A dynamic parameter control method and system for unmanned aerial vehicle nacelle tracking

Abstract

The application discloses a dynamic parameter control method and system for unmanned aerial vehicle nacelle tracking, wherein the method comprises the following steps: acquiring a target image collected by an unmanned aerial vehicle nacelle, determining a corresponding target line-of-sight angle based on the target image; the target line-of-sight angle is used to indicate a line-of-sight angle deviation corresponding to a current line-of-sight angle and an expected line-of-sight angle; calculating a corresponding modulation coefficient based on the target line-of-sight angle, a preset linear region width adjustment factor and a preset transition characteristic adjustment factor; calculating a corresponding nacelle expected rotating speed based on the target line-of-sight angle, the modulation coefficient and a preset basic control parameter, and driving the nacelle based on the nacelle expected rotating speed, wherein the basic control parameter comprises a maximum proportional gain and an integral term gain. The application dynamically adjusts the nacelle control parameter through the line-of-sight angle deviation, realizes fast non-overshoot pullback when a target large field of view deviates and stable tracking when a small field of view deviates, and effectively improves the nacelle tracking robustness and the unmanned aerial vehicle situation awareness accuracy.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) pod tracking technology, and in particular to a dynamic parameter control method and system for tracking UAV pods. Background Technology

[0002] As a core mission payload carried by UAVs, one of the core functions of the pod is to continuously and stably track selected targets on the ground or in the air. This tracking process mainly relies on photoelectric sensors to acquire target images in real time, processing the images through target tracking algorithms to identify and lock the target's position, and then adjusting the pod's attitude based on the target's position information in the image to keep the target in the center of the image or within a preset region of interest (ROI), thereby achieving stable tracking.

[0003] In practical applications of UAV pod tracking, the target's motion is uncertain, exhibiting both slow, small-range movements and rapid, large-range shifts. This places extremely high demands on the adaptability of the pod's tracking control parameters. The selection of control parameters directly determines the tracking performance: if the control gain is set too low, the pod's response speed cannot match the target's movement speed, easily leading to the target being lost from the field of view; if the control gain is set too high, the pod will overreact, causing drastic fluctuations in the target's position in the image, also resulting in tracking failure. Therefore, the selection of control gain must ensure tracking performance while avoiding tracking instability caused by excessive gain. Summary of the Invention

[0004] The purpose of this invention is to solve the problem that in the prior art, when tracking UAV pods, the use of fixed control parameters cannot adapt to different degrees of target offset, resulting in poor tracking stability or target loss. This invention proposes a dynamic parameter control method and system for tracking UAV pods.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, a dynamic parameter control method for tracking a drone pod is provided, comprising the following steps: The target image is acquired by the UAV pod, and the corresponding target line-of-sight angle is determined based on the target image; the target line-of-sight angle is used to indicate the line-of-sight angle deviation between the current line-of-sight angle and the desired line-of-sight angle. The corresponding modulation coefficients are calculated based on the target line-of-sight angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor. Based on the target line-of-sight angle, the modulation coefficient, and the preset basic control parameters, the corresponding desired rotational speed of the pod is calculated, and the pod is driven based on the desired rotational speed of the pod. The basic control parameters include the maximum proportional gain and the integral term gain.

[0006] As one possible implementation method, the formula for calculating the modulation coefficient is: ; Where M is the modulation coefficient, and δ is the linear region width adjustment factor; λ is the transition characteristic adjustment factor; Δq t The target line-of-sight angle.

[0007] As one possible implementation method, the formula for calculating the desired rotational speed of the pod is: ; Among them, u t The desired rotational speed of the pod is represented by M, and the corresponding modulation coefficient is represented by k. m k represents the maximum proportional gain. i This represents the gain of the integral term.

[0008] As one possible implementation method, the formula for calculating the target line-of-sight angle is: Target line of sight angle Δq t The calculation method is as follows: ; Where f represents the focal length of the photoelectric sensor; Δd t represents the target pixel offset; 'a' represents the conversion factor from radians to degrees. Wherein, the target pixel coordinates are (u t ,v t The pixel coordinates of the image center point are (c x ,c y ), target pixel offset Δd t The calculation method is as follows: .

