A Disturbance Rejection Double-Rate Control Method and System for Strapdown Seeker Based on Virtual Optical Axis

Abstract

The present application provides an auto-disturbance rejection dual-rate control method and system for a strapdown seeker based on a virtual optical axis. Among them, 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 commands are generated respectively; the correction commands 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 improve the tracking performance of the strapdown seeker.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of aerospace technology, and particularly to an active disturbance rejection dual-rate control method and system for a strapdown seeker based on a virtual optical axis. Background Art

[0002] In modern military, aerospace, and civilian surveillance fields, precise target tracking capabilities are crucial for ensuring mission success. Especially in applications such as long-range guided weapon systems, unmanned aerial vehicle (UAV) reconnaissance and strike platforms, and satellite communication antenna alignment, the strapdown seeker, as one of the key components, directly affects the performance of the entire system. These application scenarios typically require the seeker to quickly and accurately lock onto and continuously track moving or stationary targets, maintaining a stable tracking state even in the face of complex environmental disturbances.

[0003] Currently, traditional seeker control methods mainly rely on a single closed-loop feedback control system to achieve target tracking. Such systems continuously measure the error between the actual output (such as the seeker's pointing direction) and the expected value, and accordingly adjust the actions of the actuator to gradually reduce the deviation until satisfactory tracking accuracy is achieved.

[0004] Although the above traditional methods can meet the basic application requirements to a certain extent, they also have obvious limitations. When encountering high-speed moving targets or rapidly changing environmental conditions, a single controller architecture is difficult to simultaneously meet the requirements of both rapid response and long-term stability, often resulting in poor tracking performance. Summary of the Invention

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

[0006] In a first aspect, the embodiments of the present application provide an active disturbance rejection dual-rate control method for a strapdown seeker based on a virtual optical axis, including:

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

[0008] Real-time calculating the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model;

[0009] Using a high-rate controller to respond to the fast-changing components of the deviation vector and a low-rate controller to respond to the slow-changing components of the deviation vector, and respectively generating corresponding correction instructions;

[0010] 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, realizing the precise tracking of the tracking target.

[0011] Optionally, for establishing the virtual optical axis model, 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, and is used to simulate the ideal tracking path of the strapdown seeker for the tracking target, including:

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

[0013] Calculate 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 the ideal pointing direction of the strapdown seeker as the basis of the virtual optical axis;

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

[0016] Use 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.

[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] Use the attitude data of the strapdown seeker to determine the direction vector of the actual optical axis of the strapdown seeker, and the attitude data at least includes pitch angle, yaw angle and roll angle;

[0019] According to the position information of the tracking target and the virtual optical axis model, calculate the direction vector of the virtual optical axis at the same moment;

[0020] Perform 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 deviation degree of the actual optical axis relative to the ideal tracking path.

[0021] Optionally, the high-rate controller is used to respond to the fast-changing component of the deviation vector, and 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:

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

[0023] For the fast-changing component, apply 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 a correction instruction for the fast-changing component according to the high-frequency components;

[0024] For the slow-changing component, apply 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 a correction instruction for the slow-changing component based on the low-frequency components.

[0025] Optionally, combining 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, includes:

[0026] Filter the correction instruction of the fast-changing component generated by the high-rate controller and filter the correction instruction of the slow-changing component generated by the low-rate controller to maintain the stability of the correction instruction;

[0027] According to a preset weight distribution strategy, perform weighted fusion on the filtered high-rate correction instruction and low-rate correction instruction to form a preliminary comprehensive control signal;

[0028] Convert the preliminary comprehensive control signal into a control instruction format suitable for the execution of the strapdown seeker, and monitor 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.

