Target flight attitude control method under severe weather conditions

Abstract

This invention relates to the field of target flight attitude control technology, specifically to a target flight attitude control method under adverse weather conditions. The method involves acquiring the target's flight, weather, position, and multi-dimensional attitude parameters during the current flight period. First, based on the deviation between the flight parameters during the current flight period and preset values, an initial attitude compensation amount is calculated to immediately correct any existing trajectory deviations. Then, by matching historical wind field windows with meteorological changes during the current flight period, the weather forecast parameters for the next stage are predicted. Based on comprehensive risk factors, a feedforward attitude compensation amount is generated to anticipate upcoming wind disturbances. Finally, the two types of compensation amounts are fused, and within a preset attitude safety boundary, an optimization algorithm is used to continuously optimize and output the desired attitude parameters. This method solves the response lag problem of feedback control and avoids attitude instability caused by overshoot of control quantities under extreme weather conditions, significantly improving target flight stability and trajectory simulation accuracy under adverse weather conditions.

Description

Technical Field

[0001] This invention relates to the field of target flight attitude control technology, and specifically to a target flight attitude control method under adverse weather conditions. Background Technology

[0002] When performing flight missions, targets (such as unmanned aerial vehicles and flight test targets) typically need to fly strictly according to preset routes and attitudes to simulate the flight trajectory of a specific target with high fidelity. However, in actual flight, especially when facing severe weather conditions such as strong winds, gusts, strong crosswinds, and turbulence, wind field disturbances in the flight environment are highly sudden, phased, and nonlinear, which can easily cause significant deviations between the actual flight path and flight attitude of the target and the expected state.

[0003] Existing target attitude control methods mostly employ closed-loop feedback control systems based on trajectory deviation and attitude error (such as traditional PID control or cascade feedback control). These control methods are essentially reactive compensation control; that is, the control system only calculates the control input and performs reverse correction after the target has already exhibited significant trajectory deviation or attitude error. However, under adverse weather conditions, wind disturbances change drastically and frequently, and relying solely on current error feedback adjustment often fails to respond promptly, easily leading to problems such as compensation lag, frequent oscillations of actuators (such as control surfaces), and control input overshoot. Summary of the Invention

[0004] To address the technical problems of insufficient timely response when relying solely on current error feedback adjustments under severe and frequent wind disturbances in adverse weather conditions, which often leads to compensation lag, frequent oscillations of actuators (such as control surfaces), and control overshoot, this invention aims to provide a target flight attitude control method under adverse weather conditions. The specific technical solution adopted is as follows: Acquire the target's flight parameters, weather parameters, and various attitude parameters during the current flight period; During the current flight period, the deviation between the target's flight parameters and the preset flight parameters is analyzed, and the initial attitude compensation amount for each attitude parameter is calculated. The changes in weather parameters of the target during the current flight period are matched with the changes in weather parameters within a preset historical window. Weather parameter samples are then extracted from the preset historical window as weather prediction parameters for the target in the next flight period. Based on the numerical characteristics and changes of the weather prediction parameters, a comprehensive risk factor for the target in the next flight period is calculated. Based on the comprehensive risk factor, and in combination with the target's preset flight parameters and weather prediction parameters, the feedforward attitude compensation amount for each attitude parameter is calculated. The initial attitude compensation amount is fused with the feedforward attitude compensation amount, and an optimization algorithm is used to perform rolling optimization within the preset attitude safety limit, thereby outputting the target's expected flight attitude parameters at the next moment and performing attitude control.

[0005] Furthermore, the flight parameters include flight speed, spatial coordinates, and heading angle, wherein the spatial coordinates include flight altitude, lateral coordinates, and longitudinal coordinates, the attitude parameters include pitch angle, roll angle, and yaw angle, and the weather parameters include temperature, wind speed, and wind direction.

[0006] Furthermore, the method for obtaining the initial attitude compensation amount includes: During the current flight period, the target's flight altitude, lateral coordinate, and heading angle at the current moment are subtracted from the actual values ​​of the flight altitude, lateral coordinate, and heading angle, and the resulting difference is normalized to obtain the corresponding trajectory deviation value. Multiply the trajectory deviation value corresponding to the flight altitude by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the pitch angle. Multiply the trajectory deviation value corresponding to the lateral coordinate by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the roll angle. Multiply the trajectory deviation value corresponding to the heading angle by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the yaw angle.

[0007] Furthermore, the method for obtaining the weather forecast parameters includes: Use the current flight time period as the current weather time window, and obtain the wind speed curve, wind direction curve, and temperature curve within the current weather time window; The wind direction curve in the current weather time window and the wind direction curve in the weather parameters in the preset historical window are decomposed by sine and cosine respectively to obtain two wind direction vector component curves corresponding to the current weather time window and the preset historical window respectively. The wind direction vector component, wind speed, and temperature are used as target parameters; Using a sliding window and a preset step size, a historical parameter window with the same length as the current weather time window is extracted from the weather parameters within the preset historical window. Analyze the similarity features between each historical parameter window and the target parameter in the current weather time window to obtain the matching index between each historical parameter window and the current weather time window; The historical parameter window corresponding to the maximum matching index is used as a reference sample window, and various weather parameters within a preset time period after the reference sample window are extracted from the preset historical window as the weather prediction parameters for the target in the next flight period.

[0008] Furthermore, the method for obtaining the matching indicators includes: Calculate the DTW value between each historical parameter window and the curve of each target parameter in the current weather time window. Perform negative correlation mapping and normalization on the obtained DTW values ​​to obtain the similarity factor. Use the preset weighting factor corresponding to the target parameter to weight and fuse the similarity factors corresponding to all types of target parameters to obtain the matching index between each historical parameter window and the current weather time window.

