A method of automatic approach control based on a helicopter

Abstract

This application discloses an automatic approach control method based on a helicopter, comprising: acquiring heading signals from an instrument landing system and a magnetic heading sensor; extracting high-frequency jitter components from the signals; smoothing the signals using a low-pass filter to remove high-frequency interference; analyzing the dynamic characteristics of eddy current disturbances based on an interaction model of runway topography and rotor wake; constructing a mathematical expression for the propagation law of eddy current disturbances; determining the changing trends of disturbance intensity and direction; introducing an eddy current angle disturbance observer into the heading control law to estimate the impact of eddy current disturbances on heading control in real time; triggering a periodic pitch compensation mechanism if the disturbance intensity exceeds a preset range; and adjusting the amplitude and frequency of periodic pitch compensation using an adaptive control algorithm; dynamically updating compensation parameters to offset the heading deflection moment caused by ground effects if crosswind conditions change significantly.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an automatic approach control method based on a helicopter. Background Technology

[0002] In helicopter automatic flight control systems, the smoothness of the heading channel is affected by various factors. First, the heading signal provided by the instrument landing system is susceptible to electromagnetic interference during reception, resulting in high-frequency jitter. Although coherent demodulation techniques can extract useful information, they cannot completely eliminate the jitter. Simultaneously, data from the magnetic heading sensor and fiber optic gyroscope are also prone to electromagnetic interference at night, causing deviations in the estimated heading angle. To obtain a more accurate heading angle, an angle fusion Kalman filter needs to be constructed to optimize the data from multiple sensors. However, effectively fusing data from different sensors and eliminating heading angle jitter caused by nighttime electromagnetic interference remains a significant technical challenge.

[0003] On the other hand, the interaction between the helicopter rotor wake and the runway topography generates vortex disturbances, which are particularly pronounced under crosswind conditions. The dynamic characteristics and propagation laws of these vortex disturbances are complex, directly affecting the stability of heading control. Currently, research on the interaction model between the rotor wake and runway topography is insufficient, making it difficult to accurately predict the intensity and direction of vortex disturbances. Therefore, when introducing an vortex angle disturbance observer into the heading control law, how to estimate vortex disturbances in real time and perform periodic pitch compensation to counteract the heading deflection moment caused by changes in ground effect is also a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a helicopter-based automatic approach control method that enables more efficient and accurate detection of electricity meters in power management areas, thereby saving power resources.

[0005] This application provides an automatic approach control method based on a helicopter, including:

[0006] S101: Acquire the heading signals from the instrument landing system and magnetic heading sensor, extract the high-frequency jitter components in the signals, and use a low-pass filter to smooth the signals and remove high-frequency interference;

[0007] S102, the processed heading signal and the data from the fiber optic gyroscope are time-aligned, an angle fusion Kalman filter is constructed, and the fusion result of the sensor data is optimized by weight allocation to obtain the heading angle estimate;

[0008] S103 introduces a dynamic threshold detection mechanism to address heading angle jitter caused by nighttime electromagnetic interference. If the heading angle change exceeds a preset threshold, the sensor data is recalibrated to eliminate the impact of outliers on the fusion results.

[0009] S104. Based on the interaction model between runway topography and rotor wake, analyze the dynamic characteristics of vortex disturbance, construct a mathematical expression for the propagation law of vortex disturbance, and determine the changing trends of disturbance intensity and direction.

[0010] S105 introduces an eddy current angle disturbance observer into the heading control law to estimate the impact of eddy current disturbance on heading control in real time. If the disturbance intensity exceeds the preset range, a periodic pitch compensation mechanism is triggered.

[0011] S106 employs an adaptive control algorithm to adjust the amplitude and frequency of periodic pitch compensation. If crosswind conditions change significantly, the compensation parameters are dynamically updated to counteract the heading deflection moment caused by the ground effect.

[0012] S107: The optimized heading angle estimate and periodic compensation result are input into the flight control system to generate heading control commands. If the control commands deviate too much from the current flight status, the control parameters are recalibrated.

[0013] S108, by monitoring the operating status of the heading control system in real time, triggers the data fusion and disturbance observer re-optimization process if abnormal jitter or deviation is detected.

[0014] Preferably, S101 specifically includes:

[0015] The heading signal is obtained based on the landing system and the magnetic heading sensor;

[0016] High-frequency jitter components are extracted from the heading signal to determine the characteristic frequency range of the high-frequency jitter;

[0017] The filter is configured using preset low-pass filter parameters to filter high-frequency jitter components.

[0018] Based on the filtered signal, determine whether the high-frequency interference has been completely removed. If not, adjust the filtering parameters.

[0019] The filtered signal is smoothed using a smoothing algorithm to obtain a smooth signal free of high-frequency interference.

[0020] Based on the characteristics of the smoothed signal, determine whether the signal meets the preset smoothness threshold. If it does not meet the threshold, readjust the smoothing processing parameters.

[0021] The output is a smooth heading signal with high-frequency interference removed, completing the signal processing flow.

[0022] Preferably, S102 specifically includes:

[0023] The sampling timestamps of the heading signal and fiber optic gyroscope data are obtained, and the two data sources are time-aligned using an interpolation algorithm to obtain synchronized time series data.