[0009] As one possible implementation, it also includes the steps of sequentially adjusting the maximum proportional gain, the linear region width adjustment factor, the transient characteristic adjustment factor, and the integral term gain based on the tuning instructions.

[0010] As one possible implementation method, the steps for tuning the maximum proportional gain are as follows: Initialize the linear region width adjustment factor, transient characteristic adjustment factor, integral term gain, and maximum proportional gain. Use a step signal for testing, with the step amplitude set to the maximum line-of-sight deviation. Based on the pod's response state, gradually increase the proportional gain from the initial value until the step response reaches critical oscillation. Determine the target maximum proportional gain k based on the proportional gain corresponding to the critical oscillation of the step response. m .

[0011] As one possible implementation method, the steps for tuning the linear region width adjustment factor are as follows: The initial value of the linear zone width adjustment factor is determined based on the pod's field of view. Based on the initial transient characteristic adjustment factor and integral term gain, and the maximum proportional gain after tuning, step response tests were conducted using large line-of-sight deviation and small line-of-sight deviation respectively. In the large deviation step test, if the response overshoot exceeds the preset first overshoot threshold or oscillation occurs, the linear region width adjustment factor is increased. If the rise time exceeds the preset rise time threshold, the response is judged to be slow, and the linear region width adjustment factor is decreased. This continues until the rise time in the large deviation response is less than or equal to the preset rise time threshold, the response overshoot is less than or equal to the preset first overshoot threshold, and there is no oscillation. In the small deviation step test, if the adjustment time exceeds the preset first adjustment time threshold, the response is judged to be sluggish, and the linear region width adjustment factor is increased; if the amplitude exceeds the preset first amplitude threshold, the continuous micro-oscillation is judged to occur, and the linear region width adjustment factor is decreased; until the adjustment time in the small deviation response is less than or equal to the first preset adjustment time threshold, and the amplitude of the control quantity fluctuation in steady state is less than or equal to the preset first fluctuation threshold.

[0012] As one possible implementation method: Based on the initial transient characteristic adjustment factor and integral term gain, as well as the tuned maximum proportional gain and linear region width adjustment factor, step response tests were conducted using large line-of-sight deviation and small line-of-sight deviation, respectively. In the large deviation step test, if the screen stutters due to changes in angular velocity, the transition characteristic adjustment factor is reduced to achieve a smooth transition; if the response is slow or oscillating, the transition characteristic adjustment factor is increased, where the rise time exceeds the preset rise time threshold to determine a slow response; until the response convergence process is smooth, the pod screen has no visible stutters or oscillations, and the rise time in the large deviation response is less than or equal to the preset rise time threshold. In the small deviation step test, if the amplitude exceeds the preset second amplitude threshold, it is determined that a slight oscillation has occurred, and the transition characteristic adjustment factor is reduced. If the adjustment time exceeds the preset second adjustment time threshold, it is determined that the response is sluggish, and the transition characteristic adjustment factor is increased. This continues until the adjustment time in the small deviation response is less than or equal to the preset second adjustment time threshold, and the amplitude of the control quantity fluctuation in steady state is less than or equal to the preset second fluctuation threshold.

[0013] As one possible implementation method, the steps for tuning the integral term gain are as follows: Based on the calibrated maximum proportional gain, linear region width adjustment factor, and transition characteristic adjustment factor, a step signal test is performed; during the step signal test, the integral term gain k is set... iStarting from the minimum value, the value is gradually increased. Based on the pod response state, when the static deviation is less than or equal to the preset deviation threshold, the step response overshoot is less than or equal to the preset second overshoot threshold, and the settling time is less than or equal to the preset timeout threshold, the tuning is considered complete.