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

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

[0031]

[0032] Where, V axis (t) is the direction vector of the virtual optical axis at time t, V ideal (t) is the vector of the ideal pointing direction, is the proportional gain matrix that changes with time, system state θ(t), error e(t) and its first derivative , P target (t) and P head(t) is the position vector of the tracking target and the position vector of the strapdown seeker respectively, is the integral gain function, which depends on time s, system parameter vector θ(s), error vector e(s) and its first derivative and second derivative 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 and the direction vector of the virtual optical axis of the strapdown seeker 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 cancel the influence of measurement error, external disturbance w(t) and unmodeled dynamics 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] where, C total (t) is the comprehensive control signal at time t, C fast (t) and C slow (t) are the correction commands generated by the high-rate controller and the low-rate controller respectively, is the weight coefficient that changes with time, system state η, error e(t) and its first derivative performance index p(t) and constraint condition c(t), is another weight coefficient, which is used to adjust the influence of the cross term, and depends on the system state η, error e(t) and its first derivative also depends on other performance index q(t) and constraint condition r(t), D cross (t) is the cross term representing the interaction between the outputs of the high-rate and low-rate controllers, U adaptive (t, ξ) is the adaptive control term, ξ is the adaptive control parameter or variable, R(t, ω(t), r(t), f(t)) is the robustness compensation term, which is used to handle unmodeled dynamics and external uncertainties. Here, ω(t) represents the estimated value of the external disturbance, and r(t) and f(t) are the performance index and constraint condition related to robustness, is the optimization term, which is used to ensure the best control effect while meeting multiple performance indexes, represents a set of performance indexes to be optimized, and g(t) is the constraint condition related to the optimization process.

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

[0037] A building module, configured to build a virtual optical axis model, which 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;

[0038] A calculation module, configured to calculate in real time a 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 respectively generate corresponding correction instructions;

[0040] A generation module, configured 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, and realize accurate tracking of the tracking target.

[0041] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component, where 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 self-disturbance rejection dual-rate control method for a strapdown seeker based on a virtual optical axis as described in any item of the first aspect above.

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

[0043] In the embodiment of the present application, a virtual optical axis model is built, 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 respectively generated; 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 on the premise of maintaining the system stability, and is suitable for applications in occasions that require high-precision fast response and good anti-interference characteristics.

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

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

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

[0047] Figure 2 It is a schematic structural diagram of a self-disturbance rejection dual-rate control system for a strapdown seeker based on a virtual optical axis provided by an embodiment of the present application.

[0048] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0050] In some processes described in the specification, claims and above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The operation numbers such as 101, 102, etc. are only used to distinguish different operations, and the 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 such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are different types.

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0052] Figure 1The present application provides a flowchart of an auto-disturbance rejection dual-rate control method for a strapdown seeker based on a virtual optical axis, as shown in Figure 1 The method includes:

[0053] 101. Establish a virtual optical axis model, which is dynamically adjusted according to the attitude data of the strapdown seeker and the position information of the tracking target, and is used to simulate the 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 establishes 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 tracking 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 an 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 updates the virtual optical axis model using the latest GPS coordinates and other information to ensure that it always points in the correct direction.

[0056] The present application takes into account that in existing seeker tracking systems, 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 rapidly or the environmental conditions change, the fixed optical axis model cannot adapt to the new situation in a timely manner, resulting in a decrease in tracking accuracy or even loss of the target. In addition, traditional methods may not fully consider the impact of the change in the attitude of the strapdown seeker itself on the tracking effect, which will also reduce the response speed and accuracy of the system. To solve the above problems, the present invention proposes a virtual optical axis model scheme based on dynamic adjustment. By obtaining and processing the attitude data of the strapdown seeker and the position information of the tracking target in real time, an ideal pointing direction that can reflect the current optimal tracking path is constructed, and the virtual optical axis model is continuously updated accordingly. Such a design can significantly improve the flexibility and adaptability of the system, thereby achieving more accurate and stable tracking performance.

[0057] The specific optional solution is as follows:

[0058] Optionally, "establishing the virtual optical axis model, which is dynamically adjusted according to the attitude 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 attitude 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 attitude 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 without mechanical scanning components that can provide continuous target detection and tracking.

[0060] Attitude data: Data describing the spatial position and orientation of an object relative to a certain reference coordinate system.

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

[0062] Ideal pointing direction: The best aiming direction calculated according to the target position.

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

[0064] First, collect the attitude data of the strapdown seeker (such as pitch angle, yaw angle, etc.) and the exact position of the tracking target (such as latitude and longitude coordinates). Second, calculate the current actual pointing direction of the strapdown seeker using the obtained attitude data. Further, combine the target position information with the actual pointing direction of the strapdown seeker to calculate the best pointing direction, that is, the ideal pointing direction. Then convert the gap between the ideal pointing direction and the actual pointing direction into a series of easily controllable parameter forms. Finally, use the parameters obtained in the previous step 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 an embodiment of the present application, assume that a drone equipped with a strapdown seeker is performing a task and needs to follow a car traveling at a speed of 60 km / h. Assume that the altitude of the drone is 50 m and the horizontal distance between the two at the initial moment is 100 m.

