value is met, and multiple groups of data are obtained; and finally, calculating verification results such as a wind speed reduction rate and a positioning error value through a statistical method. Through a ground low-cost test scheme, an evaluable test environment is established, the problem that the real environment of a load shedding algorithm is difficult to verify is solved, the algorithm performance can be comprehensively and accurately verified, and the flight safety and reliability of a carrier rocket are improved.

Description

Technical Field

[0001] This application relates to the field of spacecraft control technology, and in particular to a verification method, apparatus, equipment, medium and product for a launch vehicle load reduction control algorithm. Background Technology

[0002] When a launch vehicle flies in the high dynamic pressure zone, it generates aerodynamic loads under the action of wind. In order to achieve attitude stability and tracking, the attitude control system needs to generate control torque to balance the aerodynamic loads. The rocket body structure will bear the bending moment formed by the interaction of the two, so it is necessary to increase the structural strength.

[0003] As rocket launch missions become increasingly complex, the requirements for structural efficiency and payload capacity are constantly rising. To ensure smooth passage through high dynamic pressure zones under limited structural strength constraints, active load shedding control technology has been extensively studied. However, due to the single-use nature of rockets and the difficulty in reproducing the ascent phase environment, testing of load shedding algorithms in related technologies has remained largely confined to simulation testing.

[0004] Therefore, there is an urgent need to develop a low-cost ground-based testing program and establish an assessable testing environment to verify the performance of load reduction control. Summary of the Invention

[0005] The purpose of this application is to provide a verification method, device, equipment, medium, and product for a launch vehicle load reduction control algorithm, which can establish a ground-based test environment for evaluation, and achieve comprehensive and accurate verification of the performance of the load reduction control algorithm at low cost, thereby improving the safety and reliability of the launch vehicle during flight and meeting the requirements of complex launch missions.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] Firstly, this application provides a verification method for a launch vehicle load reduction control algorithm, including:

[0008] A physical model of the launch vehicle and a wind field simulation system are constructed; the physical model of the launch vehicle is located in the wind field simulation system; the wind field simulation system is used to simulate the actual wind disturbance environment during the ascent phase of the launch vehicle.

[0009] Based on the physical model of the launch vehicle, a dynamic model of the physical model of the launch vehicle is constructed;

[0010] Obtain the initial position coordinates of the initial position of the launch vehicle physical model, and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, wind speed fluctuation range, and wind speed spatial distribution value.

[0011] Based on the dynamic model of the launch vehicle physical model, the position and attitude of the launch vehicle physical model in the wind field simulation system are controlled by the load reduction control algorithm to obtain motion response data;

[0012] Change the preset wind speed parameters, return to the steps of "obtain the initial position coordinates of the initial position of the launch vehicle entity model and start the wind field simulation system according to the preset wind speed parameters" until the preset iteration threshold is met, and obtain multiple sets of motion response data under different wind speed parameters;

[0013] Based on multiple sets of motion response data under different wind speed parameters, the verification results of the load reduction control algorithm were obtained through statistical methods.

[0014] Secondly, this application provides a verification device for a launch vehicle load reduction control algorithm, comprising:

[0015] The dynamics model building module is used to build a dynamics model of the launch vehicle based on the launch vehicle physical model.

[0016] The wind field simulation module is used to obtain the initial position coordinates of the initial position of the launch vehicle physical model and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, the wind speed fluctuation range, and the wind speed spatial distribution value.

[0017] The control and data acquisition module is used to control the position and attitude of the launch vehicle physical model in the wind field simulation system based on the dynamic model of the launch vehicle physical model, and obtain motion response data by using the load reduction control algorithm.

[0018] The parameter iteration module is used to change the preset wind speed parameters and return to the wind field simulation module until the preset iteration threshold is met, so as to obtain multiple sets of motion response data under different wind speed parameters.

[0019] The verification result acquisition module is used to calculate the verification results of the load reduction control algorithm by statistical methods based on multiple sets of motion response data under different wind speed parameters.

[0020] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the verification method for the launch vehicle load reduction control algorithm described in any one of the above.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the verification method for the launch vehicle load reduction control algorithm described in any one of the above-mentioned applications.

[0022] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the verification method for the launch vehicle load reduction control algorithm described in any one of the above descriptions.

[0023] According to the specific embodiments provided in this application, this application has the following technical effects:

[0024] This application provides a verification method, apparatus, equipment, medium, and product for a launch vehicle load reduction control algorithm. By constructing a physical model of the launch vehicle and a wind field simulation system, and placing the physical model of the launch vehicle within the wind field simulation system, a near-realistic and economical test scenario is built for subsequent verification of the load reduction control algorithm. This solves the problem that the algorithm cannot be tested under real conditions due to the unreproducible actual wind disturbance environment during the launch vehicle's ascent phase and the high cost of large-scale simulation, thus achieving the goal of providing a reliable and low-cost verification environment. A dynamic model of the launch vehicle is constructed based on the physical model, solving the problem of not being able to accurately analyze the algorithm's effect due to the lack of an accurate model, and achieving the goal of providing an accurate theoretical model for subsequent analysis of the algorithm's impact on rocket motion. The initial position coordinates of the initial position of the launch vehicle physical model are obtained, and the wind field simulation system is started according to preset wind speed parameters including initial wind speed, wind speed fluctuation range, and wind speed spatial distribution values. Different combinations of wind speed parameters can simulate various actual wind conditions. By changing these parameters and conducting multiple experiments, the limitation of verifying the algorithm only under a single wind condition is overcome, achieving a comprehensive examination of the algorithm's performance under different wind conditions. Based on the dynamic model of the launch vehicle's physical model, a load reduction control algorithm is used to control the position and attitude of the launch vehicle's physical model in a wind field simulation system, obtaining motion response data. Multiple sets of motion response data under different wind speed conditions are obtained by iterating through preset wind speed parameters until a preset iteration threshold is met. Statistical methods are then used to calculate verification results including wind speed reduction rate and positioning error. The use of multiple sets of data and statistical methods solves the problems of inaccurate and unrepresentative verification results, achieving an accurate and comprehensive evaluation of the load reduction control algorithm's performance. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an application environment diagram of a verification method for a launch vehicle load reduction control algorithm in one embodiment of this application;

[0027] Figure 2A flowchart illustrating a verification method for a launch vehicle load reduction control algorithm provided in an embodiment of this application;

[0028] Figure 3 A schematic diagram of a physical model of a launch vehicle provided in an embodiment of this application;

[0029] Figure 4 A schematic diagram of the wind speed distribution in a wind field simulated by a wind speed simulation system provided in an embodiment of this application;