[0014] Secondly, a dynamic parameter control system for tracking unmanned aerial vehicle (UAV) pods is provided, comprising: The image processing module is used to acquire target images collected by the UAV pod, and also to determine the corresponding target line-of-sight angle based on the target images; the target line-of-sight angle is used to indicate the line-of-sight angle deviation between the current line-of-sight angle and the desired line-of-sight angle. Dynamic parameter controller: Used to calculate the corresponding modulation coefficient based on the target line-of-sight angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor; It is also used to calculate the corresponding desired rotational speed of the pod based on the target line-of-sight angle, the modulation coefficient, and preset basic control parameters, and drive the pod based on the desired rotational speed of the pod, wherein the basic control parameters include the maximum proportional gain and the integral term gain.

[0015] This invention, by adopting the above technical solutions, has significant technical effects: By determining the line-of-sight angle deviation based on the target image and calculating the modulation coefficient in combination with the line-of-sight angle deviation, and then dynamically calculating the desired rotational speed of the pod, this method replaces the traditional fixed control parameter mode. It can adapt to different offset states such as small-range slow movement and large-range rapid offset without manual intervention, solving the problem that the control parameters in the existing technology are difficult to take into account the tracking requirements of different offset degrees, and improving the scenario adaptability and practicality of the control method.

[0016] Based on the dynamic adjustment of the desired rotation speed of the pod by the line-of-sight deviation, when the target deviates from the large field of view, it can output an appropriate rotation speed signal to quickly pull the target back to the center of the field of view without overshoot, avoiding the loss of the target due to screen tremors; when the target deviates from the small field of view, it outputs a smooth rotation speed signal to achieve stable tracking of the target and effectively improve the robustness of the pod tracking control.

[0017] The target line-of-sight angle is clearly defined as the deviation between the current line-of-sight angle and the desired line-of-sight angle. This deviation value is calculated by quantizing the target image, and the subsequent modulation coefficient and desired rotation speed are calculated based on this quantized value. This allows the attitude adjustment of the pod to be based on precise quantized parameters, rather than fuzzy position judgments, effectively improving the tracking and control accuracy of the pod on the target and ensuring that the target remains stably in the center of the field of view or in the preset area. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a dynamic parameter control method for tracking a drone pod according to the present invention. Figure 2 This is a schematic diagram of the dynamic parameter control system structure; Figure 3 This is a schematic diagram of the control error convergence curve. Detailed Implementation

[0020] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] Example 1: A dynamic parameter control method for tracking UAV pods, such as... Figure 1 As shown, it includes the following steps: S100: Acquire the target image captured by the UAV pod, and determine the target line-of-sight angle based on the target image; The target line-of-sight angle is used to indicate the deviation between the current line-of-sight angle and the desired line-of-sight angle. In this embodiment, the target image position of the target image is obtained based on a visual algorithm, and the target viewing angle is calculated. The servo rotation mechanism of the drone pod can be two-axis or three-axis, capable of adjusting the attitude in the horizontal and pitch directions, and can be mounted on a drone platform; the photoelectric sensor used in the pod can be infrared or visible light type, as long as it can stably acquire target images, and this embodiment does not impose detailed limitations on it.

[0022] In this embodiment, the visual algorithm runs on an edge computing device, which can receive images captured by the pod in real time and perform image calculation and processing.

[0023] In this embodiment, the visual algorithm is an image tracking algorithm capable of stably outputting the pixel coordinates of the target image. The target viewing angle is calculated from the target pixel coordinates and the pixel coordinates of the image center point. Specifically: Target line of sight angle Δq t The calculation method is as follows: ; Where f represents the focal length of the photoelectric sensor; Δd t represents the target pixel offset; 'a' represents the conversion factor from radians to angles. In this embodiment, 'a' is taken as an approximate value of 57.3. Wherein, the target pixel coordinates are (u t ,v t The pixel coordinates of the image center point are (c x ,c y ), target pixel offset Δd t The calculation method is as follows: .