[0024] Construct an angle fusion Kalman filter, set the state transition matrix and observation matrix, and initialize the filter parameters, including process noise covariance and observation noise covariance;

[0025] The heading angle information is extracted from the time-aligned data and used as the observation input for the Kalman filter. At the same time, the angular velocity information of the fiber optic gyroscope is obtained and used as the state prediction input.

[0026] The Kalman filter algorithm is used for iterative calculation to obtain the optimal estimate of the heading angle, and the filter state and error covariance matrix are updated.

[0027] Based on the sensor accuracy indicators, calculate the weighting coefficients of the heading signal and fiber optic gyroscope data, and determine the data fusion ratio.

[0028] By using a weighted fusion algorithm, combining the Kalman filter output and the original sensor data, an optimized heading angle estimate is obtained;

[0029] If there is a large deviation between the heading signal and the fiber optic gyroscope data, the weight allocation coefficients are adjusted and the fusion result is recalculated until the deviation is less than the preset threshold.

[0030] Preferably, S103 specifically includes:

[0031] Acquire the heading angle data stream, calculate the heading angle jitter value, and obtain the change;

[0032] Based on the dynamic threshold detection mechanism, it is determined whether the change exceeds the preset threshold;

[0033] If the change exceeds the preset threshold, acquire the sensor data stream and recalculate the calibration value; if it does not exceed the preset threshold, no further operation is required.

[0034] Eliminate outliers and retain the calibrated sensor data stream;

[0035] The calibrated sensor data stream is fused with the fusion result;

[0036] Analyze the impact of interference sources on the heading angle data stream and extract interference features;

[0037] Based on the interference characteristics, the dynamic threshold detection mechanism is updated, and the preset threshold is updated.

[0038] Preferably, S104 specifically includes:

[0039] For the eddy current model, the frequency characteristics of the disturbance wave are extracted by Fourier transform to determine the main propagation direction of the eddy current disturbance;

[0040] Based on the propagation direction characteristics of the disturbance wave, a mathematical formula for the propagation of eddy current disturbance is constructed using partial differential equations to obtain the spatial distribution of the disturbance intensity value.

[0041] Based on the mathematical calculation results, the trend of intensity value change is analyzed through gradient operation to determine the increase or decrease pattern of disturbance intensity at different locations;

[0042] Based on the intensity value distribution characteristics, the vector analysis method is used to calculate the change characteristics of the direction angle and determine the main direction change law of the eddy current disturbance;

[0043] Based on the characteristics of directional angle variation, a dynamic model of eddy current disturbance is constructed through statistical analysis to obtain the spatiotemporal evolution law of the disturbance field;

[0044] For the dynamic model, a machine learning algorithm is used to fit the relationship between the force and the law of change, and to predict the evolution trend of eddy current disturbance.

[0045] Preferably, S105 specifically includes:

[0046] Acquire real-time heading data from the heading control system and, in conjunction with a pre-set heading control model, determine the heading deviation value;

[0047] By using an eddy angle disturbance observer, the eddy disturbance angle is extracted from the heading deviation value to obtain the eddy disturbance intensity value;

[0048] Based on the preset disturbance intensity range, determine whether the eddy current disturbance intensity value exceeds the preset range;

[0049] If the eddy current disturbance intensity value exceeds the preset range, obtain the execution parameters of the periodic pitch compensation mechanism; if it does not exceed the preset range, no further operation is required.

[0050] A periodic pitch compensation mechanism is used to generate pitch compensation signals to adjust the dynamic response of the heading control system.

[0051] Based on the pitch compensation signal, update the output of the heading control model to obtain the corrected heading data;

[0052] Using the corrected heading data, the heading deviation value is recalculated to complete the closed-loop adjustment of the heading control system.

[0053] Preferably, step S106 specifically includes:

[0054] Acquire real-time crosswind data of the aircraft and determine whether the crosswind conditions exceed a preset threshold.

[0055] If the crosswind conditions exceed the threshold, the adaptive control algorithm is used to calculate the amplitude and frequency of periodic pitch compensation; if the crosswind conditions do not exceed the preset threshold, no further operation is required.

[0056] Update the compensation parameters based on the calculation results and generate new control commands;

[0057] Acquire the aircraft's heading deflection data to determine if there is a torque caused by the ground effect;

[0058] If a ground effect torque exists, an adaptive control algorithm is used to adjust the compensation parameters; if no ground effect torque exists, no further action is required.

[0059] The adjusted compensation parameters are applied to the periodic pitch control system;

[0060] The yaw moment is counteracted by performing compensation actions through a periodic pitch control system.

[0061] Preferably, S107 specifically includes:

[0062] The optimized heading angle estimate and periodic compensation result are obtained and input into the flight control system to generate heading control commands;

[0063] The deviation between the control command and the current flight status is compared using a preset threshold to determine whether the deviation exceeds the preset threshold.

[0064] If the deviation value exceeds the preset threshold, the control parameters are recalculated based on the deviation value, and the control parameter table is updated.

[0065] The heading control command is regenerated using the updated control parameters and input into the flight control system.

