Strapdown seeker active-disturbance-rejection dual-rate control method and system based on virtual optical axis

Abstract

The invention provides a strapdown seeker active-disturbance-rejection dual-rate control method and system based on a virtual optical axis. Wherein a virtual optical axis model is established, and the virtual optical axis model is dynamically adjusted according to attitude data of a strapdown seeker and position information of a tracking target; calculating a deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model in real time; a high-speed controller is adopted to respond to the rapid change component of the deviation vector, a low-speed controller is adopted to respond to the slow change component of the deviation vector, and corresponding correction instructions are generated respectively; and combining correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal so as to drive the strapdown seeker to adjust the attitude data. According to the technical scheme provided by the invention, the tracking performance of the strapdown seeker can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of aerospace technology, and in particular to a strapdown seeker self-anti-disturbance dual-rate control method and system based on a virtual optical axis. Background Art

[0002] In the modern military, aerospace and civil monitoring fields, accurate target tracking capabilities are essential to ensure the success of the mission. Especially in applications such as long-range guided weapon systems, UAV reconnaissance and strike platforms, and satellite communication antenna alignment, the strapdown seeker, as one of the key components, has a direct impact on the effectiveness of the entire system. These application scenarios usually require the seeker to quickly and accurately lock and continuously track moving or stationary targets, and maintain a stable tracking state even in the face of complex environmental interference.

[0003] At present, traditional seeker control methods mainly rely on a single closed-loop feedback control system to achieve target tracking. This type of system continuously measures the error between the actual output (such as the seeker pointing) and the expected value, and adjusts the action of the actuator accordingly to gradually reduce the deviation until satisfactory tracking accuracy is achieved.

[0004] Although the above traditional methods can meet basic application requirements to a certain extent, they also have obvious limitations. When encountering high-speed moving targets or drastic changes in environmental conditions, a single controller architecture is difficult to simultaneously take into account the requirements of both rapid response and long-term stability, which often leads to poor tracking effects. Summary of the invention

[0005] The embodiments of the present application provide a dual-rate control method and system for a strapdown seeker self-disturbance rejection based on a virtual optical axis, so as to solve the problem of poor tracking performance of the strapdown seeker in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a dual-rate control method for a strapdown seeker auto-disturbance rejection based on a virtual optical axis, comprising:

[0007] Establishing a virtual optical axis model, the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target, so as to simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target;

[0008] Calculating in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model;

[0009] A high-rate controller is used to respond to the fast-changing component of the deviation vector, and a low-rate controller is used to respond to the slow-changing component of the deviation vector, and corresponding correction instructions are generated respectively;

[0010] The correction instructions output by the high-rate controller and the low-rate controller are combined to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

[0011] Optionally, the establishing of a virtual optical axis model, wherein the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target to simulate an ideal tracking path of the strapdown seeker for the tracking target, comprises:

[0012] Obtain the attitude data of the strapdown seeker and the position information of the tracked target;

[0013] Calculating the actual pointing direction of the strapdown seeker according to the attitude data of the strapdown seeker;

[0014] Based on the position information of the tracking target and the actual pointing direction of the strapdown seeker, construct an ideal pointing direction of the strapdown seeker as a basis for a virtual optical axis;

[0015] mapping the difference between the ideal pointing direction and the actual pointing direction of the strapdown seeker into a series of continuously adjustable parameters;

[0016] The virtual optical axis model is updated in real time using the continuously adjustable parameters to ensure that the virtual optical axis can simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target.

[0017] Optionally, the real-time calculation of the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model includes:

[0018] Determining the direction vector of the actual optical axis of the strapdown seeker using the attitude data of the strapdown seeker, wherein the attitude data at least includes a pitch angle, a yaw angle, and a roll angle;

[0019] Calculating the direction vector of the virtual optical axis at the same time according to the position information of the tracking target and the virtual optical axis model;

[0020] A vector operation is performed on the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis to obtain a deviation vector, wherein the deviation vector is used to reflect the degree of deviation of the actual optical axis from the ideal tracking path.

[0021] Optionally, the adopting of a high-rate controller to respond to a fast-changing component of the deviation vector, adopting a low-rate controller to respond to a slow-changing component of the deviation vector, and generating corresponding correction instructions respectively, comprises:

[0022] Decomposing the deviation vector into a fast-changing component and a slow-changing component by frequency separation technology;

[0023] A high-rate controller is applied to process the fast-changing component, wherein the high-rate controller uses a high-bandwidth filter to capture high-frequency components in the deviation vector and generates a correction instruction for the fast-changing component according to the high-frequency components;

[0024] A low-rate controller is applied to process the slowly changing component. The low-rate controller uses a low-bandwidth filter to capture low-frequency components in the deviation vector and generates a correction instruction for the slowly changing component based on the low-frequency components.

[0025] Optionally, the correction instructions output by the high-rate controller and the low-rate controller are combined to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, including:

[0026] Filtering the correction instructions of the fast-changing components generated by the high-rate controller and filtering the correction instructions of the slow-changing components generated by the low-rate controller to maintain the stability of the correction instructions;

[0027] According to the preset weight distribution strategy, the filtered high-rate correction instructions and low-rate correction instructions are weighted and fused to form a preliminary comprehensive control signal;

[0028] The preliminary integrated control signal is converted into a control instruction format adapted for execution by the strapdown seeker, and the actual response of the strapdown seeker is monitored through a real-time feedback mechanism to fine-tune the preliminary integrated control signal as needed to obtain an integrated control signal.