[0024] S200. Calculate the corresponding modulation coefficients based on the target viewing angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor; The modulation coefficient is constructed based on the concept of potential energy field in physics. By constructing a generalized potential energy function and taking its derivative, a modulation coefficient containing two adjustment factors is obtained, and the specific functional expression is as follows: ; Where M is the modulation coefficient, and δ is the linear region width adjustment factor; λ is the transition characteristic adjustment factor; Δq t The target line-of-sight angle.

[0025] S300. Based on the target line-of-sight angle, the modulation coefficient, and the preset basic control parameters, calculate the corresponding desired rotational speed of the pod, and drive the pod based on the desired rotational speed of the pod. In this embodiment, the basic control parameters include the maximum proportional gain k. m Integral term gain k i ; k m k is used to determine the maximum response strength for pod tracking. m ≥0; k i Used to eliminate static tracking error, k i ≥0.

[0026] In this embodiment, a dynamic parameter controller is pre-constructed. In the dynamic parameter controller, the modulation coefficient is used to weight the maximum proportional gain to form a proportional term. The input to the dynamic parameter controller is the target angle, and the output is the desired rotational speed of the pod. Specifically: The expression for the dynamic parameter controller, i.e., the desired rotational speed u of the pod. t The calculation method is as follows: ; Among them, u t The desired rotational speed of the pod is represented by M, and the corresponding modulation coefficient is represented by k. m k represents the maximum proportional gain. i This represents the gain of the integral term.

[0027] In this embodiment, the pod is driven based on the desired rotational speed of the pod output by the dynamic parameter controller, thus completing one control iteration. By repeating this process, stable tracking of the target can be achieved.

[0028] The design of the dynamic parameter controller in this embodiment can quickly and without overshoot move the target back to the center of the field of view when the target position shifts significantly, avoiding target loss due to screen tremors; it can also achieve stable tracking of the target when the target position shifts slightly, thus improving the robustness of tracking control. As one possible implementation, it also includes a step of pre-tuning the dynamic parameter controller based on tuning instructions, that is, sequentially adjusting the maximum proportional gain k based on tuning instructions. m Linear region width adjustment factor δ, transient characteristic adjustment factor λ, integral term gain k i ; Those skilled in the art can issue adjustment commands according to actual circumstances, such as during UAV maintenance or takeoff testing before a mission, and adjust the dynamic parameter controller based on the adjustment commands.

[0029] In this embodiment, the dynamic parameter controller is tuned using a step-by-step adjustment and successive optimization approach, sequentially adjusting the maximum proportional gain k. m Linear region width adjustment factor δ, transient characteristic adjustment factor λ, integral term gain k i To ensure optimal controller parameters, the specific steps are as follows: S410, regarding the maximum proportional gain k m Adjustments were made; In this embodiment, the maximum proportional gain k is first determined. m Adjustments are made to ensure the overall system stability. The specific steps are as follows: Initialize the linear region width adjustment factor δ, the transient characteristic adjustment factor λ, and the integral term gain k. i and maximum proportional gain k m A step signal test was used, with the step amplitude set to the maximum line-of-sight deviation (e.g., an angle step from -25° to 0°). Based on the pod's response status, the proportional gain k was gradually increased from the initial value. mc Until the critical oscillation of the step response, based on the proportional gain k corresponding to the critical oscillation of the step response. mc Determine the target maximum proportional gain k m ; To eliminate the influence of other parameters, initialize the linear region width adjustment factor δ, the transient characteristic adjustment factor λ, and the integral term gain k. i ; In this embodiment, the integral term gain k i The initial value of is 0, the initial value of δ is a large value (such as 30°) to temporarily shield its influence, and the initial value of λ is a typical value of the potential energy field (such as λ=2). Those skilled in the art can set it according to actual needs. In this embodiment, k m The initial value is set to 0.1, and the adjustment step size is set to 0.1. Those skilled in the art can set these values ​​according to actual needs. Based on the pod's response status, the proportional gain k is gradually increased from the initial value. mc Until the critical oscillation of the step response, specifically: When the pod reaches the target position and exhibits continuous oscillation (swinging back and forth near the target position), it is determined to be in a critical oscillation state, and the corresponding proportional gain k is obtained. mc For the proportional gain k mc Weighted summation yields the target maximum proportional gain k m In this embodiment, k is selected based on experience. m =0.7∗k mc Until its verification is successful. In practical applications, the maximum proportional gain k is also selected. m Verification is performed; if the condition is not met, the maximum proportional gain k is gradually reduced. m The maximum target gain is output once the verification is successful; the verification condition is, for example, that the step response is oscillatory and Δq is satisfied. t It decreased to more than 50% of the initial deviation within 0.5 seconds, with no obvious lag.