[0030] Figure 5 A schematic diagram of the functional modules of a verification device for a launch vehicle load reduction control algorithm provided in an embodiment of this application;

[0031] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] The verification method for the launch vehicle load reduction control algorithm provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the initial position coordinates of the launch vehicle physical model and preset wind speed parameters to server 104. After receiving the initial position coordinates and preset wind speed parameters, server 104 starts the wind field simulation system according to the preset wind speed parameters. The preset wind speed parameters include initial wind speed, wind speed fluctuation range, and wind speed spatial distribution value. Based on the dynamic model of the launch vehicle physical model, the position and attitude of the launch vehicle physical model in the wind field simulation system are controlled by a load reduction control algorithm to obtain motion response data. The preset wind speed parameters are changed, and the process of "obtaining the initial position coordinates of the launch vehicle physical model and starting the wind field simulation system according to the preset wind speed parameters" is repeated until a preset iteration threshold is met, obtaining multiple sets of motion response data under different wind speed parameters. Based on the multiple sets of motion response data under different wind speed parameters, the verification result of the load reduction control algorithm is calculated by statistical methods. Server 104 can feed back the obtained verification result of the load reduction control algorithm to terminal 102. Furthermore, in some embodiments, the verification method for the launch vehicle load reduction control algorithm can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly verify the load reduction control algorithm based on the initial position coordinates of the initial position of the launch vehicle physical model and the preset wind speed parameters. Alternatively, the server 104 can obtain the initial position coordinates of the initial position of the model and the preset wind speed parameters from the data storage system, and verify the load reduction control algorithm based on the initial position coordinates of the initial position of the model and the preset wind speed parameters.

[0035] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0036] In one exemplary embodiment, such as Figure 2 As shown, a verification method for a launch vehicle load reduction control algorithm is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 206. Wherein:

[0037] Step 201: Construct a physical model of the launch vehicle and a wind field simulation system; the physical model of the launch vehicle is located in the wind field simulation system; the wind field simulation system is used to simulate the actual wind disturbance environment during the ascent phase of the launch vehicle.

[0038] Step 202: Based on the physical model of the launch vehicle, construct the dynamic model of the physical model of the launch vehicle.

[0039] Step 203: Obtain the initial position coordinates of the initial position of the launch vehicle physical model, and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, wind speed fluctuation range and wind speed spatial distribution value.

[0040] Step 204: Based on the dynamic model of the launch vehicle physical model, the position and attitude of the launch vehicle physical model in the wind field simulation system are controlled by the load reduction control algorithm to obtain motion response data; the load reduction control algorithm includes wind speed estimation algorithm, load reduction control algorithm and trajectory tracking control algorithm.

[0041] Step 205: Change the preset wind speed parameters, return to the step of "obtain the initial position coordinates of the initial position of the launch vehicle physical model and start the wind field simulation system according to the preset wind speed parameters" until the preset iteration threshold is met, and obtain multiple sets of motion response data under different wind speed parameters.

[0042] Step 206: Based on multiple sets of motion response data under different wind speed parameters, the verification results of the load reduction control algorithm are calculated using statistical methods; the verification results include the wind speed reduction rate and the positioning error value.

[0043] By implementing steps 201 to 206 above, this application can provide a reliable and comprehensive verification environment for launch vehicle load reduction control algorithms at low cost, effectively improving the accuracy and economy of algorithm verification, and providing strong protection for the flight safety and reliability of launch vehicles.

[0044] In another exemplary embodiment of this application, step 204 specifically includes:

[0045] Based on the dynamic model of the launch vehicle entity model, the wind speed estimate at the current moment is obtained by estimating the wind speed at the wind field location where the launch vehicle entity model is located.

[0046] If the estimated wind speed at the current moment is greater than or equal to the preset wind speed threshold, then the estimated wind speed at the current moment is used as the wind speed before hovering, causing the launch vehicle physical model to shift to a position in a weak wind zone with a wind speed lower than the preset wind speed threshold, until the estimated wind speed at the current moment is lower than the preset wind speed threshold and hovering occurs, thus obtaining the hovering wind speed and the coordinates of the first hovering position of the launch vehicle physical model.

[0047] After the physical model of the launch vehicle hovers, the wind field simulation system is turned off, allowing the physical model of the launch vehicle to return to its initial position and hover, thus obtaining the coordinates of the second hovering position.

[0048] The motion response data includes wind speed before hovering, hovering wind speed, coordinates of the first hovering position, and coordinates of the second hovering position.

[0049] In another exemplary embodiment of this application, the verification results include wind speed reduction rate and positioning error value.

[0050] Based on multiple sets of motion response data under different wind speed parameters, the verification results of the load reduction control algorithm were calculated using statistical methods, specifically including:

[0051] Calculate the difference between the wind speed before hovering and the corresponding hovering wind speed in each set of motion response data to obtain multiple sets of wind speed reduction.

[0052] The wind speed reduction rate is obtained by averaging the wind speed reduction of all groups.

[0053] Calculate the Euclidean distance difference between the second hovering position coordinates and the initial position in each set of motion response data to obtain the position difference.

[0054] The root mean square error of the position differences of all groups is calculated to obtain the positioning error value.

[0055] In another exemplary embodiment of this application, based on the dynamic model of the launch vehicle physical model, the wind speed estimate at the current moment is obtained by estimating the wind speed at the wind field location where the launch vehicle physical model is located, specifically including:

[0056] The wind direction is used as the x-axis of the launch vehicle physical model, and the direction of the wind disturbance torque is used as the y-axis of the launch vehicle physical model.

[0057] Selecting state variables Control torque Substituting the dynamic model of the launch vehicle solid model into the dynamic model, we obtain the state-space form of the dynamic model of the launch vehicle solid model:

[0058]

[0059] Where x represents the state vector; q represents the attitude angle; Indicates the rate of change of attitude angle; The second derivative of the attitude angle is represented by T; the transpose is represented by M. k Indicates control torque; δ q Indicates rudder deflection angle; k δ X represents the normal force generated by a unit rudder deflection; k Indicates the distance from the point of application of the control force to the nose of the launch vehicle; Xcg Indicates the distance from the center of mass to the nose of the launch vehicle; I y A represents the moment of inertia along the y-axis of the launch vehicle; B represents the system matrix of the state system; C represents the input matrix of the state system. denoted by f, which represents the disturbance torque generated by wind acting on the launch vehicle body; and b3, which represents the value of the disturbance torque generated by wind acting on the launch vehicle body.