[0029] Optionally, the continuously adjustable parameters include at least: a proportional gain matrix, an integral gain function, and a noise compensation term;

[0030] The process of updating the virtual optical axis model in real time using the continuously adjustable parameters includes applying the following calculation formula:

[0031]

[0032] Among them, V axis (t) is the direction vector of the virtual optical axis at time t, V ideal (t) is the vector pointing in the ideal direction, is the system state θ(t), error e(t) and its first-order derivative over time The proportional gain matrix, P target (t) and P head(t) are the position vector of the tracking target and the position vector of the strapdown seeker, is the integral gain function, which depends on time s, system parameter vector θ(s), error vector e(s) and its first-order derivative and the second-order derivative It also depends on the control input u(s), (V ideal (s)-V actual (s)) is the deviation vector between the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis at time s, ds is the small change of the integral variable s, and N(t, σ(t), w(t), d(t)) is the noise compensation term, which is used to offset the influence of measurement error, external interference w(t) and unmodeled dynamic d(t), and is adaptively adjusted according to the uncertainty σ(t) of the system.

[0033] Optionally, the process of generating the comprehensive control signal includes applying the following calculation formula:

[0034]

[0035] Among them, C total (t) is the comprehensive control signal at time t, C fast (t) and C slow (t) are the correction instructions generated by the high-rate controller and the low-rate controller, is the system state η, error e(t) and its first-order derivative over time The weight coefficient of the performance index p(t) and the constraint condition c(t) changes, is another weight coefficient used to adjust the influence of the cross term, which depends on the system state η, the error e(t) and its first-order derivative It also depends on other performance indicators q(t) and constraints r(t), D cross (t) is the cross term representing the interaction between the high-rate and low-rate controller outputs, U adaptive (t,ξ) is an adaptive control term, ξ is an adaptive control parameter or variable, R(t,ω(t),r(t),f(t)) is a robustness compensation term used to deal with unmodeled dynamics and external uncertainties, where ω(t) represents the estimated value of the external disturbance, r(t) and f(t) are performance indicators and constraints related to robustness, It is an optimization item used to ensure the best control effect while meeting multiple performance indicators. represents a set of performance indicators that need to be optimized, and g(t) is the constraint related to the optimization process.

[0036] In a second aspect, an embodiment of the present application provides a strapdown seeker anti-disturbance dual-rate control system based on a virtual optical axis, comprising:

[0037] An establishment module is used to establish a virtual optical axis model, and the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target to simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target;

[0038] A calculation module, used for calculating in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model;

[0039] a response module, configured to use a high-rate controller to respond to a fast-changing component of the deviation vector, and a low-rate controller to respond to a slow-changing component of the deviation vector, and to generate corresponding correction instructions respectively;

[0040] A generation module is used to combine the correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

[0041] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component, wherein the storage component stores one or more computer instructions, and the one or more computer instructions are used to be called and executed by the processing component to implement a dual-rate control method for self-anti-disturbance of a strapdown seeker based on a virtual optical axis as described in any one of the first aspects above.

[0042] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a dual-rate control method for self-anti-disturbance of a strapdown seeker based on a virtual optical axis as described in any one of the first aspects above.

[0043] In the embodiment of the present application, a virtual optical axis model is established, and the virtual optical axis model is dynamically adjusted according to the attitude data of the strapdown seeker and the position information of the tracking target; the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model is calculated in real time; a high-rate controller is used to respond to the fast-changing component of the deviation vector, and a low-rate controller is used to respond to the slow-changing component of the deviation vector, and corresponding correction instructions are generated respectively; the correction instructions output by the high-rate controller and the low-rate controller are combined to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data. The technical solution provided by the present application can significantly improve the tracking performance while maintaining the stability of the system, and is suitable for applications requiring high-precision fast response and good anti-interference characteristics.

[0044] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flowchart of a dual-rate control method for a strapdown seeker auto-disturbance rejection based on a virtual optical axis provided in an embodiment of the present application.

[0047] Figure 2 A schematic diagram of a strapdown seeker anti-disturbance dual-rate control system based on a virtual optical axis provided in an embodiment of the present application is provided.

[0048] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0050] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0052] Figure 1A flowchart of a dual-rate control method for a strapdown seeker anti-disturbance control based on a virtual optical axis is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0053] 101. Establishing a virtual optical axis model, wherein the virtual optical axis model is dynamically adjusted according to the attitude data of the strapdown seeker and the position information of the tracking target, so as to simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target;

[0054] In this step, the system builds a virtual optical axis model based on the attitude data of the strapdown seeker (an optical sensor without mechanical scanning components) and the position information of the tracked target. This model represents the ideal path from the current position of the seeker to the target and will be dynamically adjusted as the target position changes.

[0055] In the embodiment of the present application, it is assumed that a strapdown seeker installed on a drone is tracking a moving vehicle on the ground. When the vehicle changes direction or speed, the system will use the latest GPS coordinates and other information to update the virtual optical axis model to ensure that it always points in the correct direction.

[0056] The present application takes into account that in the existing seeker tracking system, a fixed or preset optical axis model is usually used to guide the tracking process. However, this method has certain limitations: when the target moves quickly or the environmental conditions change, the fixed optical axis model cannot adapt to the new situation in time, resulting in a decrease in tracking accuracy or even loss of the target. In addition, the traditional method may not fully consider the impact of the change in the posture of the strapdown seeker itself on the tracking effect, which will also reduce the response speed and accuracy of the system. In order to solve the above problems, the present invention proposes a virtual optical axis model solution based on dynamic adjustment, which constructs an ideal pointing direction that can reflect the current optimal tracking path by acquiring and processing the posture data of the strapdown seeker and the position information of the tracking target in real time, and continuously updates the virtual optical axis model accordingly. Such a design can significantly improve the flexibility and adaptability of the system, thereby achieving more accurate and stable tracking performance.

[0057] The options are as follows:

[0058] Optionally, the "establishing a virtual optical axis model, which is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target to simulate the ideal tracking path of the strapdown seeker for the tracking target" in 101 includes: obtaining the posture data of the strapdown seeker and the position information of the tracking target; calculating the actual pointing direction of the strapdown seeker according to the posture data of the strapdown seeker; constructing the ideal pointing direction of the strapdown seeker based on the position information of the tracking target and the actual pointing direction of the strapdown seeker as the basis of the virtual optical axis; mapping the difference between the ideal pointing direction and the actual pointing direction of the strapdown seeker into a series of continuously adjustable parameters; and using the continuously adjustable parameters to update the virtual optical axis model in real time to ensure that the virtual optical axis can simulate the ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target.