[0066] Obtain the deviation between the updated heading control command and the current flight status, and determine whether the deviation is within a preset threshold.

[0067] If the deviation value is within the preset threshold, the current heading control command is determined to be the final control command;

[0068] Flight control is executed using final control commands to complete the heading control process.

[0069] Preferably, S108 specifically includes:

[0070] Sensors are used to acquire real-time operational data of the heading control system, and the heading control parameters in the data are analyzed.

[0071] The heading control parameters are judged to determine whether there are abnormal jitters or deviations based on the preset threshold. If an abnormality is detected, the trigger bar is marked.

[0072] For the marked trigger bar, multi-source sensor data is extracted from the data fusion module and processed for data fusion.

[0073] By analyzing the fused data using the disturbance observation module, the source of the disturbance and its impact on heading control can be identified.

[0074] Based on the disturbance analysis results, the parameters of the disturbance observation module are readjusted using an optimization algorithm;

[0075] The optimized disturbance observation module is integrated with the heading control system to generate new control commands and adjust the channel configuration.

[0076] The heading control system is continuously tracked and adjusted through a real-time monitoring module.

[0077] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0078] By fusing and filtering data from the Instrument Landing System (ILS), magnetic heading sensor, and fiber optic gyroscope, a more accurate heading angle estimation is achieved. A dynamic threshold detection mechanism and eddy current disturbance observer are introduced to effectively identify and compensate for abnormal heading changes. An adaptive control algorithm is employed to adjust periodic pitch compensation, counteracting heading deflection caused by ground effects. This invention significantly improves the accuracy and stability of heading control through multi-sensor data fusion, dynamic threshold detection, eddy current disturbance modeling, and adaptive control technologies. It is particularly suitable for flight control at night and in complex terrain conditions, providing a strong guarantee for enhancing flight safety. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating an automatic approach control method based on a helicopter according to an embodiment of the present invention. Detailed Implementation

[0080] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0081] 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; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0082] Example 1: Figure 1This is a flowchart illustrating an automatic approach control method based on a helicopter according to an embodiment of the present invention.

[0083] like Figure 1 As shown, an automatic approach control method based on a helicopter includes the following steps:

[0084] S101: Acquire the heading signals from the instrument landing system and magnetic heading sensor, extract the high-frequency jitter components from the signals, and use a low-pass filter to smooth the signals and remove high-frequency interference.

[0085] High-frequency jitter refers to the higher-frequency, rapidly changing portions of the heading signal. It is typically caused by factors such as magnetic heading sensor noise, electromagnetic interference, or mechanical vibration. These high-frequency components may mask the true heading information, affecting the signal's accuracy and stability. The characteristic frequency range of high-frequency jitter needs to be determined through spectral analysis. This can usually be done by observing the signal's spectrum to identify the frequency range containing the high-frequency components. For example, a frequency threshold can be set, and frequencies exceeding this threshold can be considered high-frequency jitter. The threshold for identifying high-frequency jitter can be set according to application requirements and signal characteristics.

[0086] Specifically, the data acquisition module is connected to the landing system and the magnetic heading sensor to obtain heading signals from the landing system and the magnetic heading sensor, ensuring stable signal transmission. The sampling frequency of the data acquisition device is set to continuously capture heading signals and store them in raw data format (.txt or .csv file).

[0087] High-frequency jitter components were extracted from the heading signal to determine its characteristic frequency range. A Fast Fourier Transform (FFT) algorithm was used to perform spectral analysis on the continuously acquired heading signal, converting the time-domain signal to a frequency-domain signal. The characteristic frequency range of the high-frequency jitter was then determined by observing the spectrum.

[0088] Using preset low-pass filter parameters, a filter is configured to filter high-frequency jitter components. Based on the characteristic frequency range of the high-frequency jitter, the parameters of the low-pass filter (such as cutoff frequency, filter type, and order) are configured to filter the original heading signal and remove high-frequency jitter components. The filtered signal is then subjected to spectral analysis again. Based on the filtered signal, it is determined whether the high-frequency interference has been completely removed. If not, the filter parameters are adjusted, and the filtering process is repeated.

[0089] The filtered signal is smoothed using a smoothing algorithm to obtain a smoothed signal free of high-frequency interference. Based on the characteristics of the smoothed signal, it is determined whether the signal meets a preset smoothness threshold. If not, the smoothing parameters are readjusted. Finally, the smoothed heading signal free of high-frequency interference is output, completing the signal processing flow.

[0090] For example, when acquiring heading signals from the Instrument Landing System (ILS) and the magnetic heading sensor, the raw signals are captured by the data acquisition module at a sampling frequency of 100 times per second. These signals typically contain high-frequency jitter components. To extract these high-frequency components, a Fast Fourier Transform (FFT) algorithm is used to perform spectral analysis on the signals, identifying jitter components with frequencies higher than 20 Hz. A Butterworth low-pass filter is used to smooth these high-frequency jitters, with the filter's cutoff frequency set at 15 Hz to ensure effective removal of high-frequency interference. During the filtering process, a fourth-order Butterworth filter is used, with design parameters including a passband ripple of 5 dB and a stopband attenuation of 40 dB to ensure a smooth and distortion-free signal. After filtering, the high-frequency components in the signal are effectively suppressed, preserving stable heading information. Finally, by comparing the signal spectra before and after filtering, the filtering effect is verified, ensuring that the processed signal meets the accuracy requirements of the subsequent navigation system.