[0066] At t = 0 seconds, the drone receives a GPS signal indicating that the car is located at 40.7128°N, 74.0060°W, while the drone itself is at 40.7130°N, 74.0055°W. At this time, according to the attitude sensor of the drone, the angle pointed directly in front of it is 30° east of south.

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

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

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

[0070] This solution overcomes the problem of slow response of traditional methods in the face of rapidly changing scenarios by establishing a dynamic adjustment mechanism, and also improves the robustness and accuracy of the entire system. Especially for those application scenarios that require high-precision tracking (such as military reconnaissance, disaster relief, etc.), this improvement is particularly important.

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

[0072] In this step, it involves real-time monitoring of the difference between the actual optical axis and the ideal path. By comparing the actual pointing of the current strapdown seeker with the direction indicated by the virtual optical axis model, a deviation vector can be obtained, representing the angular or displacement gap between the two.

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

[0074] In the existing seeker tracking system, the deviation calculation method based on a preset model is usually adopted. This method often relies on fixed geometric relationships or simplified assumption conditions 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 movement and environmental interference, this static method is difficult to accurately reflect the gap between the real-time actual state of the strapdown seeker and its ideal state. This will lead to the accumulation of tracking errors and ultimately affect the overall performance of the system. To solve the above problems, 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 of the strapdown seeker and the virtual optical axis model in real time, and performs vector operations to obtain the deviation vector. This method can more truly reflect the degree of deviation of the actual optical axis relative to the ideal tracking path, so that the control system can make more accurate and timely adjustments, improving the response speed and tracking accuracy of the entire system.

[0075] The optional solution is 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: determining the direction vector of the actual optical axis of the strapdown seeker by using the attitude data of the strapdown seeker, where the attitude data at least includes pitch angle, yaw angle and roll angle; calculating the direction vector of the virtual optical axis at the same moment according to the position information of the tracking target and the virtual optical axis model; performing vector operations on the direction vector of the actual optical axis of the strapdown seeker and the direction vector of the virtual optical axis to obtain the deviation vector, and the deviation vector is used to reflect the degree of deviation of the actual optical axis relative to the ideal tracking path.

[0077] Pitch: The angle by which an object rotates around the X-axis.

[0078] Yaw: The angle by which an object rotates around the Z-axis.

[0079] Roll: The angle by which an object rotates around the Y-axis.

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

[0081] Vector operation: 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. Using the pitch angle, yaw angle, and roll angle information provided by the strapdown seeker, construct a direction vector representing the current actual pointing direction. Secondly, calculate the virtual optical axis direction. Based on the position information of the tracking target and the previously established virtual optical axis model, obtain the ideal pointing direction vector at the same moment. Further, perform vector operations to find the deviation. By performing vector subtraction on the actual optical axis direction vector and the virtual optical axis direction vector, obtain the deviation vector, which directly reflects the degree to which the actual optical axis deviates from the ideal path.

[0084] In an embodiment of the present application, consider a drone tracking a moving target vehicle on the ground. Assume that at a certain moment 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, the position coordinates of the target vehicle are known to be (100, 150) meters (relative to the current position of the drone) at this time. Using this information, the following steps can be taken:

[0089] Determine the actual optical axis direction: First, convert the attitude angles to a direction vector. For the given attitude angles, the formula can be used to obtain the direction vector of the actual optical axis. Substituting the specific values gives

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

[0091] Vector operations 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 traditional methods, this method not only improves the accuracy of deviation calculation, but also enhances the adaptability and robustness of the system. Especially in complex and changing working environments, it helps to achieve a higher level of tracking performance. In addition, the clear deviation vector also provides direct data support for subsequent control strategies, facilitating the quick and effective correction of any deviation to ensure a continuous and stable tracking effect.

[0093] 103. A high-rate controller responds to the fast-changing component of the deviation vector, and a low-rate controller responds to the slow-changing component of the deviation vector, and corresponding correction instructions are generated respectively.