[0060] Based on the state-space form of the dynamic model of the launch vehicle physical model, an extended state system is constructed to obtain an estimate of the wind disturbance torque.

[0061] Based on the estimated wind disturbance moment, the estimated wind speed is calculated using the following formula:

[0062]

[0063] in, This represents the estimated wind speed. X represents the estimated value of the wind disturbance torque; A Indicates the distance from the aerodynamic point to the nose of the launch vehicle; X cg C represents the distance from the center of mass to the nose of the launch vehicle; D ρ represents the drag coefficient of the launch vehicle; ρ represents the atmospheric density.

[0064] In another exemplary embodiment of this application, based on the state-space form of the dynamic model of the launch vehicle entity model, an extended state system is constructed to obtain an estimate of the wind disturbance torque, specifically including:

[0065] Based on the state-space form of the dynamic model of the launch vehicle physical model, an extended state system is constructed, letting... The extended state equation is obtained based on the following formula:

[0066]

[0067] Based on the extended state equations, an extended state observer is constructed using the following formula to obtain an estimate of the wind disturbance torque:

[0068]

[0069] In the formula, x o Represents the expanded state vector; Indicates the rate of change of attitude angle; For x o The first derivative with respect to time represents the dynamic changes of each state variable in the expanding state vector over time; A oThe system matrix represents the extended state system; B o The input matrix represents the extended state system; u represents the control input. This represents the state estimation vector of the extended state observer. This represents an estimated value of the attitude angle; This represents an estimated value of the rate of change of attitude angle; This represents an estimated value of the wind disturbance torque; For Z o The first derivative with respect to time indicates how quickly the state estimation vector of the extended state observer changes with time; ω represents the observer gain matrix. o y represents the observer control bandwidth; y represents the system output of the extended state observer. This represents the output matrix of the extended state observer.

[0070] In another exemplary embodiment of this application, shifting the physical model of the launch vehicle to a location in a weak wind zone with a wind speed less than a preset threshold specifically includes:

[0071] Based on the preset wind speed parameters of the wind field simulation system, the load mitigation attitude control command for the physical model of the launch vehicle is calculated using the following formula.

[0072]

[0073] Where, δ q (t) represents the load reduction attitude control command at time t; Represents the inverse Laplace transform; ω Cn ξ represents the natural frequency of the filter; s represents the complex variable in the Laplace transform; Cn δ represents the damping ratio of the filter. q-in (s) represents the attitude control input command in the Laplace frequency domain; δ q-in Indicates attitude control input commands; K Gp K Gd and K Gi These represent the proportional gain parameter, differential gain parameter, and integral gain parameter, respectively; q represents the attitude angle; q cmd Indicates command attitude parameters; q represents the rate of change of attitude angle; cmd (s) represents the command attitude parameter in the Laplace frequency domain; q cmd-in (s) represents the input command attitude parameters in the Laplace frequency domain; ξ Gn ω represents the damping ratio of the filter. Gn q represents the filter's natural frequency. cmd-in Indicates the input command attitude parameter; Δx cmdThis represents the position offset that needs to be generated at the current moment; K represents the x-axis offset velocity. VW Indicates the load control gain; V represents the estimated wind speed. w-max This indicates the preset wind speed threshold.

[0074] According to the load reduction attitude control command, the physical model of the launch vehicle is shifted to a position in a weak wind zone with a wind speed lower than the preset wind speed threshold.

[0075] In another exemplary embodiment of this application, a verification method for a launch vehicle load reduction control algorithm is provided, specifically including:

[0076] Step 1: Mathematical Modeling of the Rocket Simulation Indoor Verification Platform. In Step 1, the mathematical modeling of the rocket simulation indoor verification platform involves designing an indoor verification platform with dynamic characteristics similar to a launch vehicle. Key dynamic characteristic parameters, such as mass, moment of inertia, thrust, and control force, are determined through experiments, and a mathematical model of the rocket simulation indoor verification platform is established.

[0077] As an optional implementation, this application designs a low-cost rocket prototype for indoor flight testing of the load reduction algorithm. A model diagram of the rocket prototype is shown below. Figure 3 As shown, the prototype rocket's frame structure consists of a carbon fiber cylinder, a cover plate, and four supports. The battery and microprocessor are located on the outside of the cylinder, while a pair of counter-rotating propellers are mounted in the center. Four servo motors on the lower side of the cylinder control four cross-shaped rudder blades, which together form the aerodynamic control system. The upper and lower propellers rotate at the same speed, counteracting each other to cancel out the helical torque generated by a single blade. The thrust is controlled by adjusting the propeller speed, and the thrust direction is controlled by adjusting the deflection angle of the aerodynamic control system, thus simulating the thrust vector control of a rocket engine, and consequently controlling the rocket's attitude and position.

[0078] like Figure 3 The physical model of the launch vehicle shown retains the following characteristics of the actual rocket:

[0079] 1. Rocket attitude is controlled solely by thrust vectoring.

[0080] 2. The swing angle of the actuator is constrained.

[0081] 3. Instability caused by rocket structural errors.

[0082] Based on the aforementioned physical model of the launch vehicle, the dynamic model of the simulated rocket indoor verification platform is derived. First, the components of some key physical quantities in the x-axis, y-axis, and z-axis of the ground coordinate system are defined.

[0083] Let the components of the position vector from the origin of the ground coordinate system to the center of mass of the rocket along the x, y, and z axes of the ground coordinate system be:

[0084] p = [p x p y p z ] T .

[0085] The components of the rocket's center of mass relative to the origin of the ground coordinate system along the x, y, and z axes are:

[0086] v = [v x v y v z ] T .

[0087] Let the components of the rocket's center of mass acceleration relative to the origin of the ground coordinate system along the x, y, and z axes of the ground coordinate system be:

[0088] a = [a x a y a z ] T .

[0089] Let the components of gravitational acceleration along the x, y, and z axes of the ground coordinate system be:

[0090] g = [0 0 g] T .

[0091] Let the components of the total resultant force along the x-axis, y-axis, and z-axis of the body coordinate system be:

[0092] F = [F x F y F z ] T .