[0009] Furthermore, the method for obtaining the comprehensive risk factor includes: Obtain the predicted wind speed and predicted wind direction curves from the weather forecast parameters for the target in the next flight period; The average wind speed is obtained by averaging the wind speed values ​​in the predicted wind speed curve. The wind direction angle values ​​in the predicted wind direction curve are converted into sine and cosine values ​​respectively, thus obtaining a sine sequence and a cosine sequence. The mean of all sine values ​​and the mean of all cosine values ​​are calculated in the sine and cosine sequences respectively. The mean of the obtained sine values ​​is used as the vertical axis and the mean of the cosine values ​​is used as the horizontal axis to construct a direction vector. The angle between the direction vector and the preset reference direction is calculated as the average wind direction. Calculate the absolute value of the sine of the angle between the mean expected heading angle of the target in the next flight period and the mean wind direction, multiply it by the mean wind speed, and then normalize it to obtain the crosswind risk factor. The variance of all sine values ​​in the sine sequence is calculated as the first variance, and the variance of all cosine values ​​in the cosine sequence is calculated as the second variance. The sum of the first and second variances is normalized and used as the wind direction change risk factor. The crosswind risk factor and the wind direction change risk factor are weighted and fused using the preset weights corresponding to each attitude parameter, and the weighted result is limited using a saturation function to obtain the comprehensive risk factor for each attitude parameter.

[0010] Furthermore, the method for obtaining the feedforward attitude compensation amount includes: The comprehensive risk factor for each attitude parameter is multiplied by the corresponding preset feedforward compensation gain coefficient to obtain the absolute value of the feedforward compensation for each attitude parameter. Calculate the angle between the average wind direction and the average expected heading angle of the target in the next flight period. Use the opposite sign of the cosine of the obtained angle as the feedforward compensation direction value of the pitch angle, and use the sign of the sine of the obtained angle as the feedforward compensation direction value of the roll angle and yaw angle. The product of the feedforward compensation direction value and the absolute value of the feedforward compensation for each attitude parameter is used as the feedforward attitude compensation amount for each attitude parameter.

[0011] Furthermore, the method for obtaining the desired flight attitude parameters includes: The initial attitude compensation values ​​of all types of attitude parameters are combined into an initial attitude compensation vector, the feedforward attitude compensation values ​​of all types of attitude parameters are combined into a feedforward attitude compensation vector, and the attitude parameters of the target at the current flight moment are combined into a real-time attitude vector. The real-time attitude vector, the initial attitude compensation vector, and the feedforward attitude compensation vector are vector summed, and the summation result is used as the optimization center attitude of the beetle whisker optimization algorithm in the first iteration process. In each iteration, a random unit vector in three-dimensional space is taken as the unit search direction vector, the product of the preset beard length and the unit search direction vector is taken as the adjustment vector, the sum of the optimization center pose and the adjustment vector is taken as the right beard candidate pose, and the difference between the optimization center pose and the adjustment vector is taken as the left beard candidate pose. The predicted spatial coordinates of the target after a preset standard time step are derived under the attitudes corresponding to the left and right candidate attitudes, respectively. The Euclidean distance between the predicted spatial coordinates and expected spatial coordinates of the left and right candidate poses is calculated separately and used as the left and right cost values. The difference between the left and right cost values ​​is processed using a sign function to obtain a decision sign value. The product of the decision sign value, the preset iterative pose step size, and the unit search direction vector is used as the pose adjustment amount. The sum of the pose adjustment amount and the optimization center pose in each iteration is used as the optimization center pose for the next iteration. The preset pose safety limit corresponding to each pose parameter is used to limit the optimization center pose for the next iteration. The preset iterative pose step size is multiplied by the preset step size decay rate to obtain the preset iterative pose step size for the next iteration. When the preset iteration termination condition is met, the iteration optimization stops, and the expected pitch angle, expected roll angle and expected yaw angle of the target at the next moment are obtained from the vector corresponding to the final optimization center attitude.

[0012] Furthermore, the method for obtaining the predicted spatial coordinates includes: Based on the pitch, roll and yaw angles in the left and right candidate attitudes, the target's flight speed during the current flight period is orthogonally decomposed in the three-dimensional coordinate system to obtain the three-dimensional velocity components of the left and right whiskers. Multiply the left and right three-dimensional velocity components by a preset standard time step to obtain the left and right three-dimensional displacement increments. Add the target's spatial coordinates in the current flight period to the left and right three-dimensional displacement increments to obtain the predicted spatial coordinates of the target in the left and right candidate attitudes, respectively.

[0013] Furthermore, the attitude control includes: The aerodynamic control surface deflection command is calculated using the airborne flight control computing equipment on the target, based on the desired pitch angle, desired roll angle, and desired yaw angle at the next moment. According to the aerodynamic control surface deflection command, the actuator of the target is driven to perform an action, wherein the actuator includes at least one of a tail fin, a stabilizing fin, and a lower aerodynamic control fin.