[0030] S420, Based on the target maximum proportional gain k m The linear region width adjustment factor δ is adjusted to divide the linear region and the saturation region; The specific steps are as follows: S421. Determine the initial value of the linear zone width adjustment factor δ based on the pod's field of view. δ is the width of the linear region. The initial value is taken as a reasonable multiple of the pod's field of view, and then gradually adjusted. Those skilled in the art can set the initial value of δ and the adjustment step size according to actual needs. As an example, the initial value of δ is 0.5 times the pod's field of view, and the adjustment step size is 2. S422, Based on the initialization of the transition characteristic adjustment factor λ and integral term gain k i and the target maximum proportional gain k m The step response test is performed using large line-of-sight deviation (e.g., angle stepping from -25° to 0°) and small line-of-sight deviation (e.g., angle stepping from -1° to 0°). The initial value of the linear region width adjustment factor δ is adjusted until the preset first large deviation response index and first small deviation response index are met. The δ value at this time is then output as the target linear region width adjustment factor.

[0031] That is, during the test, λ is kept at a typical value (e.g., λ=2), and k... m The maximum proportional gain obtained by tuning in step S410, k i =0 remains unchanged; Specifically: In the large deviation step test, if the response overshoot exceeds the preset first overshoot threshold (5%) or oscillation occurs, increase δ; if the rise time exceeds the preset rise time threshold (0.4s), the response is judged to be slow and decrease δ. In the small deviation step test, if the adjustment time exceeds the preset first adjustment time threshold (0.5s), the response is judged to be sluggish and δ is increased; if the amplitude exceeds the preset first amplitude threshold (>0.2°), it is judged to be a continuous micro-tremor and δ is decreased. The primary deviation response indicator is: In the large deviation response, the rise time is less than or equal to the preset rise time threshold (0.4s), the response overshoot is less than or equal to the preset first overshoot threshold (5%), and there is no oscillation; The first small deviation response index is: In the small deviation response, the adjustment time is less than or equal to the preset first adjustment time threshold (0.5s), and in steady state, the fluctuation amplitude of the control quantity is less than or equal to the preset first fluctuation threshold (0.2° / s).

[0032] S430, Based on the target maximum proportional gain k m The transition characteristic adjustment factor λ is adjusted along with the target linear region width adjustment factor δ to optimize the transition characteristics. The specific steps are as follows: Based on the initial transient characteristic adjustment factor λ and integral term gain k i and the target maximum proportional gain k mThe target linear region width adjustment factor δ is subjected to step response tests using large line-of-sight deviation (e.g., angle stepping from -25° to 0°) and small line-of-sight deviation (e.g., angle stepping from -1° to 0°). The transition characteristic adjustment factor λ is adjusted until the response convergence process is smooth, the pod screen has no visible jerks, and the corresponding second largest deviation response index and second smallest deviation response index are met. The λ at this time is output as the transition characteristic adjustment factor.

[0033] λ is used to control the steepness of the transition of the modulation coefficient from the linear region to the saturation region. In this embodiment, the initial value of λ is 2, and the subsequent step size is adjusted to 0.2 according to the response characteristics. k is maintained throughout the testing process. m δ is a set value, and k is a set value. i =0 remains unchanged; Specifically: In the large deviation step test, if a visible inflection point appears in the response convergence process, such as a change in angular velocity causing screen jerking, then λ is reduced to achieve a smooth transition; if the response is slow or oscillating, then λ is increased. The response is considered slow if the rise time exceeds the preset rise time threshold (0.4s).