[0093] The rocket's position dynamics model is then:

[0094]

[0095] Where p represents the components of the position vector from the origin of the ground coordinate system to the rocket's center of mass along the x, y, and z axes of the ground coordinate system; p x p x and p z Let represent the components of the position vector from the origin of the ground coordinate system to the rocket's center of mass along the x, y, and z axes of the ground coordinate system, respectively; T represents the transpose; v represents the components of the velocity of the rocket's center of mass relative to the origin of the ground coordinate system along the x, y, and z axes of the ground coordinate system; v x v y and v z... x a y and a z These represent the components of the acceleration of the launch vehicle's center of mass relative to the origin of the ground coordinate system along the x, y, and z axes, respectively; g represents the components of gravitational acceleration along the x, y, and z axes of the ground coordinate system; g represents the component of gravitational acceleration along the z-axis of the ground coordinate system, i.e., the magnitude of gravitational acceleration, which is usually along a certain coordinate axis (such as the z-axis) in the ground coordinate system; F represents the components of the total resultant force along the x, y, and z axes of the launch vehicle's solid model's body coordinate system; F x F y and F z These represent the components of the total resultant force along the x-axis, y-axis, and z-axis of the body coordinate system, respectively. The velocity of the rocket's center of mass relative to the origin of the ground coordinate system represents the rate of change of velocity; m represents the mass of the launch vehicle's physical model; L gb L represents the transformation matrix from the body coordinate system of the launch vehicle's physical model to the ground coordinate system. In the derivation of the dynamic equations, since the resultant force F is given in the body coordinate system while the gravitational acceleration g is defined in the ground coordinate system, a transformation matrix from the ground coordinate system to the body coordinate system is needed to transform F to the ground coordinate system for synthesis with mg. Based on the properties of coordinate transformation matrices, the transformation matrix L from the ground coordinate system to the body coordinate system is... bg It is L gb The inverse matrix, i.e., L bg =L gb -1 Its specific expression is:

[0096]

[0097] in, Represents rotation about the x-axis in the ground coordinate system Rotation matrix of angle; R y (θ) represents the rotation matrix that rotates the object by an angle θ around the y-axis in the ground coordinate system; R z (ψ) represents the rotation matrix that rotates by an angle ψ around the z-axis in the ground coordinate system; by using L bg This ensures that the composition of forces takes place in the correct coordinate system, thus yielding accurate dynamic equations.

[0098] In this implementation method, the thrust generated by the propeller rotation is F. p =[0 0 -F p ]T The control force generated by the air rudder is F k =[F kx F ky F kz ] T The aerodynamic force generated by the wind acting on the body is F. A =[F Ax F Ay F Az ] T , where -F p F represents the magnitude of the thrust generated by the propeller rotation along the negative z-axis of the body coordinate system. kx F ky and F kz These represent the components of the control force generated by the aerodynamic rudder in the x, y, and z axes of the body coordinate system, respectively; F Ax F Ay and F Az Let F represent the components of the aerodynamic force generated by wind acting on the body along the x-axis, y-axis, and z-axis of the body coordinate system, respectively; then the expression for F in the above formula is:

[0099] F = F p +F k .

[0100] Let the rocket attitude angle be... θ and ψ represent the roll angle, pitch angle, and yaw angle, respectively. The components of the rocket's angular velocity along the x, y, and z axes of the body coordinate system are ω = [ω x ω y ω z ] T , where ω x ω y and ω z Let ω and q represent the components of the rocket's angular velocity along the x-axis, y-axis, and z-axis of the body coordinate system, respectively. The relationship between ω and q is easily obtained as follows:

[0101]

[0102] in, and These represent the rates of change of roll angle, pitch angle, and yaw angle, respectively; A q The attitude angle transformation matrix is ​​used to establish the relationship between the rocket's attitude angle change rate and angular velocity; after appropriate transformation, the rocket attitude kinematic model can be obtained as follows:

[0103]

[0104] in, Indicates the rate of change of attitude angle; A q-1 A represents q The inverse matrix; the sum of the moments acting on the rocket prototype relative to its center of mass, in the x, y, and z axes of the body coordinate system, is M = [M x M y M z ] T M x M y and M z Let M represent the components of the torque M along the x-axis, y-axis, and z-axis of the body coordinate system, respectively; clearly, the rocket's attitude dynamics model is:

[0105]

[0106] Where I represents the moment of inertia matrix of the physical model of the launch vehicle; ω represents the angular acceleration vector of the launch vehicle solid model; ω represents the angular velocity vector of the launch vehicle solid model; M represents the components of the sum of the torques acting on the launch vehicle solid model relative to its center of mass along the x, y, and z axes in the body coordinate system. Due to the propeller thrust F... p It passes through the center of mass, therefore no torque is generated. The torque M is entirely generated by the aerodynamic rudder, i.e., M = M k Control torque M k =[M kx M ky M kz ] T M kx M ky and M kz These represent the components of the control torque along the x, y, and z axes of the body coordinate system, respectively. Expanding, we get:

[0107]

[0108] in, and These represent the angular acceleration components along the x-axis, y-axis, and z-axis of the body coordinate system, respectively; I x I y and I z These represent the moments of inertia about the x-axis, y-axis, and z-axis of the body coordinate system, respectively.

[0109] In summary, the motion model of the rocket prototype is as follows:

[0110]

[0111] The mass m of the launch vehicle physical model and the moment of inertia matrix I of the launch vehicle physical model were obtained through experiments.

[0112] Step Two: Design of the Load Reduction Control Algorithm for the Indoor Verification Platform Simulated by a Rocket. In Step Two, the design of the load reduction control algorithm for the indoor verification platform simulated by a rocket is conducted. Based on the characteristics of the load reduction control mission of a launch vehicle, an indoor flight control algorithm with the same characteristics is designed, including a wind estimation algorithm, a load reduction control algorithm, and a trajectory tracking algorithm.

[0113] As an optional implementation, the load reduction control algorithm for the simulated rocket indoor verification platform comprises the following three parts: 1) wind speed estimation algorithm; 2) load mitigation control algorithm; and 3) trajectory tracking control algorithm. This load reduction control algorithm is used to verify the design method of load reduction control during the ascent phase of the launch vehicle, in order to evaluate its effectiveness and applicability.

[0114] 2.1 Wind speed estimation algorithm.

[0115] In the experiment, the wind field direction was set along the x-axis of the launch vehicle. At this time, the direction of the wind disturbance torque was the y-axis of the launch vehicle. Therefore, the wind speed estimation algorithm was designed based on the y-axis direction of the launch vehicle.