[0091] Step S102: Time-align the processed heading signal with the data from the fiber optic gyroscope, construct an angle fusion Kalman filter, and optimize the fusion result of the heading signal sensor and fiber optic gyroscope sensor data through weight allocation to obtain the heading angle estimate.

[0092] Specifically, the sampling timestamps of the heading signal and fiber optic gyroscope data are obtained, and an interpolation algorithm is used to time-align the two data sources to obtain synchronized time-series data. The timestamp information is extracted from both the heading signal and fiber optic gyroscope data sources. Since the two data sources may have inconsistent sampling frequencies or asynchronous sampling times, an interpolation algorithm is needed for time alignment. A reference time series (such as the timestamp of the heading signal) is selected, and the fiber optic gyroscope data is interpolated to align with the timestamp of the heading signal. Interpolation methods can include linear interpolation, spline interpolation, etc., selected based on data characteristics and requirements. After interpolation, synchronized time-series data is obtained, meaning the heading signal and fiber optic gyroscope data are synchronized in time. Assume the timestamp of the heading signal is t. h The timestamp of the fiber optic gyroscope is t. g The data from the fiber optic gyroscope needs to be interpolated to the timestamp of the heading signal.

[0093]

[0094] x1 and x2 are adjacent sampling time points of the fiber optic gyroscope, y1 and y2 are the corresponding sampled values, x is the sampling time point of the heading signal, and y is the interpolated fiber optic gyroscope data.

[0095] Based on the relationship between heading angle and angular velocity, an angle-fusion Kalman filter is constructed. The state transition matrix (describing how the heading angle and angular velocity of the vehicle change over time) and the observation matrix (describing how to obtain observation values ​​from the changing states of the vehicle's heading angle and angular velocity over time) are defined for the Kalman filter. The Kalman filter parameters (process noise covariance and observation noise covariance) are initialized. Heading angle information is extracted from the time-aligned data and used as the observation input to the Kalman filter; angular velocity information from the fiber optic gyroscope is obtained from the time-aligned data and used as the state prediction input.

[0096] The Kalman filter algorithm is used for iterative calculations to obtain the optimal estimate of the heading angle, updating the filter state and error covariance matrix. Using the state transition matrix and the state estimate from the previous time series, the current state value is predicted. The error covariance matrix is ​​updated based on the process noise covariance matrix and the state transition matrix. The Kalman gain is calculated based on the observation matrix, error covariance matrix, and observation noise covariance matrix. The Kalman gain determines the weight of the observation in the state update. The current state estimate is updated using the observation, Kalman gain, and predicted state value. The error covariance matrix is ​​updated based on the Kalman gain and observation noise covariance matrix, preparing for filtering at the next time step. For detailed prediction calculations, please refer to the Kalman filter steps; these will not be elaborated upon here.

[0097] Based on the accuracy specifications of the heading signal sensor and the fiber optic gyroscope sensor, weighting coefficients are calculated for the heading signal and fiber optic gyroscope data to determine the data fusion ratio. These weighting coefficients reflect the importance of each sensor's data in the fusion process.

[0098] By using a weighted fusion algorithm, combining the Kalman filter output and the raw sensor data, an optimized heading angle estimate is obtained. The fusion algorithm can take into account factors such as the accuracy and stability of each sensor and allocate weights appropriately.

[0099] If there is a significant deviation between the heading signal and the fiber optic gyroscope data (the deviation threshold can be set according to the specific application and sensor accuracy; for example, it can be set to ±5° or ±10°, depending on the system's accuracy requirements and the sensor's performance), it indicates that the current weight allocation may be unreasonable. In this case, the weight allocation coefficients need to be adjusted, and the fusion result recalculated. The weight allocation coefficients are iteratively adjusted (the iteration threshold can be set to a reasonable upper limit, such as 10 or 20 times, to avoid infinite iteration. If the iteration count exceeds this threshold and the deviation is still large, it may be necessary to check the sensor data or adjust the weight allocation strategy) until the deviation is less than the preset threshold, resulting in a satisfactory fusion result.

[0100] For example, when processing heading signals and fiber optic gyroscope data, the first step is to align the two in time. Assuming the sampling frequency of the heading signal is 100Hz and the sampling frequency of the fiber optic gyroscope is 50Hz, the data frequency of the fiber optic gyroscope can be increased to 100Hz using an interpolation algorithm to ensure that the two are perfectly aligned in time. Next, an angle fusion Kalman filter is constructed, with the state vector being the heading angle θ, the state transition matrix being F = [1], the observation matrix being H = [1], the process noise covariance matrix being Q =