[0059] Strapdown Seeker: An optical sensor with no mechanical scanning components that provides continuous target detection and tracking.

[0060] Posture data: Data that describes the spatial position and orientation of an object relative to a reference coordinate system.

[0061] Actual pointing direction: The direction the strapdown seeker is currently actually aiming at.

[0062] Ideal pointing direction: The best aiming direction calculated based on the target position.

[0063] Continuously adjustable parameters: A set of variables used to represent the difference between two directions. The direction of the virtual optical axis can be changed by adjusting these parameters.

[0064] First, the attitude data of the strapdown seeker (such as pitch angle, yaw angle, etc.) and the exact position of the tracked target (such as longitude and latitude coordinates) are collected. Secondly, the actual pointing direction of the strapdown seeker is calculated using the attitude data obtained. Further, the target position information is combined with the actual pointing direction of the strapdown seeker to calculate the best pointing direction, that is, the ideal pointing direction. The difference between the ideal pointing direction and the actual pointing direction is then converted into a series of easy-to-control parameter forms. Finally, the parameters obtained in the previous step are used as input to adjust the virtual optical axis model in real time to ensure that it always represents the best path from the current position to the target.

[0065] In the embodiment of the present application, it is assumed that a UAV equipped with a strapdown seeker is performing a mission and needs to follow a car traveling at a speed of 60 km / h. It is assumed that the UAV is 50 meters high and the horizontal distance between the two is 100 meters at the initial moment.

[0066] At t = 0 seconds, the drone receives a GPS signal showing that the car is at 40.7128°N, 74.0060°W, while the drone itself is at 40.7130°N, 74.0055°W. At this time, the drone's attitude sensor indicates that its forward pointing angle is 30° east by south.

[0067] Through simple geometric relationship calculations, it can be concluded that in order to accurately track the car, the ideal pointing angle should be about 45° east-southeast.

[0068] Next, this angle difference is converted into continuously adjustable parameters such as the corresponding roll angle and pitch angle change values.

[0069] Finally, these parameters are used to update the virtual optical axis model in real time to guide the drone to adjust its flight attitude and maintain the correct tracking direction.

[0070] This solution not only overcomes the problem of slow response of traditional methods to rapidly changing scenes by establishing a dynamic adjustment mechanism, but also improves the robustness and accuracy of the entire system. This improvement is particularly important for applications that require high-precision tracking (such as military reconnaissance, disaster relief, etc.).

[0071] 102. Calculate in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model;

[0072] This step involves real-time monitoring of the difference between the actual optical axis and the ideal path. By comparing the actual direction of the current strapdown seeker with the direction indicated by the virtual optical axis model, a deviation vector can be obtained, which represents the angle or displacement difference between the two.

[0073] In the embodiment of the present application, assuming that the above example is continued, at any given moment, if the drone deviates from the ideal trajectory due to wind or other factors, the system will immediately detect this deviation and quantify it into a specific value (such as an angle difference).

[0074] The present application takes into account that in the existing seeker tracking system, the deviation calculation method based on the preset model is usually adopted. This method often relies on fixed geometric relationships or simplified assumptions to estimate the difference between the actual optical axis and the ideal path. However, in practical applications, due to the influence of factors such as target motion and environmental interference, this static method is difficult to accurately reflect the gap between the real state of the strapdown seeker in real time and its ideal state. This will cause the tracking error to accumulate, and ultimately affect the overall performance of the system. In order to solve the above problem, the present invention proposes a more accurate deviation vector calculation scheme, which dynamically determines the direction vectors of the two by using the attitude data and virtual optical axis model of the strapdown seeker in real time, and performs vector operations to obtain the deviation vector. This method can more truly reflect the deviation degree of the actual optical axis relative to the ideal tracking path, so that the control system can make more accurate and timely adjustments, and improve the response speed and tracking accuracy of the whole system.

[0075] The options are as follows:

[0076] Optionally, the "real-time calculation of the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model" in 102 includes: using the posture data of the strapdown seeker to determine the direction vector of the actual optical axis of the strapdown seeker, the posture data at least including the pitch angle, the yaw angle and the roll angle; according to the position information of the tracking target and the virtual optical axis model, calculating the direction vector of the virtual optical axis at the same time; performing vector operation on the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis to obtain a deviation vector, and the deviation vector is used to reflect the degree of deviation of the actual optical axis from the ideal tracking path.

[0077] Pitch: refers to the angle of rotation of an object around the X-axis.

[0078] Yaw: refers to the angle of rotation of an object around the Z axis.

[0079] Roll angle: refers to the angle of rotation of an object around the Y axis.

[0080] Direction vector: A unit vector that describes the direction from one point to another point.

[0081] Vector operations: The process of performing mathematical operations such as addition and subtraction on two or more vectors.

[0082] Deviation vector: A vector that represents the difference between the actual position and the expected position.

[0083] First, determine the actual optical axis direction, and use the pitch, yaw, and roll angle information provided by the strapdown seeker to construct a direction vector representing the current actual pointing direction. Secondly, calculate the virtual optical axis direction, and obtain the ideal pointing direction vector at the same time based on the position information of the tracked target and the previously established virtual optical axis model. Further vector operations are performed to obtain the deviation. By performing vector subtraction on the actual optical axis direction vector and the virtual optical axis direction vector, the deviation vector is obtained, which directly reflects the degree to which the actual optical axis deviates from the ideal path.