[0116] Selecting state variables Control torque Substituting these values ​​into the final motion model of the rocket prototype in step one, and assuming that the higher-order terms of angular velocity are small quantities, we obtain the state-space form of the dynamic model of the launch vehicle entity model. The dynamic equations of the spacecraft can then be rewritten as:

[0117]

[0118] In the formula, The value of the disturbance torque generated by wind acting on the body of the launch vehicle is represented by f;

[0119]

[0120] Where x represents the state vector; q represents the attitude angle; Indicates the rate of change of attitude angle; The second derivative of the attitude angle is represented, i.e., the acceleration of the attitude angle; T represents the transpose; M represents the second derivative of the attitude angle. k Indicates control torque; δ θ Indicates rudder deflection angle; k δ This represents the normal force generated per unit rudder deflection; this value is obtained experimentally. k Indicates the distance from the point of application of the control force to the nose of the launch vehicle; X cg Indicates the distance from the center of mass to the nose of the launch vehicle; I yThe value represents the moment of inertia along the y-axis of the launch vehicle. All of these values ​​were obtained experimentally and are known quantities. A represents the system matrix of the state system, which is used to describe the dynamic relationship between state variables. B represents the input matrix of the state system. denoted by f, which represents the disturbance torque generated by wind acting on the launch vehicle body; and b3, which represents the value of the disturbance torque generated by wind acting on the launch vehicle body.

[0121] Based on the state-space form of the dynamic model of the launch vehicle physical model, an extended state system is constructed, letting... The extended state equation is obtained based on the following formula:

[0122]

[0123] Based on the extended state equations, an extended state observer is constructed using the following formula to obtain an estimate of the wind disturbance torque:

[0124]

[0125] In the formula, x o Represents the expanded state vector; Indicates the rate of change of attitude angle; For x o The first derivative with respect to time represents the dynamic changes of each state variable in the expanding state vector over time; A o The system matrix represents the extended state system; B o The input matrix represents the extended state system; u represents the control input. This represents the state estimation vector of the extended state observer, for x o The estimate; This represents an estimated value of the attitude angle; This represents an estimated value of the rate of change of attitude angle; This represents an estimated value of the wind disturbance torque; For Z o The first derivative with respect to time indicates how quickly the state estimation vector of the extended state observer changes with time; ω represents the observer gain matrix. o represents the observer control bandwidth, which is a parameter for adjustment; y represents the system output of the extended state observer. This represents the output matrix of the extended state observer.

[0126] According to the formula The estimated value of the wind disturbance moment is The estimated wind speed can then be determined using the following formula:

[0127]

[0128] In the formula, This represents the estimated wind speed. X represents the estimated value of the wind disturbance torque; A Indicates the distance from the aerodynamic point to the nose of the launch vehicle; X cg C represents the distance from the center of mass to the nose of the launch vehicle; D ρ represents the drag coefficient of the launch vehicle, obtained experimentally; ρ represents the atmospheric density, which can be taken as 1.225 kg / m³ at horizontal surfaces. 3 .

[0129] At this point, the wind speed estimation is complete, and the result will be used in the load mitigation control algorithm and the trajectory tracking control algorithm.

[0130] 2.2 Load mitigation control algorithm.

[0131] Set the maximum peak wind speed that the aircraft can withstand (preset wind speed threshold) V w-max This value reflects the maximum load that the rocket body can withstand during the ascent phase.

[0132] When the calculated wind speed estimate At this time, the load mitigation control algorithm starts working, and based on the preset wind field information, it manipulates the aircraft to move towards a weak wind area (away from the wind field). The guidance command for the position at this time is:

[0133]

[0134] In the formula, Δx cmd K represents the position offset that needs to be generated at the current moment. VW This represents the load control gain, which is a parameter for adjustment.

[0135]

[0136] In the formula, q cmd-in This represents the input instruction attitude parameters, which are at the beginning of the attitude instruction generation chain, and are used to generate instruction attitude parameters q. cmd (s) Provides initial information based on position. It preliminarily determines the desired attitude commands based on the rocket's position status, playing a crucial role in connecting position control and attitude control; K Gp K Gd and K Gi These represent the proportional gain parameter, the derivative gain parameter, and the integral gain parameter, respectively, which are the parameters to be adjusted. The x-axis offset velocity is used to calculate the guidance command. After this calculation, the value needs to be passed through a low-pass filter. A second-order filter is selected to obtain the final guidance output command:

[0137]

[0138] In the formula, q cmd (s) represents the command attitude parameter in the Laplace frequency domain; q cmd-in (s) represents the input command attitude parameters in the Laplace frequency domain; ξ Gn ω represents the damping ratio of the filter. Gn The natural frequency of the filter is represented by s; s is a complex variable in the Laplace transform, used to convert a time-domain signal to a frequency-domain signal. This means that after performing a Laplace transform on the original system to convert the time domain to the frequency domain and obtain the frequency domain result of the command attitude parameter output, it is necessary to convert it back to a discrete time-domain output. The forward and inverse Laplace transforms are well-known concepts in this field, and the formulas are... The discrete transfer function expression is:

[0139]

[0140] In the formula, H() represents the discrete transfer function; b0, b1, b2, a1, and a2 can all be obtained through bilinear transformation, and represent the z in the numerator polynomial, respectively. 0 The coefficients of the terms, z in the numerator polynomial -1 The coefficients of the terms, z in the numerator polynomial -2 The coefficients of the terms, z in the denominator polynomial -1 The coefficients of the terms, z in the denominator polynomial -2 The coefficient of the term; z -1 z -2 Let T represent the input signal of the previous sampling period and the input signal of the previous two sampling periods in the discrete control system of the launch vehicle, respectively; assuming the sampling period is T, the above coefficient expressions are as follows:

[0141]

[0142] The attitude control input command is:

[0143]

[0144] Where, δ q-in Indicates attitude control input commands; K Gp K Gd and K Gi These represent the proportional gain parameter, differential gain parameter, and integral gain parameter, respectively; q represents the attitude angle; q cmd This represents the commanded attitude parameter, i.e., the desired attitude angle of the launch vehicle. It is a target attitude value predetermined or calculated in real-time based on factors such as the rocket's flight mission requirements, orbital settings, and external disturbances. As a reference value for attitude control, the actual attitude angle q of the rocket needs to be continuously adjusted to approach q0. cmdThis allows for precise attitude control, ensuring the rocket flies along its predetermined trajectory. represents the rate of change of attitude angle; D represents an intermediate variable used in calculating the transfer function coefficients; after obtaining the attitude control input command, it is passed through a low-pass filter of an attitude loop to obtain the load mitigation attitude control command in the Laplace frequency domain of the attitude control system:

[0145]

[0146] Where, δ q (s) represents the load mitigation attitude control command in the Laplace frequency domain; δ q-in This represents the attitude control input command, used to control the rocket's attitude; it is used to generate the final attitude control command δ. q (t) provides the key input. It reflects the deviation of the rocket's attitude in real time. The attitude control system adjusts the control input in real time based on this input to make the actual attitude of the rocket as close as possible to the commanded attitude, ensuring the accuracy and stability of attitude control. After discretization of the transfer function, the time-domain load mitigation attitude control command q is obtained and input to the servo motor. θ (t), the method is the same as above, and will not be repeated here.