[01] , and the observation noise covariance matrix being R =

[02] . Through the prediction and update steps of the Kalman filter, the predicted state θ_k|k-1=F*θ_k-1|k-1 and the prediction covariance P_k|k-1=F*P_k-1|k-1*F^T+Q are calculated respectively. Then, the Kalman gain K_k=P_k|k-1*H^T*(H*P_k|k-1*H^T+R)^(-1) is calculated. Finally, the updated state θ_k|k=θ_k|k-1+K_k*(z_k-H*θ_k|k-1) and covariance P_k|k=(I-K_k*H)*P_k|k-1 are calculated. To optimize the fusion results of sensor data, a weight allocation strategy is adopted. The weight of the heading signal sensor is set to 6, and the weight of the fiber optic gyroscope sensor is set to 4. The weighted average method is used to calculate the estimated heading angle after fusion, θ_fused = 6*θ_gps + 4*θ_fog, where θ_gps is the angle value of the heading signal and θ_fog is the angle value of the fiber optic gyroscope.

[0101] S103 introduces a dynamic threshold detection mechanism to address heading angle jitter caused by nighttime electromagnetic interference. If the heading angle change exceeds a preset threshold, the sensor data is recalibrated to eliminate the impact of outliers on the fusion results.

[0102] Specifically, the heading angle data stream is acquired, and the heading angle jitter value is calculated to obtain the change. Heading angle data is acquired in real time from the heading sensor (each data point includes a timestamp and the corresponding heading angle value). The jitter value is quantified as the change by calculating the heading angle difference between adjacent data points. Here, the jitter value refers to the degree of change of the heading angle within a time series.

[0103] The dynamic threshold detection mechanism determines whether the change exceeds a preset threshold. A preset threshold is set to determine whether the change in heading angle is normal. The threshold can be dynamically adjusted based on historical data, sensor characteristics, or application requirements. The calculated change is compared with the preset threshold; if the change exceeds the threshold, the heading angle data is considered to be abnormal or interfered with.

[0104] If the change exceeds a preset threshold, acquire the sensor data stream and recalculate the calibration value. When the change exceeds the preset threshold, a complete data stream needs to be acquired from the relevant sensor (heading signal sensor or fiber optic gyroscope sensor). Use the acquired data to recalculate the sensor calibration value. If the change does not exceed the preset threshold, the current heading angle data can be considered normal and has not been significantly abnormal or interfered with. Recalculation of the calibration value is not necessary because the current data is already considered reliable.

[0105] Eliminate outliers and retain the calibrated sensor data stream. Based on the calibration results, identify and eliminate outliers in the sensor data stream. Outliers may be caused by sensor malfunctions, environmental interference, or data transmission errors. Save the calibrated sensor data stream for subsequent data fusion and analysis.

[0106] The calibrated sensor data stream is fused with the previous fusion result (Kalman filter output). The fusion process can employ weighted averaging, Kalman filtering, or other data fusion algorithms. Through data fusion, a more accurate and stable heading angle estimate is obtained.

[0107] The impact of interference sources on the heading angle data stream is analyzed to extract interference features. Based on these features, the dynamic threshold detection mechanism is updated and optimized, and the preset threshold is updated. Interference sources causing anomalies in the heading angle data are analyzed, such as magnetic field interference and mechanical vibration. Features related to the interference sources, such as the frequency, amplitude, and duration of the interference, are extracted from the sensor data stream. Based on the extracted interference features, the parameters or strategies of the dynamic threshold detection mechanism are adjusted. For example, if the interference is mainly high-frequency noise, filtering for high-frequency components can be increased or the threshold sensitivity can be reduced. The preset threshold is updated based on the optimized dynamic threshold detection mechanism. This ensures that the new threshold adapts to current environmental conditions and sensor characteristics, improving the accuracy and stability of heading angle estimation.

[0108] For example, in addressing the heading angle jitter issue caused by nighttime electromagnetic interference, heading angle data is first acquired using a magnetic heading sensor at a sampling frequency of 100 Hz to ensure real-time data accuracy. Then, a dynamic threshold detection mechanism is designed, with an initial threshold of 5 degrees. Based on historical data statistics, the standard deviation of the heading angle is calculated to be 2 degrees, and the dynamic threshold is set to five times the standard deviation, i.e., 5 degrees. When the heading angle change exceeds 5 degrees, the system automatically triggers a recalibration process. During calibration, a Kalman filter algorithm is used to optimize the magnetic heading sensor data through two steps: state prediction and measurement update. In the state prediction stage, the state estimate from the previous time series and the system model are used to predict the current state. In the measurement update stage, the current measured value and the predicted value are combined, and the optimal estimate is obtained through Kalman gain weighting.

[0109] S104. Based on the interaction model between runway topography and rotor wake, the dynamic characteristics of vortex disturbance are analyzed, the propagation law of vortex disturbance is obtained, and the changing trends of disturbance intensity and direction are determined.