[0094] In this step, in order to effectively handle errors of different natures, two types of controllers are adopted here: 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 generates corresponding correction measures according to its characteristics.

[0095] In the embodiment of the present application, it is assumed that when encountering an instantaneous large offset caused by sudden atmospheric turbulence, the high-rate controller will quickly respond and issue an immediate adjustment command to correct this situation. At the same time, for the slight cumulative error caused by long-term flight, it is slowly fine-tuned by the low-rate controller.

[0096] This application takes into account that in traditional tracking control systems, usually a single controller is used to handle all changes in the deviation vector. However, such a design often fails to meet the requirements of both fast response and stable control at the same time. For example, if the controller is too sensitive, it may overreact to noise or minor 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, in the face of a deviation vector that contains both high-frequency (fast) and low-frequency (slow) components, traditional methods are difficult to achieve an ideal control effect. To solve this problem, the present invention proposes a dual-rate controller scheme based on frequency separation technology. By decomposing the deviation vector into a fast-changing component and a slow-changing component, and processing them with a high-rate controller and a low-rate controller respectively, different types of errors can be managed more effectively, thereby improving the overall performance of the system.

[0097] The specific implementation of this optional solution is as follows:

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

[0099] Frequency separation technique: A signal processing method used to divide an input signal into multiple parts according to different frequency ranges.

[0100] High - bandwidth filter: A filter that allows higher - frequency signals to pass through while suppressing lower - frequency signals.

[0101] Low - bandwidth filter: A filter that allows lower - frequency signals to pass through while suppressing higher - frequency signals.

[0102] Correction instruction: A command sent to the actuator to adjust the system state according to the result calculated by the controller.

[0103] First, use the frequency separation technique, such as Fourier transform or other suitable methods, to decompose the deviation vector into a fast - changing component and a slow - changing component. Second, use a high - bandwidth filter to capture the high - frequency components in the deviation vector. Generate correction instructions for the fast - changing component based on these high - frequency components. Finally, use a low - bandwidth filter to capture the low - frequency components in the deviation vector. Generate correction instructions for the slow - changing component based on these low - frequency components.

[0104] In the embodiment of this application, assume that a drone is tracking a moving target. At a certain moment, the deviation vector detected by the strap - down seeker For the sake of simplicity, only consider the data of one dimension, for example, the deviation value of 0.144 in the X - axis direction.

[0105] First, assume that the Fourier transform is used to analyze the deviation data 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. Second, apply a high - pass filter (for example, the cut - off frequency is 1 Hz), and the high - frequency component is about 0.05. The high - rate controller generates a correction instruction based on this high - frequency component, such as immediately adjusting the pitch angle to reduce the instantaneous deviation. Finally, apply a low - pass filter (for example, the cut - off frequency is 0.1 Hz), and the low - frequency component is about 0.094. The low - rate controller generates a correction instruction based on this low - frequency component, such as gradually adjusting the flight path to maintain a stable tracking state in the long term.

[0106] The design of this dual - rate controller can effectively distinguish and process different frequency components in the deviation vector, enabling the system to not only quickly respond to emergencies but also smoothly correct persistent deviations. This method not only improves the tracking accuracy but also enhances the stability of the system, especially outstanding in complex and changeable 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. 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, realizing the precise tracking of the tracking target.

[0108] In this step, the last step is to integrate the outputs from the two controllers to form a complete control strategy. This strategy will be used to guide how the strapdown seeker adjusts its own attitude to minimize or even eliminate all types of errors mentioned above, so as to maintain the best tracking state of the target.

[0109] In the embodiment of the present application, it is assumed that in actual operation, this means that the control system will comprehensively consider two aspects of suggestions - both to respond promptly to emergencies and to steadily solve long-term problems. For example, it may first perform some emergency steering actions to counter the influence of strong winds, and then gradually adjust back to a more stable flight mode to ensure that the UAV can follow the ground vehicle smoothly and accurately.

[0110] The present application takes into account that in the existing tracking control system, although the dual-rate controller can process the fast-changing component and the slow-changing component separately, directly combining the outputs of the two controllers may introduce instability or conflicts between control instructions. For example, if the high-rate controller frequently issues large-amplitude correction instructions, while the low-rate controller provides relatively gentle adjustments, there may be a cancellation 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 completely matched, thus affecting the coordination and performance of the overall system. To solve the above problems, the present invention proposes a comprehensive control signal generation scheme. By filtering, weighted fusion, and real-time feedback mechanisms to optimize the outputs of the high-rate controller and the low-rate controller, ensuring that the finally generated comprehensive control signal can quickly respond to sudden deviations and stably perform long-term adjustments, so as to achieve the precise tracking of the tracking target.