[0014] The present invention has the following beneficial effects: First, the target's flight parameters, weather parameters, and attitude parameters for the current flight period are acquired. In actual flight, the target is often subjected to complex aerodynamic factors, causing deviations between flight parameters and preset values. Therefore, analyzing the deviation between current flight parameters and preset parameters to calculate the initial attitude compensation can promptly correct any deviations in the flight trajectory. Furthermore, during actual flight, under severe weather conditions such as strong winds, gusts, crosswinds, vertical winds, or turbulence, the target's actual trajectory is highly prone to deviating from the expected trajectory. Therefore, by matching the weather changes during the current flight period with preset historical windows and extracting weather forecast parameters, the data scarcity and response lag inherent in relying on real-time sensing are overcome. This enables accurate advance prediction of short-term severe weather trends in local flight areas. Simultaneously, by calculating a comprehensive risk factor based on the numerical characteristics of weather forecast parameters and combining it with the target's preset flight parameters to calculate the feedforward attitude compensation, pre-adjustments for impending wind disturbances can be made, fundamentally avoiding control lag problems when wind disturbances change drastically. Finally, the initial attitude compensation amount and the feedforward attitude compensation amount are fused together, and the rolling optimization output is performed within the preset attitude safety limit using an optimization algorithm. This takes into account both the elimination of current errors and the resistance to future wind disturbances, and avoids mechanical stall or attitude instability caused by control overshoot under extreme weather conditions. Ultimately, this significantly improves the stability of target flight and the accuracy of trajectory simulation under adverse weather conditions. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for controlling the flight attitude of a target under adverse weather conditions, as provided in an embodiment of the present invention. Detailed Implementation

[0016] The following description, in conjunction with the accompanying drawings, details a specific scheme for a target flight attitude control method under adverse weather conditions provided by the present invention.

[0017] Please see Figure 1 The diagram illustrates a method flowchart for target flight attitude control under adverse weather conditions, according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the target's flight parameters, weather parameters, and various attitude parameters during the current flight period.

[0018] First, based on the specific mission objective currently being simulated by the target, the selected flight mode for the target is determined. During the target's flight according to the preset flight mode, various data are acquired in real time using the airborne sensors on the target: using the airborne navigation and positioning system (such as GPS / BeiDou positioning module) and inertial measurement unit (IMU), the lateral coordinates, flight altitude, and longitudinal coordinates of the target at each moment during the current flight period are acquired as spatial coordinates, as well as the flight speed and heading angle of the target at each moment during the current flight period, which, together with the spatial coordinates, are collectively referred to as the target's flight parameters; at the same time, the pitch angle, roll angle, and yaw angle of the target at each moment during the current flight period are acquired as attitude parameters; and the wind speed, wind direction, and temperature of the environment in which the target is located at each moment during the current flight period are also acquired as weather parameters.

[0019] In addition, it is necessary to extract the weather parameters, flight parameters and attitude parameters of the target flight process environment within the preset historical window from the historical database. In this embodiment of the invention, the preset historical window can be set to 24 hours before the target flight, and the current flight time period is set to 10 minutes back from the current time. The specific time period can be adjusted according to the implementation scenario. In this embodiment of the invention, the sampling time interval can be set to 1 second. The specific interval can be adjusted according to the implementation scenario and is not limited here.

[0020] Step S2: During the current flight period, analyze the deviation between the target's flight parameters and the preset flight parameters, and calculate the initial attitude compensation amount for each attitude parameter.

[0021] In real flight environments, due to limitations imposed by the target's own machining errors, aerodynamic parameter perturbations, and residual wind disturbances that the prediction model cannot fully cover, the target's actual flight trajectory will inevitably deviate from the expected trajectory. Therefore, it is necessary to first analyze the existing flight deviations and calculate the compensation amount required for direct correction. In the underlying physical logic of flight control, different positional deviations are corrected by different attitudes. For example, lateral coordinate deviations are mainly corrected by bank angle (roll angle); flight altitude deviations are mainly corrected by pitch angle (nose-up / nose-down); and heading deviations are mainly corrected by yaw angle. Therefore, in this step, the deviations between the target's flight parameters and preset flight parameters can be analyzed during the current flight period, and these deviations can be mapped and calculated as the initial attitude compensation amount for each attitude parameter.

[0022] Preferably, in one embodiment of the present invention, the method for obtaining the initial attitude compensation amount includes: During the current flight period, the preset values ​​(preset flight parameters) for the target's current flight altitude, lateral coordinate, and heading angle are subtracted from their corresponding actual values. The resulting differences are then normalized to obtain the corresponding trajectory deviation value. The trajectory deviation value characterizes the degree of deviation of each of the aforementioned flight parameters from the predetermined flight path at the current moment. A larger value indicates a more severe deviation of the target due to wind disturbance. This normalization can employ truncated proportional normalization, i.e. ,in, Indicates the independent variable. Indicates the normalization threshold. This represents the function that takes the maximum value. This represents the function that takes the minimum value. The dependent variable is represented by the normalized threshold for flight altitude (set to 100 meters), the normalized threshold for lateral coordinate (set to 150 meters), and the normalized threshold for heading angle (set to 15 degrees).

[0023] To correct altitude deviation, the most direct aerodynamic response for the target is to change the pitch angle. Therefore, the trajectory deviation value corresponding to the flight altitude is multiplied by the corresponding preset compensation gain coefficient, and the product is used as the initial attitude compensation amount for the pitch angle. To correct lateral deviation from the predetermined flight path, the aircraft needs to roll, i.e., change the roll angle. Therefore, the trajectory deviation value corresponding to the lateral coordinate is multiplied by the corresponding preset compensation gain coefficient, and the product is used as the initial attitude compensation amount for the roll angle. When the target's nose is not pointing towards the predetermined flight path, the yaw angle needs to be changed. Therefore, the trajectory deviation value corresponding to the heading angle is multiplied by the corresponding preset compensation gain coefficient, and the product is used as the initial attitude compensation amount for the yaw angle. The larger the initial attitude compensation amount for each attitude parameter obtained in the above steps, the more drastic the adjustment required.