[0110] Specifically, based on topographic model and wake field data, a dynamic model of the eddy current state is constructed using fluid dynamics equations to obtain the initial distribution characteristics of the eddy current field. Topographic model data is collected, including topographic height h(x,y), slope θ, and roughness R. q Data such as flow velocity v(x,y,z,t), flow direction φ, and vorticity ω(x,y,z,t) are collected and preprocessed, including denoising, interpolation, and normalization, to ensure accuracy and consistency. Based on the characteristics of the vortex field, fluid dynamics equations are selected, considering the influence of terrain on the flow (which may require adding terrain-related source terms or boundary conditions to the equations). The fluid dynamics equations are discretized using numerical methods (such as the finite difference method, finite element method, and spectral method), and initial and boundary conditions are set by combining the terrain model and wake field data. Through numerical solutions, the initial distribution characteristics of the vortex field are obtained, including the location, size, and intensity of the vortices.

[0111] For the eddy current model, the frequency characteristics of the disturbance wave are extracted using Fourier transform to determine the main propagation direction of the eddy current disturbance. Based on the propagation direction characteristics of the disturbance wave, a mathematical formula for the propagation of the eddy current disturbance is constructed using partial differential equations to obtain the spatial distribution of the disturbance intensity values. Based on the calculation results of the mathematical formula, the trend of intensity value changes is analyzed through gradient calculation to determine the increase or decrease law of disturbance intensity at different locations. Based on the intensity value distribution characteristics, the change characteristics of the direction angle are calculated using vector analysis methods to determine the change law of the main direction of the eddy current disturbance. Based on the change characteristics of the direction angle, a dynamic model of the eddy current disturbance is constructed through statistical analysis to obtain the spatiotemporal evolution law of the disturbance field. For the dynamic model, a machine learning algorithm is used to fit the relationship between the force and the law of change to predict the long-term evolution trend of the eddy current disturbance.

[0112] For example, in the interaction model between runway topography and rotor wake, the dynamic characteristics of vortex disturbance are first analyzed using numerical simulation. Computational fluid dynamics (CFD) is employed, with the rotor speed set at 1200 rpm and the wake velocity at 15 m / s. The Navier-Stokes equations are used for solving the problem. The computational domain is discretized into 1 million grid cells to ensure computational accuracy. During the simulation, a turbulence model (such as the k-ε model) is introduced to capture the turbulent characteristics in the wake, and the boundary conditions are set to no-slip wall conditions to simulate the actual influence of the runway topography. Through iterative calculations, the velocity and pressure field distributions of the rotor wake are obtained, and the generation and evolution of vortices are further analyzed. When constructing the mathematical expression for the propagation law of vortex disturbance, the vorticity transport equation is used, combined with the continuity equation and momentum equation, to derive the relationship between vortex intensity and propagation distance. Assuming an initial vortex intensity of 5 and a propagation distance of 10 meters, the equations were solved using numerical integration to obtain the attenuation law of vortex intensity with distance. It was found that the vortex intensity decays exponentially during propagation, with an attenuation coefficient of 1. To determine the variation trend of disturbance intensity and direction, the gradient of the vortex velocity field was calculated using vector field analysis, yielding the spatial distribution of disturbance intensity. Statistical analysis revealed that the disturbance intensity is greatest below the rotor and gradually weakens with increasing distance. Furthermore, the disturbance direction is primarily along the rotor rotation direction, with a deflection angle of 5 degrees. These analytical results provide a scientific basis for optimizing rotor design and runway layout.

[0113] S105 introduces an eddy current angle disturbance observer into the heading control law to estimate the impact of eddy current disturbance on heading control in real time. If the disturbance intensity exceeds the preset range, a periodic pitch compensation mechanism is triggered.

[0114] Specifically, this involves acquiring real-time navigation data from the heading control system. Heading sensors are used to collect heading data from the heading control system, ensuring that the frequency and accuracy of data acquisition meet the system's requirements. The collected heading data undergoes preprocessing such as noise reduction and filtering to improve accuracy and reliability. Finally, the preprocessed heading data is converted into a format usable by the heading control system.

[0115] Based on a pre-defined heading control model, the heading deviation value is determined. According to the characteristics and requirements of the heading control system, a heading control model is pre-defined; this model should be able to describe the desired heading trajectory or target heading of the heading control system. Real-time heading data is compared with the heading control model to calculate the heading deviation value (the difference between the actual heading and the desired heading).

[0116] An eddy current angle disturbance observer is used to extract the eddy current disturbance angle from the heading deviation value. An eddy current angle disturbance observer is designed to extract the eddy current disturbance angle from the heading deviation value. The eddy current angle disturbance observer should be able to accurately identify and separate the influence of eddy current disturbance on the heading. The heading deviation value is input into the eddy current angle disturbance observer to extract the eddy current disturbance angle. The eddy current disturbance angle represents the direction of the disturbance generated by the eddy current on the heading control system.

[0117] The eddy current disturbance intensity value is obtained. Based on the changes in the eddy current disturbance angle and heading deviation, the eddy current disturbance intensity value is calculated. The eddy current disturbance intensity value represents the magnitude of the disturbance generated by the eddy current on the heading control system.

[0118] Based on a preset disturbance intensity range, determine whether the eddy current disturbance intensity value exceeds the preset range. According to the stability and performance requirements of the heading control system, a preset eddy current disturbance intensity range is established. This range should ensure that the heading control system remains stable when subjected to eddy current disturbances. Compare the calculated eddy current disturbance intensity value with the preset range to determine if it exceeds the preset range. If the eddy current disturbance intensity value exceeds the preset range, compensation adjustment is required; otherwise, no further action is necessary.