[0147] 2.3 Trajectory tracking control algorithm.

[0148] When the calculated wind speed estimate At this time, the trajectory tracking control algorithm starts working, and based on the preset positioning point information, it controls the aircraft to move towards the positioning point. At this time, the calculated values ​​of the input command attitude parameters are:

[0149]

[0150] In the formula, x0 represents the positioning point information, which is a known value. After obtaining the calculated values ​​of the input command attitude parameters, they need to be passed through a low-pass filter. The filter type is selected as a second-order element, and the final command attitude parameters in the Laplace frequency domain are as follows:

[0151]

[0152] Then, by applying the inverse Laplace transform, the command attitude parameters θ in the time domain at time t are obtained. cmd (t).

[0153] The attitude control input command is:

[0154]

[0155] After receiving the attitude control input command, the load mitigation attitude control command in the Laplace frequency domain of the attitude control system is obtained after low-pass filtering through an attitude loop:

[0156]

[0157] At this point, the design of the load reduction control algorithm for the simulated rocket indoor verification platform is complete.

[0158] Step 3: Indoor Verification Test Design of Launch Vehicle Load Reduction Control Algorithm. In Step 3, the indoor verification test design of the launch vehicle load reduction control algorithm involves creating an indoor wind field environment using fans to simulate the wind field during the launch vehicle's ascent phase. The magnitude of the indoor flight platform's movement towards a weaker wind zone within the wind field is used to represent the payload scheduling performance of a real rocket. After shutting down the fans, the positioning accuracy of the indoor flight platform returning to its initial point is used to represent the trajectory tracking performance of a real rocket. An indoor verification test scheme for the load reduction algorithm is designed to comprehensively evaluate its payload control and trajectory tracking capabilities.

[0159] 3.1 Experimental conditions and environment setup.

[0160] The software and hardware equipment required for indoor flight testing are shown in Table 1 below.

[0161] Table 1. Equipment and Software Configuration Required for Indoor Flight Testing

[0162] Serial Number Product Name unit quantity 1 Airborne controller tower 1 2 power supply tower 1 3 Rotor test platform set 1 4 High-power fan tower 1 5 Indoor optical positioning system set 1 6 Airborne inertial combination unit tower 1 7 Ground simulation computer tower 1 8 Data transmission system set 1 9 Flight control program code set 1

[0163] The flight environment consists of a fan and an indoor positioning system. The fan generates an indoor wind field to simulate the wind field during actual rocket flight; the indoor positioning system simulates satellite navigation information to provide precise positioning for the spacecraft.

[0164] After the wind turbine is turned on, it operates at a constant power. An ultrasonic anemometer is used to calibrate the wind field, which serves as the preset information for the load reduction algorithm. The wind field distribution is shown in Table 2 below.

[0165] Table 2 Wind field distribution data based on ultrasonic calibration

[0166] Distance (m) 1.0 1.5 2.0 2.5 3.0 Wind speed (m / s) 7.4059 7.1390 5.8838 4.9303 4.0061 Wind direction (°) 338.14 342.6 346.8 348.26 343.17

[0167] The relative relationship between wind speed and location The forecast wind field information is used by the load reduction control algorithm of the rotorcraft flight platform.

[0168] 3.2 Indoor verification test of launch vehicle load reduction control algorithm.

[0169] The relationship between flight test indicators and tracking accuracy indicators in the launch vehicle ascent phase load reduction control problem: The initial position x0 is defined as 1 meter away from the wind turbine. The distance between the rotor experimental platform and the initial position is used as the optimization variable to simulate the tracking accuracy of the launch vehicle ascent phase. The closer the distance is to the initial position x0, the higher the tracking accuracy in the launch vehicle ascent phase load reduction control problem.

[0170] The relationship between flight test indicators and load control indicators in the problem of load reduction control during the ascent phase of a launch vehicle: Wind speeds above 5 m / s are considered high-wind areas, and wind speeds below 5 m / s are considered low-wind areas. A schematic diagram of the wind speed distribution is shown below. Figure 4 As shown, the wind speed at which the platform hovers is used as an optimization variable to simulate the load conditions during the ascent phase of the launch vehicle. The lower the wind speed at its location, the smaller the load in the load reduction control problem during the ascent phase of the launch vehicle.

[0171] Experimental steps: Set the load reduction flag in the load reduction control algorithm. JZ Set to 1, which sets the maximum wind speed the aircraft can withstand to 5 m / s.

[0172] The ground station sends a takeoff command, and the rotor platform takes off. Under the action of the load mitigation control algorithm, the aircraft will change its position in the wind field, gradually flying from the high-wind area to the area with lower wind speed, until it hovers when the wind speed constraint is met. The first hovering position x1 at this moment is recorded, and the measured wind speed V is recorded by the airborne ultrasonic wind-measuring radar. w (x1), if V w If (x1) ≤ 5 m / s, then the load reduction algorithm is proven to be effective. This is because the goal of the load reduction algorithm is to adjust the position of the aircraft in the wind field to reduce the wind load. When the aircraft can hover stably in an area where the wind speed does not exceed its bearing capacity, it means that the algorithm has achieved the expected load reduction effect.

[0173] If the fan is turned off at this time, the spacecraft will fly back to the initial position x0 under the guidance algorithm that integrates load and accuracy. When the spacecraft finally hovers, the second hovering position is recorded as x2. The distance between the second hovering position x2 and the initial position x0 represents the terminal positioning accuracy, which corresponds to the terminal ballistic tracking accuracy during the ascent phase of the launch vehicle.

[0174] The above process is equivalent to the wind acting on the aircraft, causing the aircraft's position to deviate, and hovering again at the first hovering position x1. At this point, the wind fan is turned off, so the aircraft's estimate of the wind speed will decrease, and it will gradually return to the initial position x0. Theoretically, if the estimate is completely accurate and the model has no deviation, it can completely return to the initial position. However, in actual systems, there will always be deviations and disturbances, so the aircraft's estimate of the wind speed will not be completely zero, but only close to zero. Therefore, the distance between the final second hovering position x2 and the initial position x0 is called the positioning error, which is used to test the algorithm's ability to resist interference.