[0034] In the small deviation step test, if the amplitude exceeds the preset second amplitude threshold (>0.1°), it is determined that a slight oscillation has occurred, and λ is decreased; if the adjustment time exceeds the preset second adjustment time threshold (0.3s), it is determined that the response is sluggish, and λ is increased. In this embodiment, the second amplitude threshold is less than the first amplitude threshold; the second adjustment time threshold is less than the first adjustment time threshold.

[0035] The second largest deviation response index is: In the large deviation response, the rise time is less than or equal to the preset rise time threshold (0.4s), the response overshoot is less than or equal to the preset first overshoot threshold (5%), and there is no oscillation; The second small deviation response index is: In the small deviation response, the adjustment time is less than or equal to the preset second adjustment time threshold (0.3s), and in steady state, the fluctuation amplitude of the control quantity is less than or equal to the preset second fluctuation threshold (0.1° / s).

[0036] S440, Based on the target maximum proportional gain k m The target linear region width adjustment factor δ and the target transient characteristic adjustment factor λ are used to adjust the integral term gain ki to eliminate static error. The specific steps are as follows: Based on the target maximum proportional gain k m 1. Adjust the target linear region width δ and the target transition characteristic λ. 2. Perform a step signal test (same as step S410). During the step signal test, set the integral term gain k. iStarting from the minimum value and gradually increasing, based on the pod response state, when the static deviation is less than or equal to the preset deviation threshold (0.1°), the step response overshoot is less than or equal to the preset second overshoot threshold (3%), and the settling time is less than or equal to the preset timeout threshold (1.5s), the resulting integral term gain k is... i As the gain of the target integral term.

[0037] In this embodiment, k i The initial value is 0.01, and the step size is adjusted by 0.01 based on the response characteristics.

[0038] The target maximum proportional gain k is tuned based on the above step S400. m Target linear region width adjustment factor δ, target transient characteristic adjustment factor λ, target integral term gain k i Construct a dynamic parameter controller and deploy it to an edge computing device, such as... Figure 2 As shown, the dynamic parameter controller is the core component. The input of the dynamic parameter controller is the target line-of-sight angle obtained by the image tracking algorithm, and the output is the desired rotation speed of the pod. Based on the desired rotation speed of the pod, the pod servo mechanism is driven to rotate, completing one control iteration. By repeatedly executing the above control process, continuous and stable tracking of the target is achieved. In this embodiment, the dynamic parameter controller is deployed on an edge computing device, connected to the pod video stream, and its tracking performance is tested. The tracking response speed, overshoot, and steady-state error are analyzed using curve analysis. The specific steps are as follows: The corresponding dynamic parameter controller is deployed to the edge computing device. This device needs to establish a communication connection with the UAV pod, be able to receive the video stream data output by the pod in real time, and send the pod's desired rotation speed generated by the dynamic parameter controller to the pod servo mechanism. The tracking performance test uses step signals of different amplitudes. During the test, the line-of-sight angle data and corresponding timestamps are recorded in real time, and the control error convergence curve is plotted as follows: Figure 3 As shown, curve analysis is used to track indicators such as response speed, overshoot, and steady-state error in order to adjust each coefficient based on each indicator.

[0039] This method acquires the target image position in real time and calculates the line-of-sight deviation using a visual algorithm. Based on the concept of potential energy field in physics, it constructs a modulation coefficient containing two adjustment factors. This modulation coefficient is introduced into the proportional term of the controller to construct a dynamic parameter controller. The optimal parameters of the controller are determined through a scientific parameter tuning process. The controller is deployed to an edge computing device and connected to the pod video stream to achieve dynamic tracking control of the target. It can dynamically adjust the control parameters according to the magnitude of the target's line-of-sight deviation. When the target position experiences a large field-of-sight shift, it can quickly and without overshoot move the target back to the center of the field of view, avoiding target loss due to image tremors. Even when the target position experiences a small field-of-sight shift, it can still achieve stable tracking of the target, improving the robustness of the tracking control.