[0119] Obtain the execution parameters of the periodic pitch compensation mechanism. Design a periodic pitch compensation mechanism to adjust the dynamic response of the heading control system. The mechanism should be able to dynamically adjust the control parameters of the heading control system based on changes in the intensity of eddy current disturbances. Based on the intensity of the eddy current disturbances and the characteristics of the heading control system, obtain the execution parameters of the periodic pitch compensation mechanism. These parameters include the amplitude, frequency, and phase of the pitch compensation.

[0120] A pitch compensation signal is generated through a periodic pitch compensation mechanism. Based on the execution parameters, the pitch compensation signal is generated through the periodic pitch compensation mechanism. The pitch compensation signal should be able to offset the impact of eddy current disturbances on the heading control system, so as to keep the heading control system stable.

[0121] Adjusting the dynamic response of the heading control system. Inputting pitch compensation signals into the heading control system adjusts its dynamic response. By adjusting the pitch compensation signals, the heading control system can respond quickly to eddy current disturbances and maintain heading stability.

[0122] The output of the heading control model is updated based on the pitch compensation signal. The updated output should more accurately reflect the actual heading of the heading control system.

[0123] Using the corrected heading data, the heading deviation value is recalculated to complete the closed-loop adjustment of the heading control system. The recalculated heading deviation value should more accurately reflect the difference between the heading control system and the desired heading. The recalculated heading deviation value is then input into the heading control system for the next round of adjustment and control. Through continuous closed-loop adjustment and control, the heading control system can maintain a stable heading at all times.

[0124] For example, the heading control system acquires real-time heading data through sensors such as gyroscopes and magnetic compasses. The system sets the standard heading to 30 degrees east of north. When the measured heading is 35 degrees east of north, the heading deviation is 5 degrees. The eddy current angle disturbance observer is designed based on the Kalman filter algorithm and can separate the angle change caused by eddy current disturbance from the heading deviation. The observer extracts the disturbance angle caused by eddy currents by comparing the normal heading change rate with the actual heading change rate. For example, when the instantaneous heading change rate exceeds 2 degrees per second, it can be determined that eddy current disturbance exists. The intensity of eddy current disturbance can be represented by a dimensionless parameter, typically ranging from 0 to 10, with values ​​below 5 considered normal. When the disturbance intensity calculated by the observer is 7.5, it exceeds the preset safety range, requiring the activation of the compensation mechanism. The core of the periodic pitch compensation mechanism is to counteract the influence of eddy currents by adjusting the rotor blade angle. The execution parameters include the pitch amplitude and phase angle. When the eddy current disturbance intensity is 7.5, the system automatically sets the pitch amplitude to 4 degrees and the phase angle to 90 degrees, generating a corresponding compensation signal. This compensation signal acts on the actuator of the heading control system, causing the rotor blades to produce periodic pitch-changing motion, thereby counteracting the eddy current disturbance. The compensated heading is gradually corrected from the original 35 degrees east of north to the expected 30 degrees east of north. The heading control model is based on the proportional-integral-derivative (PI-DE) control principle, optimizing the system response by adjusting control parameters. In practical applications, a proportional gain of 0.8, an integral time constant of 2 seconds, and a derivative time constant of 0.5 seconds yield good control performance. The system continuously acquires heading data at a sampling frequency of 20 Hz, recalculating the heading deviation after each sampling to achieve closed-loop control. In this way, the heading control system can respond to eddy current disturbances in real time, maintaining a stable heading. When the eddy current disturbance weakens, the system reduces the compensation amount accordingly to avoid over-compensation. Practice shows that this control strategy can keep the heading deviation within ±2 degrees, effectively improving flight quality. By accumulating and analyzing heading data, a characteristic model of eddy current disturbances can be established, providing a basis for further optimization of control strategies. An eddy current angle disturbance observer is introduced into the heading control law. First, the impact of eddy current disturbances on heading control is estimated in real time using a Kalman filter algorithm. For example, at a flight speed of 150 m / s, the observer can update the eddy current angle disturbance value at a sampling frequency of 0.1 seconds. In the specific algorithm, the state equation and observation equation are based on the aircraft's dynamic model and sensor data, respectively, and the state estimate is updated by minimizing the prediction error. When the disturbance intensity exceeds a preset range (e.g., ±5 degrees), the system automatically triggers a periodic pitch compensation mechanism. The compensation mechanism uses a PID control algorithm with a proportional coefficient of 8, an integral coefficient of 2, and a derivative coefficient of 1, adjusting the control surface deflection angle at a period of 0.5 seconds. During the analysis, the system compares the real-time disturbance value with historical data to determine whether the disturbance is a normal fluctuation. If the disturbance continues to exceed the preset range, the compensation parameters are further adjusted.Throughout the process, the system ensures the stability and accuracy of heading control through real-time data stream processing and adaptive algorithm optimization. For example, when the disturbance intensity is 8 degrees, the compensation mechanism can control the heading deviation within ±5 degrees within 1 second.