[0111] The specific scheme is as follows:

[0112] Optionally, "combining 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" 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; performing weighted fusion on the filtered high-rate correction instructions and low-rate correction instructions according to a preset weight allocation strategy to form a preliminary comprehensive control signal; converting the preliminary comprehensive control signal into a control instruction format suitable for the execution of 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 process: A signal processing technique used to remove noise or unnecessary frequency components.

[0114] Weight allocation strategy: Assigning corresponding weight values to different input signals according to specific rules to determine their influence in the final result.

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

[0116] Real-time feedback mechanism: A technique for monitoring the actual response of a system and making adjustments accordingly.

[0117] The filtering process is to filter the correction instructions generated by the high-rate controller to reduce noise interference. Filter the correction instructions generated by the low-rate controller to ensure its smoothness.

[0118] Weighted fusion is to perform weighted summation on the 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 formulas) to form a preliminary comprehensive control signal.

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

[0120] In the embodiment of the present application, assume that a drone 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°, and the low-rate controller generates another correction instruction Δθ low = 0.02°.

[0121] Process Δθ using a simple low - pass filter high to obtain the filtered high - rate correction command Δθ' high = 0.04°.

[0122] Similarly, apply a low - pass filter to Δθ low to obtain the filtered low - rate correction command Δθ' los = 0.018°.

[0123] Assume that the preset weight allocation strategy is that the high - rate command accounts for 70% and the low - rate command accounts for 30%. Then the preliminary comprehensive control signal Δθ combined can be calculated according to 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] Convert Δθ combined into a control command format suitable for use by the strap - down seeker, such as sending a pitch - angle adjustment command.

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

[0127] This method of generating the comprehensive control signal not only improves the reliability of the output of a single controller, but also makes the final control signal more adaptable to the actual working conditions through a reasonable weight allocation strategy and a 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 when facing a complex and changeable environment, this method can more effectively cope with various challenges and ensure continuous and accurate tracking effects.

[0128] In this application, considering 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 factors such as environmental interference, measurement errors, and the nonlinear characteristics of the system itself, such models often struggle to accurately reflect the dynamic relationship between the strapdown seeker and the target. This can lead to a decrease in tracking accuracy, especially in high-speed motion or complex environments. To address 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, this scheme can dynamically adjust the direction vector of the virtual optical axis to more precisely 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 specific optional scheme is as follows:

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

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

[0132]

[0133] where, V axis (t) is the direction vector of the virtual optical axis at time t, V ideal (t) is the vector of the ideal pointing direction, is the proportional gain matrix that varies with time, the system state θ(t), the error e(t), and its first derivative P target (t) and P head (t) are respectively the position vectors of the tracking target and the strapdown seeker, is the integral gain function, which depends on time s, the system parameter vector θ(s), the error vector e(s), and its first derivative and second derivative and 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 infinitesimal change of the integral variable s, and N(t, σ(t), w(t), d(t)) is the noise compensation term, which is used to cancel the effects of measurement errors, external interference w(t), and unmodeled dynamics d(t), and is adaptively adjusted according to the uncertainty σ(t) of the system.

[0134] Proportional gain matrix A proportional coefficient matrix that varies according to time, system state, error, and its first derivative, used to quickly respond to deviations.

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

[0136] Noise compensation term (N(t, σ(t), w(t), d(t))): A compensation term used to cancel measurement errors, external disturbances, and the effects of unmodeled dynamics, and adaptively adjust according to system uncertainties.

[0137] Vector of the ideal pointing direction (V ideal (t)): Represents the ideal pointing direction from the strapdown seeker to the tracking target.