[0040] Example 2: A dynamic parameter control system for tracking a drone pod, comprising: The image processing module is used to acquire target images collected by the UAV pod, and also to determine the corresponding target line-of-sight angle based on the target images; the target line-of-sight angle is used to indicate the line-of-sight angle deviation between the current line-of-sight angle and the desired line-of-sight angle. Dynamic parameter controller: It is used to calculate the corresponding modulation coefficient based on the target line-of-sight angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor; it is also used to calculate the corresponding desired pod rotation speed based on the target line-of-sight angle, the modulation coefficient, and the preset basic control parameters, and drive the pod based on the desired pod rotation speed, wherein the basic control parameters include the maximum proportional gain and the integral term gain.

[0041] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0043] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] This invention is described with reference to flowchart illustrations and / or block diagrams of the method, terminal device (system), and computer program product according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0045] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] It should be noted that: The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0048] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0049] Furthermore, it should be noted that the shapes and names of the parts and components described in the specific embodiments described in this specification may differ. All equivalent or simple variations made to the structure, features, and principles described in this patent concept are included within the protection scope of this patent. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to replace them, as long as they do not depart from the structure of this invention or exceed the scope defined in these claims, they should all fall within the protection scope of this invention.

Claims

1. A dynamic parameter control method for tracking unmanned aerial vehicle (UAV) pods, characterized in that, Includes the following steps: The target image is acquired by the UAV pod, and the corresponding target line-of-sight angle is determined based on the target image; the target line-of-sight angle is used to indicate the line-of-sight angle deviation between the current line-of-sight angle and the desired line-of-sight angle. The corresponding modulation coefficients are calculated based on the target line-of-sight angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor. Based on the target line-of-sight angle, the modulation coefficient, and the preset basic control parameters, the corresponding desired rotational speed of the pod is calculated, and the pod is driven based on the desired rotational speed of the pod. The basic control parameters include the maximum proportional gain and the integral term gain.

2. The dynamic parameter control method for tracking a UAV pod according to claim 1, characterized in that, The formula for calculating the modulation coefficient is: ； Where M is the modulation coefficient, and δ is the linear region width adjustment factor; λ is the transition characteristic adjustment factor; Δq t The target line-of-sight angle.

3. The dynamic parameter control method for tracking a UAV pod according to claim 2, characterized in that, The formula for calculating the desired rotational speed of the pod is: ； Among them, u t The desired rotational speed of the pod is represented by M, and the corresponding modulation coefficient is represented by k. m k represents the maximum proportional gain. i This represents the gain of the integral term.

4. A dynamic parameter control method for tracking a UAV pod according to any one of claims 1 to 3, characterized in that, The formula for calculating the target line-of-sight angle is: Target line-of-sight angle Δq t The calculation method is as follows: ； Where f represents the focal length of the photoelectric sensor; Δd t represents the target pixel offset; 'a' represents the conversion factor from radians to degrees. Wherein, the target pixel coordinates are (u t ,v t The pixel coordinates of the image center point are (c x ,c y ), target pixel offset Δd t The calculation method is as follows: 。 5. The dynamic parameter control method for tracking a UAV pod according to claim 1, characterized in that, It also includes the steps of sequentially adjusting the maximum proportional gain, the linear region width adjustment factor, the transient characteristic adjustment factor, and the integral term gain based on the tuning instructions.

6. A dynamic parameter control method for tracking a UAV pod according to claim 5, characterized in that, The steps for tuning the maximum proportional gain are as follows: Initialize the linear region width adjustment factor, transient characteristic adjustment factor, integral term gain, and maximum proportional gain. Use a step signal for testing, with the step amplitude set to the maximum line-of-sight deviation. Based on the pod's response state, gradually increase the proportional gain from the initial value until the step response reaches critical oscillation. Determine the target maximum proportional gain k based on the proportional gain corresponding to the critical oscillation of the step response. m .