[0084] In the embodiment of the present application, consider a drone that is tracking a target vehicle moving on the ground. Assume that at a certain time t, the altitude of the drone is 50 meters, and its attitude sensor reports the following attitude data:

[0085] Pitch angle θ = 10°

[0086] Yaw angle ψ=45°

[0087] Roll angle φ=0°

[0088] At the same time, according to GPS positioning, it is known that the target vehicle's location coordinates are (100,150) meters (relative to the current position of the drone). Using this information, you can follow the steps below:

[0089] Determine the actual optical axis direction: First, convert the attitude angle into a direction vector. For a given attitude angle, the formula can be used Get the direction vector of the actual optical axis. Substituting the specific value into the value, we get

[0090] Calculate the direction of the virtual optical axis: Based on the target position and the height of the drone, the ideal direction vector from the drone to the target can be calculated by simple trigonometric functions. If the drone is assumed to be at the origin, then

[0091] Vector operation to find the deviation: Finally, through Calculate the deviation vector. The result is (0.144,-0.172,0.142).

[0092] Through the above process, the deviation between the actual optical axis of the strapdown seeker and the ideal tracking path can be obtained in real time and accurately. Compared with the traditional method, this method not only improves the accuracy of the deviation calculation, but also enhances the adaptability and robustness of the system, especially in complex and changing working environments, which helps to achieve a higher level of tracking performance. In addition, the clear deviation vector also provides direct data support for subsequent control strategies, making it easy to quickly and effectively correct any deviations and ensure continuous and stable tracking effects.

[0093] 103. Use a high-rate controller to respond to the fast-changing component of the deviation vector, and a low-rate controller to respond to the slow-changing component of the deviation vector, and generate corresponding correction instructions respectively;

[0094] In this step, two types of controllers are used to effectively handle errors of different natures: a high-rate controller is used to deal with fast-changing error components; a low-rate controller is responsible for slower but persistent deviations. Each controller will produce corresponding corrective measures based on its characteristics.

[0095] In the embodiment of the present application, assuming that when encountering a sudden large deviation caused by atmospheric turbulence, the high-rate controller will react quickly and issue an immediate adjustment command to correct the situation. At the same time, for the slight accumulated error caused by long-term flight, the low-rate controller will slowly make fine adjustments.

[0096] The present application takes into account that in traditional tracking control systems, a single controller is usually used to handle all deviation vector changes. However, such a design often cannot take into account the needs of fast response and stable control at the same time. For example, if the controller is too sensitive, it may overreact to noise or small disturbances, resulting in system instability; on the contrary, if the controller responds too slowly, it may not be able to correct sudden large deviations in time, affecting the tracking accuracy. Therefore, when faced with deviation vectors that contain both high-frequency (fast) and low-frequency (slow) components, traditional methods are difficult to achieve ideal control effects. In order to solve this problem, the present invention proposes a dual-rate controller solution based on frequency separation technology. By decomposing the deviation vector into fast-changing components and slow-changing components, and processing them with high-rate controllers and low-rate controllers respectively, different types of errors can be managed more effectively, thereby improving the overall performance of the system.

[0097] The options are as follows:

[0098] Optionally, "the use of a high-rate controller to respond to the fast-changing component of the deviation vector, the use of a low-rate controller to respond to the slowly changing component of the deviation vector, and respectively generating corresponding correction instructions" in 103 includes: decomposing the deviation vector into a fast-changing component and a slow-changing component through a frequency separation technique; for the fast-changing component, applying a high-rate controller for processing, the high-rate controller uses a high-bandwidth filter to capture the high-frequency components in the deviation vector, and generates correction instructions for the fast-changing components based on the high-frequency components; for the slow-changing component, applying a low-rate controller for processing, the low-rate controller uses a low-bandwidth filter to capture the low-frequency components in the deviation vector, and generates correction instructions for the slow-changing components based on the low-frequency components.

[0099] Frequency separation technology: A signal processing method used to separate the input signal into multiple parts according to different frequency ranges.

[0100] High Bandwidth Filter: A filter that allows higher frequency signals to pass while suppressing lower frequency signals.

[0101] Low Bandwidth Filter: A filter that allows lower frequency signals to pass while suppressing higher frequency signals.

[0102] Correction instruction: A command sent to the actuator to adjust the system state based on the results calculated by the controller.

[0103] First, frequency separation techniques, such as Fourier transform or other suitable methods, are used to decompose the deviation vector into fast-changing components and slow-changing components. Second, a high-bandwidth filter is used to capture the high-frequency components in the deviation vector. Correction instructions for the fast-changing components are generated based on these high-frequency components. Finally, a low-bandwidth filter is used to capture the low-frequency components in the deviation vector. Correction instructions for the slow-changing components are generated based on these low-frequency components.

[0104] In the embodiment of the present application, it is assumed that a UAV is tracking a moving target. At a certain moment, the deviation vector detected by the strapdown seeker To simplify the explanation, only the data in one dimension is considered, for example, the deviation value in the X-axis direction is 0.144.

[0105] First, assume that the deviation data is analyzed using Fourier transform, and it is found that the deviation is mainly composed of two parts: one is a high-frequency component with strong periodicity, and the other is a relatively smooth low-frequency component. Secondly, a high-pass filter is applied (for example, the cutoff frequency is 1Hz), and the high-frequency component is about 0.05. The high-rate controller generates correction instructions based on this high-frequency component, such as immediately adjusting the pitch angle to reduce the instantaneous deviation. Finally, a low-pass filter is applied (for example, the cutoff frequency is 0.1Hz), and the low-frequency component is about 0.094. The low-rate controller generates correction instructions based on this low-frequency component, such as gradually adjusting the flight path to maintain a stable tracking state for a long time.

[0106] This dual-rate controller design can effectively distinguish and process different frequency components in the deviation vector, allowing the system to respond quickly to emergencies and smoothly correct persistent deviations. This approach not only improves tracking accuracy, but also enhances system stability, especially in complex and changing working environments. In addition, it can better adapt to the influence of various interference sources, ensuring that the entire tracking process is more reliable.