[0024] It should be noted that, in this embodiment of the invention, the preset values ​​corresponding to the flight altitude, lateral coordinate, and heading angle can be extracted based on the preset three-dimensional flight trajectory of the target; the preset compensation gain coefficients corresponding to the pitch angle, roll angle, and yaw angle can be set to 6 degrees, 12 degrees, and 8 degrees, respectively. The specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0025] Step S3: Match the changes in weather parameters of the target during the current flight period with the changes in weather parameters within a preset historical window, thereby extracting weather parameter samples from the preset historical window as the weather prediction parameters for the target in the next flight period; calculate the comprehensive risk factor of the target in the next flight period based on the numerical characteristics and changes of the weather prediction parameters; calculate the feedforward attitude compensation amount for each attitude parameter based on the comprehensive risk factor and in combination with the target's preset flight parameters and weather prediction parameters.

[0026] In real flight environments, constrained by geographical topology and thermodynamic cycles, local wind fields (such as sea breezes, mountain breezes, and turbulent vortices) often exhibit significant diurnal similarities (i.e., the wind field evolution pattern at this time yesterday is highly likely to be repeated today) and extremely strong short-term physical continuity. Therefore, the fluctuation trajectory of weather parameters such as wind direction, wind speed, and temperature encountered by the target during the current flight period is essentially a local slice of a macroscopic meteorological evolution process. Thus, in this invention, this physical law can be utilized to match the changes in weather parameters of the target during the current flight period with the changes in weather parameters within a preset historical window, thereby extracting weather parameter samples from the preset historical window as weather prediction parameters for the target in the next flight period.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining weather forecast parameters includes: Use the current flight time period as the current weather time window and obtain the wind speed curve, wind direction curve, and temperature curve within the current weather time window.

[0028] In a geographic coordinate system, wind direction is usually represented in degrees (0 degrees to 360 degrees). To avoid situations where the wind direction curves are physically close but have large final difference values, in this embodiment of the invention, the wind direction curves in the current weather time window and the wind direction curves in the weather parameters of the preset historical window are decomposed into sine and cosine values, respectively. The one-dimensional angle sequence is then projected into a two-dimensional Cartesian coordinate system to obtain two wind direction vector component curves corresponding to the current weather time window and the preset historical window.

[0029] Using wind direction vector components, wind speed, and temperature as target parameters, a historical parameter window with the same length as the current weather time window is extracted from the weather parameters within the preset historical window using a sliding window and a preset step size.

[0030] Next, the similarity characteristics between each historical parameter window and the target parameters within the current weather time window are analyzed to obtain a matching index between each historical parameter window and the current weather time window. The DTW value between the curves of each historical parameter window and each target parameter within the current weather time window is calculated. The smaller the DTW value, the higher the similarity between the two. Therefore, the obtained DTW values ​​are subjected to negative correlation mapping and normalization to correct the logical relationship and obtain a similarity factor. Then, using the preset weighting factor corresponding to the target parameter, the similarity factors corresponding to all types of target parameter curves are weighted and fused. The preset weighting factor corresponding to each type of target parameter is multiplied by the similarity factor corresponding to each type of target parameter. The sum of the products corresponding to all types of target parameters is used as the matching index between each historical parameter window and the current weather time window. The larger the matching index, the higher the similarity between the weather parameter change characteristics in the historical parameter window and the current weather time window. The negative correlation mapping and normalization processing here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, x represent the independent variable, and c represent the preset scaling reference value. The preset scaling reference value can reasonably scale the DTW value corresponding to each weather parameter to avoid the situation where the exponential function output is extremely unbalanced. In this embodiment of the invention, the preset scaling reference value of wind speed can be set to 50, the preset scaling reference value of temperature can be set to 10, and the preset scaling reference value of wind direction vector component can be set to 5. The specific values ​​can be obtained by calibrating the average value of historical samples.

[0031] Finally, the historical parameter window corresponding to the maximum matching index is used as a reference sample window, and various weather parameters within a preset time period after the reference sample window are extracted from the preset historical window as the weather prediction parameters for the target in the next flight period.

[0032] It should be noted that the calculation process of the DTW value is a well-known technique, and the specific process will not be elaborated here; the preset step size can be set to 10 seconds; since wind speed and wind direction changes have the most direct and severe aerodynamic disturbances to the target attitude, the preset weighting factors for wind speed, the two wind direction vector components, and temperature in the weather parameters can be set to 0.5, 0.2, 0.2, and 0.1, respectively. The specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0033] Thus, weather forecast parameters for the target in the next flight period can be obtained based on historical weather parameters. However, given that the dimensions of weather forecast parameters are different, and that numerical characteristics usually correspond to steady-state wind deflection while changes correspond to dynamic turbulence, the two have different disturbance mechanisms on the target. Therefore, we can analyze the numerical characteristics and changes of weather forecast parameters and combine them to obtain a comprehensive risk factor for the target in the next flight period, which is used to characterize the overall disturbance threat level of weather parameters to the target's flight process in the next flight period.

[0034] Preferably, in one embodiment of the present invention, the method for obtaining the comprehensive risk factor includes: In this embodiment of the invention, the types of weather parameters include temperature, wind speed, and wind direction. Since wind speed determines the magnitude of the disturbance and wind direction determines the direction of the disturbance, these two are the basic parameters for constructing the disturbance situation of the target. Therefore, the comprehensive risk factor is calculated mainly based on the numerical characteristics and changes of wind speed and wind direction. Thus, the predicted wind speed curve and predicted wind direction curve of the target in the weather prediction parameters in the next flight period are obtained.

[0035] The average wind speed is obtained by directly averaging the wind speed values ​​in the predicted wind speed curve. The larger the average wind speed, the stronger the absolute kinetic energy of the wind field that the target may experience in the next flight period, and the more significant the disturbance will be.