7. A dynamic parameter control method for tracking a UAV pod according to claim 5, characterized in that, The steps for tuning the linear region width adjustment factor are as follows: The initial value of the linear zone width adjustment factor is determined based on the pod's field of view. Based on the initial transient characteristic adjustment factor and integral term gain, and the maximum proportional gain after tuning, step response tests were conducted using large line-of-sight deviation and small line-of-sight deviation respectively. In the large deviation step test, if the response overshoot exceeds the preset first overshoot threshold or oscillation occurs, the linear region width adjustment factor is increased. If the rise time exceeds the preset rise time threshold, the response is judged to be slow, and the linear region width adjustment factor is decreased. This continues until the rise time in the large deviation response is less than or equal to the preset rise time threshold, the response overshoot is less than or equal to the preset first overshoot threshold, and there is no oscillation. In the small deviation step test, if the adjustment time exceeds the preset first adjustment time threshold, the response is judged to be sluggish, and the linear region width adjustment factor is increased; if the amplitude exceeds the preset first amplitude threshold, the continuous micro-oscillation is judged to occur, and the linear region width adjustment factor is decreased; until the adjustment time in the small deviation response is less than or equal to the preset first adjustment time threshold, and the control quantity fluctuation amplitude in steady state is less than or equal to the preset first fluctuation threshold.

8. A dynamic parameter control method for tracking a UAV pod according to claim 5, characterized in that, The steps for tuning the transition characteristic adjustment factor are as follows: Based on the initial transient characteristic adjustment factor and integral term gain, as well as the tuned maximum proportional gain and linear region width adjustment factor, step response tests were conducted using large line-of-sight deviation and small line-of-sight deviation, respectively. In the large deviation step test, if the screen stutters due to changes in angular velocity, the transition characteristic adjustment factor is reduced to achieve a smooth transition; if the response is slow or oscillating, the transition characteristic adjustment factor is increased, where the rise time exceeds the preset rise time threshold to determine a slow response; until the response convergence process is smooth, the pod screen has no visible stutters or oscillations, and the rise time in the large deviation response is less than or equal to the preset rise time threshold. In the small deviation step test, if the amplitude exceeds the preset second amplitude threshold, it is determined that a slight oscillation has occurred, and the transition characteristic adjustment factor is reduced. If the adjustment time exceeds the preset second adjustment time threshold, it is determined that the response is sluggish, and the transition characteristic adjustment factor is increased. This continues until the adjustment time in the small deviation response is less than or equal to the preset second adjustment time threshold, and the amplitude of the control quantity fluctuation in steady state is less than or equal to the preset second fluctuation threshold.

9. A dynamic parameter control method for tracking a UAV pod according to claim 5, characterized in that, The steps for tuning the integral term gain are as follows: Based on the calibrated maximum proportional gain, linear region width adjustment factor, and transition characteristic adjustment factor, a step signal test is performed; during the step signal test, the integral term gain k is set... i Starting from the minimum value, the value is gradually increased. Based on the pod response state, when the static deviation is less than or equal to the preset deviation threshold, the step response overshoot is less than or equal to the preset second overshoot threshold, and the settling time is less than or equal to the preset timeout threshold, the tuning is considered complete.

10. A dynamic parameter control system for tracking unmanned aerial vehicle (UAV) pods, characterized in that, include: The image processing module is used to acquire target images collected by the UAV pod, and also to determine the corresponding target line-of-sight angle based on the target images; the target line-of-sight angle is used to indicate the line-of-sight angle deviation between the current line-of-sight angle and the desired line-of-sight angle. Dynamic parameter controller: Used to calculate the corresponding modulation coefficient based on the target line-of-sight angle, as well as the preset linear region width adjustment factor and transition characteristic adjustment factor; It is also used to calculate the corresponding desired rotational speed of the pod based on the target line-of-sight angle, the modulation coefficient, and preset basic control parameters, and drive the pod based on the desired rotational speed of the pod, wherein the basic control parameters include the maximum proportional gain and the integral term gain.

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