[0107] 104. Combining the correction instructions output by the high-rate controller and the low-rate controller, a comprehensive control signal is generated to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

[0108] The last step in this process is to integrate the outputs from the two controllers to form a complete control strategy. This strategy will be used to guide the strapdown seeker on how to adjust its attitude to minimize or even eliminate all the types of errors mentioned above, thereby maintaining the best tracking state for the target.

[0109] In the present application, it is assumed that in actual operation, this means that the control system will take into account both suggestions - responding to emergencies in a timely manner and solving long-term problems steadily. For example, it may first perform some emergency steering actions to combat the influence of strong winds, and then gradually adjust back to a more stable flight mode to ensure that the drone can smoothly and accurately follow the ground vehicle.

[0110] The present application takes into account that in the existing tracking control system, although the dual-rate controller can handle the fast-changing component and the slow-changing component separately, directly merging the outputs of the two controllers may introduce instability or conflicts between control instructions. For example, if the high-rate controller frequently issues large-scale correction instructions, and the low-rate controller provides a relatively gentle adjustment, there may be a mutual offset or superposition effect between the two, resulting in a poor final control effect. In addition, the output formats and response characteristics of different controllers may not be fully matched, thereby affecting the coordination and performance of the overall system. In order to solve the above problems, the present invention proposes a comprehensive control signal generation scheme. The outputs of the high-rate controller and the low-rate controller are optimized by filtering processing, weighted fusion and real-time feedback mechanism to ensure that the final generated comprehensive control signal can both quickly respond to sudden deviations and stably perform long-term adjustments, thereby achieving accurate tracking of the tracking target.

[0111] The plan is as follows:

[0112] Optionally, "the correction instructions output by the high-rate controller and the low-rate controller are combined to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model" in 104 includes: filtering the correction instructions of the fast-changing components generated by the high-rate controller and filtering the correction instructions of the slow-changing components generated by the low-rate controller to maintain the stability of the correction instructions; weighted fusion of the filtered high-rate correction instructions and the low-rate correction instructions according to a preset weight distribution strategy to form a preliminary comprehensive control signal; converting the preliminary comprehensive control signal into a control instruction format adapted for execution by the strapdown seeker, and monitoring the actual response of the strapdown seeker through a real-time feedback mechanism to fine-tune the preliminary comprehensive control signal as needed to obtain a comprehensive control signal.

[0113] Filtering: A signal processing technique used to remove noise or unnecessary frequency components.

[0114] Weight assignment strategy: Assign corresponding weight values ​​to different input signals according to specific rules to determine their influence on the final result.

[0115] Weighted fusion: The process of combining multiple signals and calculating the sum according to preset weights.

[0116] Real-time feedback mechanism: A technology that monitors the actual response of the system and makes adjustments accordingly.

[0117] Filtering is to filter the correction instructions generated by the high-speed controller to reduce noise interference. The correction instructions generated by the low-speed controller are filtered to ensure their stability.

[0118] Weighted fusion is to perform weighted summation of filtered high-rate correction instructions and low-rate correction instructions according to a preset weight allocation strategy (such as based on experimental data or empirical formula) to form a preliminary comprehensive control signal.

[0119] Conversion and fine-tuning is to convert the preliminary integrated control signal into a specific execution command format suitable for the strapdown seeker. By real-time monitoring of the actual response of the strapdown seeker, the preliminary integrated control signal is fine-tuned as needed to obtain the best integrated control signal.

[0120] In the embodiment of the present application, it is assumed that a UAV is using a strapdown seeker to track a moving target on the ground. At a certain moment, the high-rate controller generates a correction instruction Δθ high =0.05°, the low-rate controller generates another correction command Δθ low =0.02°.

[0121] A simple low-pass filter is used to filter Δθ high After processing, the filtered high-speed correction instruction Δθ' is obtained. high =0.04°.

[0122] Similarly, for Δθ low Apply a low-pass filter to obtain the low-rate correction instruction Δθ' after filtering los =0.018°.

[0123] Assume that the preset weight distribution strategy is that high-rate instructions account for 70% and low-rate instructions account for 30%. Then the preliminary comprehensive control signal Δθ combined It can be calculated by the following formula:

[0124] Δθ combined =0.7×Δθ′ high +0.3×Δθ′ low =0.7×0.04+0.3×0.018=0.0334°

[0125] Δθ combined Convert into a control command format suitable for strapdown seeker, such as sending pitch angle adjustment command.

[0126] By monitoring the attitude sensor data of the drone in real time, confirm whether the actual pitch angle change after adjustment is in line with expectations. If the actual response is found to be slightly insufficient, Δθ can be appropriately increased. combined , for example, adjusted to Δθ final =0.035° to further optimize the tracking accuracy.

[0127] This comprehensive control signal generation method not only improves the reliability of a single controller output, but also makes the final control signal more adaptable to actual working conditions through a reasonable weight distribution strategy and real-time feedback mechanism. It can maintain the stability of the system while ensuring a fast response, significantly improving the overall performance of the tracking system. Especially in the face of complex and changing environments, this method can more effectively cope with various challenges and ensure continuous and accurate tracking results.

[0128] The present application takes into account that in existing tracking control systems, virtual optical axis models are usually static or established based on simple linear relationships. However, in practical applications, due to the influence of factors such as environmental interference, measurement errors, and the nonlinear characteristics of the system itself, such models are often difficult to accurately reflect the dynamic relationship between the strapdown seeker and the target. This will lead to a decrease in tracking accuracy, especially in high-speed motion or complex environments. In order to solve the above problems, the present invention proposes a virtual optical axis model update scheme based on continuously adjustable parameters. By introducing a proportional gain matrix, an integral gain function, and a noise compensation term, the scheme can dynamically adjust the direction vector of the virtual optical axis so that it can more accurately simulate the ideal tracking path. This method not only improves the adaptability and robustness of the system, but also enhances the resistance to various uncertain factors, thereby ensuring higher tracking accuracy and stability.