[0036] Since wind direction is typically represented in degrees (0°~360°) in a geographic coordinate system, exhibiting a periodic characteristic with consecutive beginnings and endings, to avoid mathematical errors from direct calculation, this embodiment of the invention converts each wind direction angle value in the predicted wind direction curve into sine and cosine values ​​based on sine-cosine orthogonal vector decomposition, thereby obtaining sine and cosine sequences. Then, the mean of all sine and cosine values ​​is calculated in both sequences, and the mean of the sine values ​​is used as the ordinate, while the mean of the cosine values ​​is used as the abscissa to construct a direction vector. The angle between this direction vector and a preset reference direction is calculated as the average wind direction. The average wind direction can be used to characterize the geographic orientation of the dominant wind direction experienced by the target in the next flight period. It should be noted that in this embodiment of the invention, the preset reference direction is set to geographic true north.

[0037] Since the wind speed component perpendicular to the flight path is the direct factor causing target yaw, the absolute value of the sine of the angle between the average wind direction and the average expected flight path angle for the next flight period is calculated. This absolute sine value is used to extract the vertical component and unify the destructive weights of left and right crosswinds. Then, the absolute value of the obtained sine value is multiplied by the average wind speed and normalized to obtain the crosswind risk factor. The larger the crosswind risk factor, the more perpendicular the wind direction is to the flight path and the higher the wind speed, indicating a higher risk of the target being blown off its intended flight path. Normalization can be performed using maximum and minimum values. The maximum and minimum values ​​can be calculated and obtained from a database within a preset historical window. Specifically, the calculation involves multiplying the absolute value of the sine of the historical wind speed and the corresponding historical wind direction angle at each moment within the preset historical window, and then determining the maximum and minimum values ​​from all products.

[0038] The variance of all sine values ​​in the sine sequence is calculated as the first variance, and the variance of all cosine values ​​in the cosine sequence is calculated as the second variance. The sum of the first and second variances is normalized and used as the wind direction change risk factor. The larger the wind direction change risk factor, the more chaotic the wind direction is in the next flight period, and the more likely there will be strong turbulence, gusts, etc., and the greater the impact on the target will be. The normalization here can be performed using maximum and minimum value normalization, where the minimum value can be 0, representing the ideal state of absolutely stable wind direction, and the maximum value can be 1, representing the extreme turbulent state of completely random wind direction distribution within 360 degrees.

[0039] Finally, since different attitude parameters have varying aerodynamic sensitivities to weather parameters, a weighted fusion of the crosswind risk factor and the sudden wind direction risk factor is performed using preset weighted weights corresponding to each attitude parameter. For any attitude parameter, the preset weighted weights include a preset first weighted weight and a preset second weighted weight. The corresponding preset first weighted weight is multiplied by the crosswind risk factor to obtain the first risk product. The corresponding preset second weighted weight is multiplied by the sudden wind direction risk factor to obtain the second risk product. The sum of the first and second risk products is limited using a saturation function to obtain the comprehensive risk factor for each attitude parameter. The larger the comprehensive risk factor, the greater the impact of weather parameters on the target in the next flight period, and the more severe the need for intervention or compensation. Here, the saturation function normalizes the sum of the first and second risk products. When the sum is greater than or equal to 1, the output is 1; when the sum is less than or equal to 0, the output is 0.

[0040] It should be noted that when calculating the comprehensive risk factor, for the roll angle, the preset first weighting weight can be 0.8 and the preset second weighting weight can be 0.2; for the pitch angle, the preset first weighting weight can be 0.6 and the preset second weighting weight can be 0.4; and for the yaw angle, the preset first weighting weight can be 0.7 and the preset second weighting weight can be 0.3. The specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0041] After determining the comprehensive risk factors, they can be combined with the target's preset flight parameters and weather forecast parameters to calculate the feedforward attitude compensation for each attitude parameter. This allows for the output of pre-adjusted control quantities before wind disturbances actually affect the target, thus avoiding response lag issues.

[0042] Preferably, in one embodiment of the present invention, the method for obtaining the feedforward attitude compensation amount includes: Since the comprehensive risk factor is only used to characterize the degree of influence of weather parameters faced by the target in the next flight period and the degree of intervention or compensation required, it can be integrated with the preset feedforward compensation gain coefficient characterizing the compensation amount of attitude parameters to obtain the specific physical compensation value of each attitude parameter. Therefore, the comprehensive risk factor of each attitude parameter is first multiplied by the corresponding preset feedforward compensation gain coefficient to obtain the absolute value of feedforward compensation for each attitude parameter. The larger the absolute value of feedforward compensation, the greater the amount of compensation required.

[0043] After obtaining the specific compensation values ​​for each attitude parameter, the direction of compensation can be further determined: Calculate the angle between the target's expected mean heading angle and the average wind direction in the next flight period. The cosine value can be used to extract the wind component in the longitudinal direction (parallel to the heading), which will cause the target to accelerate upwards or decelerate and lose altitude. The sine value is used to extract the wind component in the lateral direction (perpendicular to the heading), which will directly cause lateral drift. Therefore, after obtaining the angle between the expected mean heading angle and the average wind direction, the cosine value of the angle can be calculated. A positive cosine value indicates a headwind, which will cause increased airspeed and upwards, requiring a lower pitch angle. Conversely, a negative cosine value indicates a tailwind, which will cause relative airflow. The descent causes a drop in altitude, necessitating an increase in the pitch angle. Therefore, the opposite sign of the cosine of the obtained angle is used as the feedforward compensation direction value for the pitch angle. Simultaneously, the sine of the obtained angle is calculated. A positive sine indicates that the crosswind is blowing from one side of the target's flight path (e.g., the right side), and the physical thrust applied by the weather parameters will cause the aircraft to deflect to the left. Conversely, a negative sine indicates that the crosswind is blowing from the other side of the target's flight path (e.g., the left side), and the physical thrust applied by the weather parameters will cause the aircraft to deflect to the right. To determine whether the target needs to tilt or yaw to the left or right, the sign of the sine of the obtained angle is directly used as the feedforward compensation direction value for the roll and yaw angles, used to counteract the lateral drift caused by the crosswind.