[0175] By changing the wind speed parameters and repeating the above steps, the position difference is obtained by calculating the Euclidean distance difference between the coordinates of the second hovering position and the initial position for each group. Then, the root mean square error of all group position differences is calculated to obtain the positioning error value, which is used to test the algorithm's anti-interference ability. The smaller the positioning error, the more accurately the algorithm can return the aircraft to the vicinity of the initial position under the presence of deviations and disturbances, that is, the stronger the algorithm's anti-interference ability.

[0176] This application also provides an application scenario in which the verification method for the launch vehicle load reduction control algorithm described above is applied. Specifically, the verification method for the launch vehicle load reduction control algorithm provided in this embodiment can be applied to a scenario of ground-based simulated flight testing of a launch vehicle. This scenario includes a test preparation phase, a simulated flight phase, and a data analysis phase. The launch vehicle model enters the simulated flight environment from the test preparation phase, undergoes various tests under simulated flight conditions, obtains relevant flight data, and then enters the data analysis phase. The verification method for the launch vehicle load reduction control algorithm provided in this embodiment belongs to the control algorithm verification sub-phase within the simulated flight phase. Specifically, in the simulated flight phase, by simulating the interference conditions such as wind fields that the rocket may encounter in actual flight, this verification method is used to test and evaluate the effectiveness and stability of the load reduction control algorithm to ensure that the algorithm can reliably achieve the load reduction function in actual flight.

[0177] Based on the same inventive concept, this application also provides a verification device for a launch vehicle load reduction control algorithm, which implements the verification method for the launch vehicle load reduction control algorithm described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more verification device embodiments for launch vehicle load reduction control algorithms provided below can be found in the limitations of the verification method for launch vehicle load reduction control algorithms described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 5 As shown, a verification device for a launch vehicle load reduction control algorithm is provided, comprising:

[0179] The dynamic model construction module 301 is used to construct the dynamic model of the launch vehicle based on the launch vehicle physical model.

[0180] The wind field simulation module 302 is used to obtain the initial position coordinates of the initial position of the launch vehicle physical model and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, the wind speed fluctuation range and the wind speed spatial distribution value.

[0181] The control and data acquisition module 303 is used to control the position and attitude of the launch vehicle physical model in the wind field simulation system based on the dynamic model of the launch vehicle physical model and obtain motion response data through the load reduction control algorithm.

[0182] The parameter iteration module 304 is used to change the preset wind speed parameters and return to the wind field simulation module until the preset iteration threshold is met, so as to obtain multiple sets of motion response data under different wind speed parameters.

[0183] The verification result acquisition module 305 is used to calculate the verification result of the load reduction control algorithm by statistical methods based on multiple sets of motion response data under different wind speed parameters.

[0184] This application proposes an indoor verification method for launch vehicle load reduction control algorithms. First, a rocket-like indoor flight platform with dynamic characteristics similar to a real launch vehicle is designed. Key parameters such as mass, moment of inertia, thrust, and control force are experimentally determined, and a mathematical model of the platform is established. Then, for the load reduction control task during the launch vehicle's ascent phase, an indoor flight control algorithm is developed, incorporating wind speed estimation, load mitigation control, and trajectory tracking control algorithms. Finally, a simulated wind field is constructed indoors using a fan to simulate the real wind disturbance environment during the launch vehicle's ascent phase. The load scheduling performance and trajectory tracking accuracy of the load reduction control algorithm are evaluated by assessing the platform's motion response in the wind field, thus achieving comprehensive verification of the launch vehicle load reduction control algorithm's performance.

[0185] This experimental method enables low-cost verification of the load reduction control algorithm for the ascent phase of a launch vehicle. The spacecraft is designed to meet the dynamic characteristics of a launch vehicle and possesses a control mechanism similar to that of a launch vehicle, thus effectively simulating rocket motion. Secondly, the load reduction control algorithm for the simulated rocket indoor flight platform also references the load reduction control of the launch vehicle's ascent phase, integrating the two most critical algorithms into the control algorithm of the simulated rocket indoor flight platform. Finally, the design of the indoor load reduction test also takes into account the flight environment and mission of the launch vehicle's ascent phase.

[0186] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores processing data for the launch vehicle load reduction control algorithm. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a verification method for a launch vehicle load reduction control algorithm.

[0187] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0188] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0189] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0190] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0191] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Furthermore, any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory.

[0192] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0193] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A verification method for a launch vehicle load reduction control algorithm, characterized in that, The verification method for the launch vehicle load reduction control algorithm includes: A physical model of the launch vehicle and a wind field simulation system are constructed; the physical model of the launch vehicle is located in the wind field simulation system; the wind field simulation system is used to simulate the actual wind disturbance environment during the ascent phase of the launch vehicle. Based on the physical model of the launch vehicle, a dynamic model of the physical model of the launch vehicle is constructed; Obtain the initial position coordinates of the initial position of the launch vehicle physical model, and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, wind speed fluctuation range, and wind speed spatial distribution value. Based on the dynamic model of the launch vehicle physical model, the position and attitude of the launch vehicle physical model in the wind field simulation system are controlled by the load reduction control algorithm to obtain motion response data; Change the preset wind speed parameters, return to the steps of "obtain the initial position coordinates of the initial position of the launch vehicle physical model and start the wind field simulation system according to the preset wind speed parameters" until the preset iteration threshold is met, and obtain multiple sets of motion response data under different wind speed parameters; Based on multiple sets of motion response data under different wind speed parameters, the verification results of the load reduction control algorithm were obtained through statistical methods.

2. The verification method for the launch vehicle load reduction control algorithm according to claim 1, characterized in that, Based on the dynamic model of the launch vehicle's physical model, the position and attitude of the launch vehicle's physical model in the wind field simulation system are controlled by a load reduction control algorithm to obtain motion response data, specifically including: Based on the dynamic model of the launch vehicle physical model, the wind speed at the current moment is estimated by estimating the wind speed at the wind field location of the launch vehicle physical model. If the wind speed estimate at the current moment is greater than or equal to the preset wind speed threshold, then the wind speed estimate at the current moment is used as the wind speed before hovering, causing the launch vehicle physical model to shift to a position in a weak wind zone that is less than the preset wind speed threshold, until the wind speed estimate at the current moment is less than the preset wind speed threshold and hovering is achieved, thus obtaining the hovering wind speed and the coordinates of the first hovering position of the launch vehicle physical model. After the physical model of the launch vehicle hovers, the wind field simulation system is turned off, allowing the physical model of the launch vehicle to return to its initial position and hover, thus obtaining the coordinates of the second hovering position. The motion response data includes wind speed before hovering, hovering wind speed, coordinates of the first hovering position, and coordinates of the second hovering position.