[0129] The options are as follows:

[0130] Optionally, the continuously adjustable parameters include at least: a proportional gain matrix, an integral gain function, and a noise compensation term;

[0131] The process of updating the virtual optical axis model in real time using the continuously adjustable parameters includes applying the following calculation formula:

[0132]

[0133] Among them, V axis (t) is the direction vector of the virtual optical axis at time t, V ideal (t) is the vector pointing in the ideal direction, is the system state θ(t), error e(t) and its first-order derivative over time The proportional gain matrix, P target (t) and P head (t) are the position vector of the tracking target and the position vector of the strapdown seeker, is the integral gain function, which depends on time s, system parameter vector θ(s), error vector e(s) and its first-order derivative and the second-order derivative It also depends on the control input u(s), (V ideal (s)-V actual (s)) is the deviation vector between the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis at time s, ds is the small change of the integral variable s, and N(t, σ(t), w(t), d(t)) is the noise compensation term, which is used to offset the influence of measurement error, external interference w(t) and unmodeled dynamic d(t), and is adaptively adjusted according to the uncertainty σ(t) of the system.

[0134] Proportional Gain Matrix A matrix of proportional coefficients that vary according to time, system state, error, and its first-order derivative, for rapid response to deviations.

[0135] Integral gain function An integrating coefficient function that depends on time, system parameters, the error and its derivative, and the control input is used to eliminate the steady-state error.

[0136] Noise compensation term (N(t,σ(t),w(t),d(t))): A compensation term used to offset measurement errors, external disturbances, and unmodeled dynamic effects, and is adaptively adjusted based on system uncertainty.

[0137] The vector pointing in the ideal direction (V ideal (t)): represents the ideal pointing direction from the strapdown seeker to the tracked target.

[0138] Deviation vector (V ideal (s)-V actual (s)): Indicates the difference between the ideal pointing direction and the actual pointing direction.

[0139] First, an initial virtual optical axis direction vector is set, which can be based on the current strapdown seeker attitude data and target position information. Secondly, the proportional gain matrix is ​​determined according to the current time, system state, error and its first-order derivative. This matrix will be adjusted in real time as these parameters change. Then, the integral gain function is determined using historical data (such as error and its derivative) and the current control input to eliminate long-standing deviations in a cumulative manner. Then, based on the current measurement error, external interference, unmodeled dynamics and system uncertainty, the appropriate noise compensation term is calculated to correct the virtual optical axis model. Finally, the above calculation results are substituted into the formula to obtain the updated virtual optical axis direction vector.

[0140] In the embodiment of the present application, it is assumed that a drone is performing a mission and needs to track a moving car. At a certain time t, the following data is known:

[0141] The position vector P of the strapdown seeker head (t) = (0,0,50) m (height 50 m).

[0142] The position vector P of the target car target (t) = (100, 150, 0) meters.

[0143] The vector V pointing in the ideal direction ideal (t)=(0.632,0.948,-0.316).

[0144] The current error e(t) = 0.144, the first-order derivative of the error

[0145] Assume the proportional gain matrix (where is the identity matrix), the integral gain function The noise compensation term N(t,σ(t),w(t),d(t))=0.001.

[0146] Substitute into the formula to calculate:

[0147]

[0148] After simplification, we get:

[0149]

[0150] Assuming that the integral part accumulates to 0.05 after a period of time, the final virtual optical axis direction vector is:

[0151] V axis (t)=(0.632,0.948,-0.316)+(10,15,-5)+0.05+0.001

[0152] V axis (t) = (10.637, 15.949, -5.315)

[0153] Through the above process, we can see that the new virtual optical axis model not only considers instantaneous deviations, but also cumulative errors and the influence of external interference. This comprehensive approach enables the virtual optical axis to more accurately follow the actual path of the target and maintain high tracking accuracy even in the presence of multiple uncertainties. In addition, the use of the proportional gain matrix and the integral gain function ensures that the system can both quickly respond to deviations and stably eliminate long-term errors, thereby significantly improving the overall tracking performance.

[0154] The present application takes into account that in the existing tracking control system, although the dual-rate controller can handle the fast-changing component and the slow-changing component respectively, how to effectively combine the outputs of the two controllers to form a comprehensive control signal is still a challenge. Directly superimposing the outputs of the two controllers may lead to uncoordinated or unstable behavior, especially in the face of complex environmental interference and system uncertainty. In addition, traditional weighted fusion methods often lack flexibility and are difficult to adapt to the needs under different operating conditions. In order to solve these problems, the present invention proposes a comprehensive control signal generation scheme based on dynamic weight adjustment, cross-term adjustment, adaptive control, robustness compensation and optimization strategy. This method can not only more flexibly combine the outputs of high-rate and low-rate controllers, but also ensure that the overall performance of the system reaches the optimal state by introducing a variety of performance indicators and constraints. In this way, the response speed, stability and anti-interference ability of the system can be improved, thereby achieving a more accurate and stable tracking effect.

[0155] The plan is as follows:

[0156] Optionally, the process of generating the comprehensive control signal includes applying the following calculation formula:

[0157]

[0158] Among them, C total (t) is the comprehensive control signal at time t, C fast (t) and C slow (t) are the correction instructions generated by the high-rate controller and the low-rate controller, is the system state η, error e(t) and its first-order derivative over time The weight coefficient of the performance index p(t) and the constraint condition c(t) changes, is another weight coefficient used to adjust the influence of the cross term, which depends on the system state η, the error e(t) and its first-order derivative It also depends on other performance indicators q(t) and constraints r(t), D cross (t) is the cross term representing the interaction between the high-rate and low-rate controller outputs, U adaptive (t,ξ) is an adaptive control term, ξ is an adaptive control parameter or variable, R(t,ω(t),r(t),f(t)) is a robustness compensation term used to deal with unmodeled dynamics and external uncertainties, where ω(t) represents the estimated value of the external disturbance, r(t) and f(t) are performance indicators and constraints related to robustness, It is an optimization item used to ensure the best control effect while meeting multiple performance indicators. represents a set of performance indicators that need to be optimized, and g(t) is the constraint related to the optimization process.