[0044] Finally, the product of the feedforward compensation direction value and the absolute value of the feedforward compensation for each attitude parameter is used as the feedforward attitude compensation amount for each attitude parameter. At this time, the absolute value of the feedforward attitude compensation amount can be used to characterize the size of the angle that needs to be adjusted to resist wind disturbance, while the sign value is used to indicate the direction of spatial movement that needs to be adjusted.

[0045] It should be noted that, in this embodiment of the invention, the preset feedforward compensation gain coefficient can be specifically set to a pitch angle of 4 degrees, a roll angle of 8 degrees, and a yaw angle of 5 degrees.

[0046] Step S4: The initial attitude compensation amount and the feedforward attitude compensation amount are fused together, and the optimization algorithm is used to perform rolling optimization within the preset attitude safety limit, thereby outputting the expected flight attitude parameters of the target at the next moment and performing attitude control.

[0047] The initial attitude compensation amount for each attitude parameter obtained in step S2 can be used to correct the flight path deviation that has already occurred. The feedforward attitude compensation amount for each attitude parameter obtained in step S3 can be used to prevent wind interference that is about to occur. Therefore, in this step, the two can be fused together. On this basis, the optimization algorithm is used to perform rolling optimization within the preset attitude safety limit, thereby outputting the expected flight attitude parameters of the target at the next moment.

[0048] Preferably, in one embodiment of the present invention, the method for obtaining the desired flight attitude parameters includes: First, the initial attitude compensation values ​​of all types of attitude parameters are combined into an initial attitude compensation vector. Then, the feedforward attitude compensation values ​​of all types of attitude parameters are combined into a feedforward attitude compensation vector. Finally, the attitude parameters of the target at the current flight moment are combined into a real-time attitude vector.

[0049] The real-time attitude vector, the initial attitude compensation vector, and the feedforward attitude compensation vector are summed, and the summation result is used as the optimization center attitude of the beetle-shaped optimization algorithm in the first iteration.

[0050] Next, in each iteration, a random unit vector in three-dimensional space is taken as the unit search direction vector. The product of the preset whisker length and the unit search direction vector is used as the adjustment vector. The optimization algorithm makes subsequent selection judgments by comparing the costs in the two directions. Therefore, the sum of the optimization center attitude and the adjustment vector is used as the right whisker candidate attitude, and the difference between the optimization center attitude and the adjustment vector is used as the left whisker candidate attitude. The predicted spatial coordinates of the target after a preset standard time step are deduced under the attitudes corresponding to the left and right whisker candidate attitudes: based on the pitch, roll, and yaw angles in the left and right whisker candidate attitudes, the target is positioned in the current three-dimensional coordinate system. The flight speed during the flight period is orthogonally decomposed to obtain the left and right three-dimensional velocity components. The left and right three-dimensional velocity components are multiplied by a preset standard time step to obtain the left and right three-dimensional displacement increments. Then, the spatial coordinates of the target in the current flight period are added to the left and right three-dimensional displacement increments to obtain the predicted spatial coordinates of the target in the left and right candidate attitudes, respectively. The predicted spatial coordinates represent the specific position coordinates that the target will actually reach in the real three-dimensional physical space after flying in the attitude corresponding to the left and right candidate attitudes for the preset standard time step.

[0051] Furthermore, the Euclidean distance between the predicted spatial coordinates and expected spatial coordinates of the left and right candidate attitudes is calculated separately, serving as the left and right cost values. A larger cost value indicates a larger yaw error. Therefore, a sign function is used to process the difference between the left and right cost values ​​to obtain a decision sign value. A positive decision sign value indicates a smaller right-side error, requiring the optimization center to be moved to the right. A negative decision sign value indicates a smaller left-side error, requiring the optimization center to be moved to the left. Thus, the product of the decision sign value, the preset iteration attitude step size, and the unit search direction vector is used as the attitude adjustment amount. The sum of the attitude adjustment amount and the optimization center attitude in each iteration is used as the optimization center attitude for the next iteration. The preset attitude safety limit corresponding to each attitude parameter is used to limit the optimization center attitude for the next iteration. The preset iteration attitude step size is multiplied by the preset step size decay rate to obtain the preset iteration attitude step size for the next iteration.

[0052] Finally, when the preset iteration termination condition is met, the iteration optimization stops, and the expected pitch angle, expected roll angle and expected yaw angle of the target at the next moment are obtained from the vector corresponding to the final optimization center attitude.

[0053] It should be noted that in this embodiment of the present invention, the preset beard length is set to 2 degrees, the preset iterative attitude step size in the first iteration process is set to 1 degree, the preset step size decay rate is set to 0.95, the preset safety limit for pitch angle is set to ±20 degrees, the preset safety limit for roll angle is set to ±45 degrees, the preset safety limit for yaw angle is set to ±15 degrees, and the preset iteration termination condition is set to 50 iterations; the expected spatial coordinates can be obtained from the continuous three-dimensional flight trajectory curve obtained by mission heading analysis.

[0054] After calculating the target's expected pitch angle, expected roll angle, and expected yaw angle at the next moment based on the aforementioned optimization algorithm, the target's attitude can be controlled based on these indicators.

[0055] Preferably, in one embodiment of the present invention, attitude control includes: Since the expected pitch angle, expected roll angle, and expected yaw angle at the next moment are the specific attitudes of the target aircraft in space, the aerodynamic control surface deflection commands need to be calculated by the target's onboard flight control computing equipment based on the expected pitch angle, expected roll angle, and expected yaw angle at the next moment in order to provide the target with executable action standards.