3. The verification method for the launch vehicle load reduction control algorithm according to claim 1, characterized in that, The verification results include wind speed reduction rate and positioning error value; Based on multiple sets of motion response data under different wind speed parameters, the verification results of the load reduction control algorithm were calculated using statistical methods, specifically including: Calculate the difference between the wind speed before hovering and the corresponding hovering wind speed in each set of motion response data to obtain multiple sets of wind speed reduction. The wind speed reduction rate is obtained by averaging the wind speed reduction of all groups. Calculate the Euclidean distance difference between the second hovering position coordinates and the initial position in each set of motion response data to obtain the position difference; The root mean square error of the position differences of all groups is calculated to obtain the positioning error value.

4. The verification method for the launch vehicle load reduction control algorithm according to claim 2, characterized in that, Based on the dynamic model of the launch vehicle physical model, the wind speed at the current moment is estimated by estimating the wind speed at the location of the launch vehicle physical model in the wind field. Specifically, this includes: The wind direction is used as the x-axis of the launch vehicle physical model, and the direction of the wind disturbance torque is used as the y-axis of the launch vehicle physical model. Selecting state variables Control torque Substituting the dynamic model of the launch vehicle solid model into the dynamic model, we obtain the state-space form of the dynamic model of the launch vehicle solid model: Where x represents the state vector; q represents the attitude angle; Indicates the rate of change of attitude angle; The second derivative of the attitude angle is represented by T; the transpose is represented by M. k Indicates control torque; δ q Indicates rudder deflection angle; k δ X represents the normal force generated by a unit rudder deflection; k Indicates the distance from the point of application of the control force to the nose of the launch vehicle; X cg Indicates the distance from the center of mass to the nose of the launch vehicle; I y A represents the moment of inertia along the y-axis of the launch vehicle; B represents the system matrix of the state system; C represents the input matrix of the state system. denoted as , where f represents the disturbance torque generated by wind acting on the launch vehicle's body; and b3 represents the value of the disturbance torque generated by wind acting on the launch vehicle's body. Based on the state-space form of the dynamic model of the launch vehicle physical model, an extended state system is constructed to obtain the estimated value of the wind disturbance torque. Based on the estimated wind disturbance moment, the estimated wind speed is calculated using the following formula: in, This represents the estimated wind speed. X represents the estimated value of the wind disturbance torque; A Indicates the distance from the aerodynamic point to the nose of the launch vehicle; X cg C represents the distance from the center of mass to the nose of the launch vehicle; D ρ represents the drag coefficient of the launch vehicle; ρ represents the atmospheric density.

5. The verification method for the launch vehicle load reduction control algorithm according to claim 4, characterized in that, Based on the state-space form of the dynamic model of the launch vehicle's physical model, an extended state system is constructed to obtain estimates of the wind disturbance torque, specifically including: Based on the state-space form of the dynamic model of the launch vehicle physical model, an extended state system is constructed, letting... The extended state equation is obtained based on the following formula: Based on the extended state equations, an extended state observer is constructed using the following formula to obtain an estimate of the wind disturbance torque: In the formula, x o Represents the expanded state vector; Indicates the rate of change of attitude angle; For x o The first derivative with respect to time represents the dynamic changes of each state variable in the expanding state vector over time; A o The system matrix represents the extended state system; B o The input matrix represents the extended state system; u represents the control input. This represents the state estimation vector of the extended state observer. This represents an estimated value of the attitude angle; This represents an estimated value of the rate of change of attitude angle; This represents an estimated value of the wind disturbance torque; For Z o The first derivative with respect to time indicates how quickly the state estimation vector of the extended state observer changes with time; ω represents the observer gain matrix. o y represents the observer control bandwidth; y represents the system output of the extended state observer. This represents the output matrix of the extended state observer.

6. The verification method for the launch vehicle load reduction control algorithm according to claim 2, characterized in that, The launch vehicle physical model is shifted to a location in a low-wind zone with wind speeds below a preset threshold, specifically including: Based on the preset wind speed parameters of the wind field simulation system, the load mitigation attitude control command of the launch vehicle physical model is calculated using the following formula. Where, δ q (t) represents the load reduction attitude control command at time t; Represents the inverse Laplace transform; ω Cn ξ represents the natural frequency of the filter; s represents the complex variable in the Laplace transform; Cn δ represents the damping ratio of the filter. q-in (s) represents the attitude control input command in the Laplace frequency domain; δ q-in Indicates attitude control input commands; K Gp K Gd and K Gi These represent the proportional gain parameter, differential gain parameter, and integral gain parameter, respectively; q represents the attitude angle; q cmd Indicates command attitude parameters; q represents the rate of change of attitude angle; cmd (s) represents the command attitude parameter in the Laplace frequency domain; q cmd-in (s) represents the input command attitude parameters in the Laplace frequency domain; ξ Gn ω represents the damping ratio of the filter. Gn q represents the filter's natural frequency; cmd-in Indicates the input command attitude parameter; Δx cmd This represents the position offset that needs to be generated at the current moment; K represents the x-axis offset velocity. VW Indicates the load control gain; V represents the estimated wind speed. w-max Indicates the preset wind speed threshold; According to the load reduction attitude control command, the physical model of the launch vehicle is shifted to a position in a weak wind zone with a wind speed lower than the preset wind speed threshold.

7. A verification device for a launch vehicle load reduction control algorithm, characterized in that, The verification device for the launch vehicle load reduction control algorithm applies the verification method for the launch vehicle load reduction control algorithm as described in any one of claims 1-6, and the verification device for the launch vehicle load reduction control algorithm includes: The dynamics model building module is used to build a dynamics model of the launch vehicle based on the launch vehicle physical model. The wind field simulation module is used to obtain the initial position coordinates of the initial position of the launch vehicle physical model and start the wind field simulation system according to the preset wind speed parameters; the preset wind speed parameters include the initial wind speed, the wind speed fluctuation range, and the wind speed spatial distribution value. The control and data acquisition module is used to control the position and attitude of the launch vehicle physical model in the wind field simulation system based on the dynamic model of the launch vehicle physical model, and obtain motion response data by using the load reduction control algorithm. The parameter iteration module is used to change the preset wind speed parameters and return to the wind field simulation module until the preset iteration threshold is met, so as to obtain multiple sets of motion response data under different wind speed parameters. The verification result acquisition module is used to calculate the verification results of the load reduction control algorithm by statistical methods based on multiple sets of motion response data under different wind speed parameters.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the verification method for the launch vehicle load reduction control algorithm according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the verification method for the launch vehicle load reduction control algorithm as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the verification method for the launch vehicle load reduction control algorithm as described in any one of claims 1-6.

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