[0159] Weight coefficient and The proportionality factor is dynamically adjusted according to time, system status, error and its first-order derivative, performance indicators and constraints.

[0160] Cross term (D cross (t)) represents the interaction between the high-rate and low-rate controller outputs to balance the effects of both.

[0161] Adaptive control term (U adaptive (t,ξ)): Control items that are automatically adjusted according to the system operation status to adapt to different working environments.

[0162] Robustness compensation term (R(t,ω(t),r(t),f(t))): is used to deal with unmodeled dynamics and external uncertainties and enhance the robustness of the system.

[0163] Optimization Additional items to ensure optimal control while meeting multiple performance indicators.

[0164] First, the weight coefficient of the high-rate controller output is calculated based on the current time, system state, error and its first-order derivative, performance indicators and constraints. Secondly, similarly, the weight coefficients used to adjust the impact of cross-terms are calculated Then determine the interaction D between the high-rate and low-rate controller outputs cross (t), which can be done by analyzing the correlation between the two or using a predefined function. Then, based on the real-time feedback data of the system, the adaptive control item U is calculated. adaptive (t,ξ) so that it can be adjusted according to the actual situation. Then estimate the external disturbance ω(t) and combine the performance index w(t) and constraint f(t) related to robustness to calculate the robustness compensation term R(t,ω(t),r(t),f(t)). Further based on the performance index to be optimized and related constraints g(t), determine the optimization items To ensure that the system achieves optimal performance under multiple objectives. Finally, substitute the above results into the formula to calculate the final comprehensive control signal C total (t).

[0165] In the embodiment of the present application, it is assumed that a drone is performing a mission and needs to track a moving target. At a certain time t, the following data is known:

[0166] The correction command C generated by the high-speed controller fast(t) = 0.05 rad / s.

[0167] The correction command C generated by the low-speed controller slow (t) = 0.02 rad / s.

[0168] The current error e(t) = 0.144 rad, the first-order derivative of the error

[0169] The system state η(t) includes information such as attitude angle.

[0170] The performance indicator p(t) may be the tracking accuracy, and the constraint condition c(t) may be the maximum allowable rotation speed.

[0171] Cross term D cross (t) = 0.01.

[0172] Adaptive control item U adaptive (t,ξ)=0.005.

[0173] The external disturbance estimation value ω(t)=0.003, the robustness-related performance index r(t)=0.9, and the constraint condition f(y)=0.1.

[0174] Performance indicators in optimization items Constraint g(t) = 0.1. Assume weight coefficient Robustness compensation term R(t,ω(t),r(t),f(t))=0.002, optimization term

[0175] Substitute into the formula to calculate:

[0176]

[0177] After simplification, we get:

[0178] C total (t)=0.7·0.05+(1-0.7)·0.02+0.3·0.01+0.005+0.002+0.001

[0179] C total (t)=0.035+0.006+0.003+0.005+0.002+0.001

[0180] C total (t) = 0.052 rad / s

[0181] Through the above process, it can be seen that the new integrated control signal generation method not only considers the output of high-rate and low-rate controllers, but also makes the final control signal better adapt to the actual operating conditions by dynamically adjusting the weight coefficients, introducing cross terms, adaptive control, robustness compensation and optimization strategies. This comprehensive method improves the response speed and stability of the system, enhances the resistance to unknown interference and uncertainty, and thus significantly improves the overall tracking performance. In addition, through the introduction of optimization terms, the system can achieve the best control effect while meeting multiple performance indicators, further improving the practicality and reliability of the system.

[0182] Figure 2 A schematic diagram of a strapdown seeker anti-disturbance dual-rate control system based on a virtual optical axis is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0183] Establishing module 21, used for establishing a virtual optical axis model, the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target, so as to simulate the ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target;

[0184] A calculation module 22, used for calculating in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model;

[0185] A response module 23, used for using a high-rate controller to respond to the fast-changing component of the deviation vector, and a low-rate controller to respond to the slow-changing component of the deviation vector, and generating corresponding correction instructions respectively;

[0186] The generation module 24 is used to combine the correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

[0187] Figure 2 The strapdown seeker self-disturbance rejection dual-rate control system based on a virtual optical axis can be implemented Figure 1 The implementation principle and technical effect of the strapdown seeker auto-disturbance rejection dual-rate control method based on a virtual optical axis described in the illustrated embodiment will not be repeated. For the strapdown seeker auto-disturbance rejection dual-rate control system based on a virtual optical axis in the above embodiment, the specific manner in which each module and unit performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0188] In one possible design, Figure 2The strapdown seeker self-disturbance rejection dual-rate control system based on a virtual optical axis of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32.

[0189] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0190] The processing component 32 is used to: establish a virtual optical axis model, which is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracked target; calculate the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model in real time; use a high-rate controller to respond to the fast-changing component of the deviation vector, and a low-rate controller to respond to the slow-changing component of the deviation vector, and generate corresponding correction instructions respectively; combine the correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal to drive the strapdown seeker to adjust the posture data.

[0191] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0192] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0193] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0194] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0195] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0196] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0197] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a dual-rate control method for a strapdown seeker self-disturbance rejection based on a virtual optical axis.

[0198] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0199] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0200] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dual-rate control method for strapdown seeker auto-disturbance rejection based on virtual optical axis, characterized in that: include: Establishing a virtual optical axis model, the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target, so as to simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target; Calculating in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model; A high-rate controller is used to respond to the fast-changing component of the deviation vector, and a low-rate controller is used to respond to the slow-changing component of the deviation vector, and corresponding correction instructions are generated respectively; The correction instructions output by the high-rate controller and the low-rate controller are combined to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

2. The method according to claim 1, characterized in that The virtual optical axis model is established, and the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target to simulate the ideal tracking path of the strapdown seeker for the tracking target, including: Obtain the attitude data of the strapdown seeker and the position information of the tracked target; Calculating the actual pointing direction of the strapdown seeker according to the attitude data of the strapdown seeker; Based on the position information of the tracking target and the actual pointing direction of the strapdown seeker, construct an ideal pointing direction of the strapdown seeker as a basis for a virtual optical axis; mapping the difference between the ideal pointing direction and the actual pointing direction of the strapdown seeker into a series of continuously adjustable parameters; The virtual optical axis model is updated in real time using the continuously adjustable parameters to ensure that the virtual optical axis can simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target.