[0056] Finally, according to the aerodynamic control surface deflection command, the target's actuator is driven to perform an action, wherein the actuator includes at least one of the tail fin, stabilizing fin, and lower aerodynamic control fin.

[0057] It should be noted that the airborne flight control computing device in this embodiment of the invention is the core hardware unit of the target's internal control closed loop, typically including a core microprocessor (such as a DSP, ARM, or FPGA chip), a storage unit, and input / output interfaces. The storage unit is used not only to store the program instructions of the aforementioned feedforward and feedback fusion algorithm, but also to pre-store the target's aerodynamic control allocation law. The airborne flight control computing device calls the aerodynamic control allocation law through its core microprocessor to dynamically decouple the three-dimensional desired attitude (desired pitch angle, desired roll angle, and desired yaw angle), mapping it to the specific physical deflection angles required by the aerodynamic surfaces at each specific position. Subsequently, through the input / output interfaces, the physical deflection angles are converted into low-level electrical drive signals (such as PWM pulse width modulation signals or CAN bus digital commands) that can be recognized by the servo motors, and then sent to the actuators on each distributed node of the target.

[0058] In summary, the first step is to obtain the target's flight parameters, weather parameters, and attitude parameters during the current flight period. In actual flight, the target is often subjected to complex aerodynamic factors, causing deviations between the flight parameters and preset values. Therefore, analyzing the deviation between the current flight parameters and preset parameters to calculate the initial attitude compensation can promptly correct any deviations in the flight trajectory. Furthermore, during actual flight, under severe weather conditions such as strong winds, gusts, crosswinds, vertical winds, or turbulence, the target's actual trajectory is highly prone to deviating from the expected trajectory. Therefore, by matching the weather changes during the current flight period with preset historical windows and extracting weather forecast parameters, the data scarcity and response lag inherent in relying on real-time sensing are overcome. This enables accurate advance prediction of short-term severe weather trends in local flight areas. Simultaneously, by calculating a comprehensive risk factor based on the numerical characteristics of the weather forecast parameters and combining it with the target's preset flight parameters to calculate the feedforward attitude compensation, pre-adjustments for impending wind disturbances can be made, fundamentally avoiding control lag problems when wind disturbances change drastically. Finally, the initial attitude compensation amount and the feedforward attitude compensation amount are fused together, and the rolling optimization output is performed within the preset attitude safety limit using an optimization algorithm. This takes into account both the elimination of current errors and the resistance to future wind disturbances, and avoids mechanical stall or attitude instability caused by control overshoot under extreme weather conditions. Ultimately, this significantly improves the stability of target flight and the accuracy of trajectory simulation under adverse weather conditions.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for target flight attitude control under adverse weather conditions, characterized in that, The method includes: Acquire the target's flight parameters, weather parameters, and various attitude parameters during the current flight period; During the current flight period, the deviation between the target's flight parameters and the preset flight parameters is analyzed, and the initial attitude compensation amount for each attitude parameter is calculated. The changes in weather parameters of the target during the current flight period are matched with the changes in weather parameters within a preset historical window. Weather parameter samples are then extracted from the preset historical window as weather prediction parameters for the target in the next flight period. Based on the numerical characteristics and changes of the weather prediction parameters, a comprehensive risk factor for the target in the next flight period is calculated. Based on the comprehensive risk factor, and in combination with the target's preset flight parameters and weather prediction parameters, the feedforward attitude compensation amount for each attitude parameter is calculated. The initial attitude compensation amount is fused with the feedforward attitude compensation amount, and an optimization algorithm is used to perform rolling optimization within the preset attitude safety limit, thereby outputting the target's expected flight attitude parameters at the next moment and performing attitude control.

2. The target flight attitude control method under adverse weather conditions according to claim 1, characterized in that, The flight parameters include flight speed, spatial coordinates, and heading angle. The spatial coordinates include flight altitude, lateral coordinates, and longitudinal coordinates. The attitude parameters include pitch angle, roll angle, and yaw angle. The weather parameters include temperature, wind speed, and wind direction.

3. The target flight attitude control method under adverse weather conditions according to claim 2, characterized in that, The method for obtaining the initial attitude compensation amount includes: During the current flight period, the target's flight altitude, lateral coordinate, and heading angle are subtracted from the preset values ​​of the target's flight altitude, lateral coordinate, and heading angle at the current moment, and the resulting difference is normalized to obtain the corresponding trajectory deviation value. Multiply the trajectory deviation value corresponding to the flight altitude by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the pitch angle. Multiply the trajectory deviation value corresponding to the lateral coordinate by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the roll angle. Multiply the trajectory deviation value corresponding to the heading angle by the corresponding preset compensation gain coefficient, and the resulting product is used as the initial attitude compensation amount for the yaw angle.

4. The target flight attitude control method under adverse weather conditions according to claim 2, characterized in that, The methods for obtaining the weather forecast parameters include: Use the current flight time period as the current weather time window, and obtain the wind speed curve, wind direction curve, and temperature curve within the current weather time window; The wind direction curve in the current weather time window and the wind direction curve in the weather parameters in the preset historical window are decomposed by sine and cosine respectively to obtain two wind direction vector component curves corresponding to the current weather time window and the preset historical window respectively. The wind direction vector component, wind speed, and temperature are used as target parameters; Using a sliding window and a preset step size, a historical parameter window with the same length as the current weather time window is extracted from the weather parameters within the preset historical window. Analyze the similarity features between each historical parameter window and the target parameter in the current weather time window to obtain the matching index between each historical parameter window and the current weather time window; The historical parameter window corresponding to the maximum matching index is used as a reference sample window, and various weather parameters within a preset time period after the reference sample window are extracted from the preset historical window as the weather prediction parameters for the target in the next flight period.