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

[0139] First, set an initial virtual optical axis direction vector, which can be based on the current strapdown seeker attitude data and target position information. Secondly, determine the proportional gain matrix according to the current time, system state, error, and its first derivative, and this matrix will be adjusted in real time as these parameters change. Then, use historical data (such as error and its derivative) and the current control input to determine the integral gain function to eliminate long-term deviations in a cumulative manner. Next, calculate an appropriate noise compensation term based on the current measurement error, external disturbance, unmodeled dynamics, and system uncertainties to correct the virtual optical axis model. Finally, substitute the calculation results of the above items into the formula to obtain the updated virtual optical axis direction vector.

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

[0141] Position vector P of the strapdown seeker head (t) = (0, 0, 50) meters (altitude 50 meters).

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

[0143] Vector of the ideal pointing direction V ideal (t) = (0.632, 0.948, -0.316).

[0144] Current error e(t) = 0.144, first 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 for calculation:

[0147]

[0148] After simplification, we get:

[0149]

[0150] Assume that the integral part accumulates to 0.05 after a period of time, then 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, it can be seen that the new virtual optical axis model not only considers the instantaneous deviation, but also takes into account the cumulative error and the influence of external disturbances. This comprehensive method enables the virtual optical axis to more accurately follow the actual path of the target and maintain a 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 not only quickly respond to deviations but also stably eliminate long-term errors, thus significantly improving the overall tracking performance.

[0154] In the existing tracking control systems, although the dual-rate controller can process fast-changing components and slow-changing components separately, how to effectively combine the outputs of these two controllers to form a comprehensive control signal remains a challenge. Directly adding the outputs of the two controllers may lead to inconsistent or unstable behavior, especially in the face of complex environmental disturbances and system uncertainties. In addition, traditional weighted fusion methods often lack flexibility and are difficult to adapt to the requirements under different operating conditions. To address these issues, the present invention proposes a comprehensive control signal generation scheme based on dynamic weight adjustment, cross-term regulation, adaptive control, robustness compensation, and optimization strategies. This method can not only more flexibly combine the outputs of the high-rate and low-rate controllers, but also ensure that the overall performance of the system reaches the optimal state by introducing multiple performance indicators and constraint conditions. This can improve the response speed, stability, and anti-interference ability of the system, thereby achieving a more accurate and stable tracking effect.

[0155] The specific scheme is as follows:

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

[0157]

[0158] where, 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 respectively, is the weight coefficient that varies with time, system state η, error e(t) and its first derivative performance index p(t) and constraint condition c(t), is another weight coefficient used to adjust the influence of the cross-term, depending on the system state η, error e(t) and its first derivative also depending on other performance index q(t) and constraint condition r(t), D cross (t) is the cross-term representing the interaction between the outputs of the high-rate and low-rate controllers, U adaptive (t, ξ) is the adaptive control term, ξ is the adaptive control parameter or variable, R(t, ω(t), r(t), f(t)) is the robustness compensation term for dealing with unmodeled dynamics and external uncertainties, where ω(t) represents the estimated value of the external disturbance, r(t) and f(t) are the performance index and constraint condition related to robustness, is the optimization term for ensuring the best control effect while satisfying multiple performance indicators, Denote a set of performance metrics to be optimized, and \(g(t)\) is the constraint condition related to the optimization process.

[0159] Weight coefficient and A proportionality coefficient that is dynamically adjusted according to time, system state, error and its first derivative, performance metrics, and constraint conditions.

[0160] Cross-term (\(D\) cross (t)): Represents the interaction between the high-rate and low-rate controller outputs to balance their effects.

[0161] Adaptive control term (\(U\) adaptive (t, ξ)): A control term that is automatically adjusted according to the system operation to adapt to different working environments.

[0162] Robustness compensation term (\(R(t, ω(t), r(t), f(t))\): Used to handle unmodeled dynamics and external uncertainties to enhance the robustness of the system.

[0163] Optimization term An additional term that ensures the best control effect while satisfying multiple performance metrics.

[0164] First, according to the current time, system state, error and its first derivative, performance metrics, and constraint conditions, calculate the weight coefficient of the high-rate controller output Secondly, similarly, calculate the weight coefficient for adjusting the influence of the cross-term Then determine the interaction \(D\) between the high-rate and low-rate controller outputs cross (t), which can be accomplished by analyzing their correlation or using a predefined function. Then, based on the real-time feedback data of the system, calculate the adaptive control term \(U\) adaptive (t, ξ) so that it can be adjusted according to the actual situation. Then estimate the external disturbance \(ω(t)\) and combine it with the performance metric \(w(t)\) related to robustness and the constraint condition \(f(t)\) to calculate the robustness compensation term \(R(t, ω(t), r(t), f(t))\). Further, based on the performance metrics to be optimized and the related constraint condition \(g(t)\), determine the optimization term to ensure that the system achieves the best 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 this application, assume that a drone is performing a mission and needs to track a moving target. At a certain moment \(t\), the following data is known:

[0166] The correction instruction \(C\) generated by the high-rate controller fastω(t) = 0.05 rad / s.