3. The method according to claim 2, characterized in that The real-time calculation of the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model includes: Determining the direction vector of the actual optical axis of the strapdown seeker using the attitude data of the strapdown seeker, wherein the attitude data at least includes a pitch angle, a yaw angle, and a roll angle; Calculating the direction vector of the virtual optical axis at the same time according to the position information of the tracking target and the virtual optical axis model; A vector operation is performed on the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis to obtain a deviation vector, wherein the deviation vector is used to reflect the degree of deviation of the actual optical axis from the ideal tracking path.

4. The method according to claim 3, characterized in that The high-rate controller is used to respond to the fast-changing component of the deviation vector, the low-rate controller is used to respond to the slow-changing component of the deviation vector, and corresponding correction instructions are generated respectively, including: Decomposing the deviation vector into a fast-changing component and a slow-changing component by frequency separation technology; A high-rate controller is applied to process the fast-changing component, wherein the high-rate controller uses a high-bandwidth filter to capture high-frequency components in the deviation vector and generates a correction instruction for the fast-changing component according to the high-frequency components; A low-rate controller is applied to process the slowly changing component. The low-rate controller uses a low-bandwidth filter to capture low-frequency components in the deviation vector and generates a correction instruction for the slowly changing component based on the low-frequency components.

5. The method according to claim 4, characterized in that The method combines the correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, including: Filtering the correction instructions of the fast-changing components generated by the high-rate controller and filtering the correction instructions of the slow-changing components generated by the low-rate controller to maintain the stability of the correction instructions; According to the preset weight distribution strategy, the filtered high-rate correction instructions and low-rate correction instructions are weighted and fused to form a preliminary comprehensive control signal; The preliminary integrated control signal is converted into a control instruction format adapted for execution by the strapdown seeker, and the actual response of the strapdown seeker is monitored through a real-time feedback mechanism to fine-tune the preliminary integrated control signal as needed to obtain an integrated control signal.

6. The method according to claim 2, characterized in that The continuously adjustable parameters include at least: a proportional gain matrix, an integral gain function, and a noise compensation term; The process of updating the virtual optical axis model in real time using the continuously adjustable parameters includes applying the following calculation formula: Among them, V axis (t) is the direction vector of the virtual optical axis at time t, V ideal (t) is the vector pointing in the ideal direction, is the system state θ(t), error e(t) and its first-order derivative over time The proportional gain matrix, P target (t) and P head (t) are the position vector of the tracking target and the position vector of the strapdown seeker, is the integral gain function, which depends on time s, system parameter vector θ(s), error vector e(s) and its first-order derivative and the second-order derivative It also depends on the control input u(s), (V ideal (s)-V actual (s)) is the deviation vector between the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis at time s, ds is the small change of the integral variable s, and N(t, σ(t), w(t), d(t)) is the noise compensation term, which is used to offset the influence of measurement error, external interference w(t) and unmodeled dynamic d(t), and is adaptively adjusted according to the uncertainty σ(t) of the system.

7. The method according to claim 1, characterized in that The process of generating the integrated control signal includes applying the following calculation formula: Among them, C total (t) is the comprehensive control signal at time t, C fast (t) and C slow (t) are the correction instructions generated by the high-rate controller and the low-rate controller, is the system state η, error e(t) and its first-order derivative over time The weight coefficient of the performance index p(t) and the constraint condition c(t) changes, is another weight coefficient used to adjust the influence of the cross term, which depends on the system state η, the error e(t) and its first-order derivative It also depends on other performance indicators q(t) and constraints r(t), D cross (t) is the cross term representing the interaction between the high-rate and low-rate controller outputs, U adaptive R(t,ω(t),r(t),f(t)) is a robustness compensation term used to deal with unmodeled dynamics and external uncertainties. Here, ω(t) represents the estimated value of the external disturbance. r(t) and f(t) are performance indicators and constraints related to robustness. O(t,ζ(t),g(t)) is an optimization term used to ensure that the best control effect is achieved while meeting multiple performance indicators. ζ(t) represents a set of performance indicators that need to be optimized. g(t) is a constraint related to the optimization process.

8. A strapdown seeker auto-disturbance rejection dual-rate control system based on a virtual optical axis, characterized in that: include: An establishment module is used to establish a virtual optical axis model, and the virtual optical axis model is dynamically adjusted according to the posture data of the strapdown seeker and the position information of the tracking target to simulate an ideal tracking path of the strapdown seeker from the current position to the position information of the tracking target; A calculation module, used for calculating in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model; a response module, configured to use a high-rate controller to respond to a fast-changing component of the deviation vector, and a low-rate controller to respond to a slow-changing component of the deviation vector, and to generate corresponding correction instructions respectively; A generation module is used to combine the correction instructions output by the high-rate controller and the low-rate controller to generate a comprehensive control signal to drive the strapdown seeker to adjust the attitude data so that the actual optical axis of the strapdown seeker tends to the virtual optical axis model, thereby achieving accurate tracking of the tracking target.

9. A computing device, characterized in that It comprises a processing component and a storage component, wherein the storage component stores one or more computer instructions, and the one or more computer instructions are used to be called and executed by the processing component to implement a strapdown seeker self-anti-disturbance dual-rate control method based on a virtual optical axis as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a strapdown seeker self-disturbance rejection dual-rate control method based on a virtual optical axis as claimed in any one of claims 1 to 7 is implemented.

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