5. The target flight attitude control method under adverse weather conditions according to claim 4, characterized in that, The methods for obtaining the matching metrics include: Calculate the DTW value between each historical parameter window and the curve of each target parameter in the current weather time window. Perform negative correlation mapping and normalization on the obtained DTW values ​​to obtain the similarity factor. Use the preset weighting factor corresponding to the target parameter to weight and fuse the similarity factors corresponding to all types of target parameters to obtain the matching index between each historical parameter window and the current weather time window.

6. The target flight attitude control method under adverse weather conditions according to claim 2, characterized in that, The methods for obtaining the comprehensive risk factors include: Obtain the predicted wind speed and predicted wind direction curves from the weather forecast parameters for the target in the next flight period; The average wind speed is obtained by averaging the wind speed values ​​in the predicted wind speed curve. The wind direction angle values ​​in the predicted wind direction curve are converted into sine and cosine values ​​respectively, thus obtaining a sine sequence and a cosine sequence. The mean of all sine values ​​and the mean of all cosine values ​​are calculated in the sine and cosine sequences respectively. The mean of the obtained sine values ​​is used as the vertical axis and the mean of the cosine values ​​is used as the horizontal axis to construct a direction vector. The angle between the direction vector and the preset reference direction is calculated as the average wind direction. Calculate the absolute value of the sine of the angle between the mean expected heading angle of the target in the next flight period and the mean wind direction, multiply it by the mean wind speed, and then normalize it to obtain the crosswind risk factor. The variance of all sine values ​​in the sine sequence is calculated as the first variance, and the variance of all cosine values ​​in the cosine sequence is calculated as the second variance. The sum of the first and second variances is normalized and used as the wind direction change risk factor. The crosswind risk factor and the wind direction change risk factor are weighted and fused using the preset weights corresponding to each attitude parameter, and the weighted result is limited using a saturation function to obtain the comprehensive risk factor for each attitude parameter.

7. The target flight attitude control method under adverse weather conditions according to claim 6, characterized in that, The method for obtaining the feedforward attitude compensation amount includes: The comprehensive risk factor for each attitude parameter is multiplied by the corresponding preset feedforward compensation gain coefficient to obtain the absolute value of the feedforward compensation for each attitude parameter. Calculate the angle between the average wind direction and the average expected heading angle of the target in the next flight period. Use the opposite sign of the cosine of the obtained angle as the feedforward compensation direction value of the pitch angle, and use the sign of the sine of the obtained angle as the feedforward compensation direction value of the roll angle and yaw angle. The product of the feedforward compensation direction value and the absolute value of the feedforward compensation for each attitude parameter is used as the feedforward attitude compensation amount for each attitude parameter.

8. The target flight attitude control method under adverse weather conditions according to claim 2, characterized in that, The method for obtaining the desired flight attitude parameters includes: The initial attitude compensation values ​​of all types of attitude parameters are combined into an initial attitude compensation vector, the feedforward attitude compensation values ​​of all types of attitude parameters are combined into a feedforward attitude compensation vector, and the attitude parameters of the target at the current flight moment are combined into a real-time attitude vector. The real-time attitude vector, the initial attitude compensation vector, and the feedforward attitude compensation vector are vector summed, and the summation result is used as the optimization center attitude of the beetle whisker optimization algorithm in the first iteration process. In each iteration, a random unit vector in three-dimensional space is taken as the unit search direction vector, the product of the preset beard length and the unit search direction vector is taken as the adjustment vector, the sum of the optimization center pose and the adjustment vector is taken as the right beard candidate pose, and the difference between the optimization center pose and the adjustment vector is taken as the left beard candidate pose. The predicted spatial coordinates of the target after a preset standard time step are derived under the attitudes corresponding to the left and right candidate attitudes, respectively. The Euclidean distance between the predicted spatial coordinates and expected spatial coordinates of the left and right candidate poses is calculated separately and used as the left and right cost values. The difference between the left and right cost values ​​is processed using a sign function to obtain a decision sign value. The product of the decision sign value, the preset iterative pose step size, and the unit search direction vector is used as the pose adjustment amount. The sum of the pose adjustment amount and the optimization center pose in each iteration is used as the optimization center pose for the next iteration. The preset pose safety limit corresponding to each pose parameter is used to limit the optimization center pose for the next iteration. The preset iterative pose step size is multiplied by the preset step size decay rate to obtain the preset iterative pose step size for the next iteration. When the preset iteration termination condition is met, the iteration optimization stops, and the expected pitch angle, expected roll angle and expected yaw angle of the target at the next moment are obtained from the vector corresponding to the final optimization center attitude.

9. The target flight attitude control method under adverse weather conditions according to claim 8, characterized in that, The method for obtaining the predicted spatial coordinates includes: Based on the pitch, roll and yaw angles in the left and right candidate attitudes, the target's flight speed during the current flight period is orthogonally decomposed in the three-dimensional coordinate system to obtain the three-dimensional velocity components of the left and right whiskers. Multiply the left and right three-dimensional velocity components by a preset standard time step to obtain the left and right three-dimensional displacement increments. Add the target's spatial coordinates in the current flight period to the left and right three-dimensional displacement increments to obtain the predicted spatial coordinates of the target in the left and right candidate attitudes, respectively.

10. A target flight attitude control method under adverse weather conditions according to claim 8, characterized in that, The attitude control includes: The aerodynamic control surface deflection command is calculated using the airborne flight control computing equipment on the target, based on the desired pitch angle, desired roll angle, and desired yaw angle at the next moment. According to the aerodynamic control surface deflection command, the actuator of the target is driven to perform an action, wherein the actuator includes at least one of a tail fin, a stabilizing fin, and a lower aerodynamic control fin.

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