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

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

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

[0170] The performance index p(t) can be the tracking accuracy, and the constraint condition c(t) can be the maximum allowable rotational speed.

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

[0172] The adaptive control term U adaptive ω(t, ξ) = 0.005.

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

[0174] The performance index in the optimization term The constraint condition g(t) = 0.1. Assume the weight coefficient The robustness compensation term R(t, ω(t), r(t), f(t)) = 0.002, the optimization term

[0175] Substitute into the formula for calculation:

[0176]

[0177] After simplification, we get:

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

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

[0180] C total ω(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 outputs of the high-rate and low-rate controllers, but also enables the final control signal to better adapt to the actual operating conditions by dynamically adjusting the weight coefficients, introducing cross terms, adaptive control, robustness compensation, and optimization strategies. This integrated method improves the response speed and stability of the system, enhances the resistance to unknown disturbances and uncertainties, and thus significantly improves the overall tracking performance. In addition, by introducing the optimization term, the system can achieve the best control effect while meeting multiple performance indicators, further enhancing the practicality and reliability of the system.

[0182] Figure 2 FIG. is a schematic structural diagram of an active disturbance rejection dual-rate control system for a strapdown seeker based on a virtual optical axis provided by an embodiment of the present application, as Figure 2 shown, the device includes:

[0183] A building module 21 for building a virtual optical axis model, which is dynamically adjusted according to the attitude data of the strapdown seeker and the position information of the tracking target, and is used 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 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 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 respectively generating corresponding correction instructions;

[0186] A generation module 24 for combining the correction instructions output by the high-rate controller and the low-rate controller to generate an integrated 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, and realizing accurate tracking of the tracking target.

[0187] Figure 2 The active disturbance rejection dual-rate control system for a strapdown seeker based on a virtual optical axis can execute Figure 1 The active disturbance rejection dual-rate control method for a strapdown seeker based on a virtual optical axis described in the embodiment shown, and its implementation principle and technical effects will not be elaborated. For the active disturbance rejection dual-rate control system for a strapdown seeker based on a virtual optical axis in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0188] In a possible design, Figure 2A disturbance rejection dual-rate control system for a strapdown seeker based on a virtual optical axis in the illustrated embodiment 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 configured to: establish a virtual optical axis model, which is dynamically adjusted according to the attitude data of the strapdown seeker and the position information of the tracking target; calculate in real time the deviation vector between the actual optical axis of the strapdown seeker and the virtual optical axis model; employ a high-rate controller to respond to the fast-changing components of the deviation vector and a low-rate controller to respond to the slow-changing components 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 attitude data.

[0191] Among them, 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 for executing the above method.

[0192] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may 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 disc.

[0193] Of course, the computing device may also necessarily 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 peripheral interface module may be an output device, an input device, etc.

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

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

[0197] An embodiment of the present application also provides a computer storage medium storing a computer program, which when executed by a computer can implement the above Figure 1 A self-disturbance rejection dual-rate control method for a strapdown seeker based on a virtual optical axis shown in the embodiment.

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

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

[0200] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment 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 essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions for causing 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 and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment 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; 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; 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.

2. The method according to claim 1, 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.

3. The method according to claim 2, 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.

4. The method according to claim 3, 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.

5. The method according to claim 1, 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 slight 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 errors, external interference w(t) and unmodeled dynamics d(t), and perform adaptive adjustment according to the uncertainty σ(t) of the system.

6. 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.

7. A strapdown seeker anti-disturbance dual-rate control system based on 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 generating module, for combining the correction instructions output by the high rate controller and the low rate controller, and generating 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; 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.

8. 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 6.

9. 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 anti-disturbance dual-rate control method based on a virtual optical axis as claimed in any one of claims 1 to 6 is implemented.

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