[0125] S106 employs an adaptive control algorithm to adjust the amplitude and frequency of periodic pitch compensation. If crosswind conditions change significantly, the compensation parameters are dynamically updated to counteract the heading deflection moment caused by the ground effect.

[0126] Specifically, real-time crosswind data for the aircraft is acquired. Using anemometers and wind direction sensors mounted on the aircraft, wind speed and direction data at the aircraft's current location are collected in real time, ensuring high-frequency updates of the sensor data to accurately capture rapid changes in crosswind. The raw sensor data is smoothed using filters to remove noise and outliers. The crosswind component, i.e., the wind speed and direction perpendicular to the aircraft's flight direction, is calculated.

[0127] Determine if crosswind conditions exceed preset thresholds. Based on the aircraft's design specifications and performance limitations, set thresholds for crosswind speed and direction angle. Compare real-time crosswind data with these preset thresholds. If the crosswind speed or direction angle exceeds the threshold, the adaptive control algorithm is triggered. If the crosswind conditions do not exceed the preset thresholds, the current crosswind's impact on the aircraft is considered acceptable, and no further action is required.

[0128] An adaptive control algorithm is used to calculate the amplitude and frequency of periodic pitch compensation. An adaptive control algorithm suitable for the aircraft's dynamic characteristics, such as Model Reference Adaptive Control (MRAC) or Self-Tune Control (STC), is selected. Based on the magnitude and direction of the crosswind data deviation, the algorithm dynamically adjusts the amplitude (i.e., the change in blade angle) and frequency (i.e., the period of change) of the periodic pitch. The aircraft's current speed, altitude, attitude, and other state information are considered to ensure the accuracy and effectiveness of the compensation actions.

[0129] The compensation parameters are updated based on the calculation results, and new control commands are generated. The pitch amplitude and frequency calculated by the algorithm are used as new compensation parameters, and these parameters are written into the aircraft's control system, replacing the original compensation parameters. Based on the updated compensation parameters, new control commands are generated to adjust the aircraft's propeller angle or engine thrust.

[0130] Acquire the aircraft's heading deflection data. Using sensors such as gyroscopes and magnetic compasses, collect the aircraft's heading angle and heading deflection rate in real time, and filter and calibrate the raw data to ensure accuracy and reliability.

[0131] Determine if there is a torque caused by the ground effect. Analyze heading deflection data to identify abnormal heading deflections caused by ground effects (such as ground reflection airflow, ground friction, etc.), and set a threshold for identifying ground effect torques to determine if there is a significant impact from the ground effect.

[0132] An adaptive control algorithm is used to adjust the compensation parameters. If a ground effect torque is identified, the adaptive control algorithm is restarted, and the algorithm dynamically adjusts the compensation parameters of the periodic pitch according to the magnitude and direction of the ground effect torque. If no ground effect torque exists, no further action is required.

[0133] The adjusted compensation parameters are applied to the periodic pitch control system. By writing the adjusted compensation parameters into the aircraft's periodic pitch control system, the system can ensure that it responds to changes in these parameters in real time and accurately executes compensation actions.

[0134] The cyclic pitch control system performs compensation actions to counteract the yaw torque. Based on updated compensation parameters, the cyclic pitch control system adjusts the aircraft's propeller angle or engine thrust, generating a compensation torque opposite to the ground effect torque through periodic pitch changes, thereby counteracting yaw. The system monitors the aircraft's yaw in real time and further adjusts the compensation parameters as needed to ensure that the aircraft maintains a stable flight attitude and heading under complex environmental conditions.

[0135] S107 inputs the optimized heading angle estimate and periodic compensation result into the flight control system to generate heading control commands. If the control commands deviate too much from the current flight status, the control parameters are recalibrated.

[0136] Specifically, the optimized heading angle estimate and periodic compensation result are obtained and input into the flight control system to generate heading control commands. A preset threshold is used to compare the deviation between the control commands and the current flight state to determine if the deviation exceeds the preset threshold. If the deviation exceeds the preset threshold, the control parameters are recalculated based on the deviation, and the control parameter table is updated. The updated control parameters are used to regenerate the heading control commands and input into the flight control system. The deviation between the updated heading control commands and the current flight state is obtained and determined if the deviation is within a preset threshold. If the deviation is within the preset threshold, the current heading control command is determined as the final control command. The final control command is used to execute flight control, completing the heading control process.

[0137] For example, the optimized heading angle estimate is further processed using a Kalman filter algorithm. Assuming the current heading angle estimate is 125 degrees, the filtered result is 123 degrees, the filter gain is 95, and the state covariance matrix is ​​[1, 0; 0, 1]. Subsequently, the periodic compensation result is analyzed. A Fourier transform algorithm is used to extract a compensation signal with a period of 10 seconds, an amplitude of 2 degrees, and a phase of 30 degrees. The processed heading angle estimate and compensation signal are input to the flight control system. The system generates a heading control command based on a preset PID control algorithm, where the proportional coefficient is 2, the integral time is 5 seconds, the derivative time is 5 seconds, and the generated heading control command is 120 degrees. The system monitors the current flight status in real time. Assuming the current actual heading angle is 128 degrees, the deviation between the control command and the current flight status is calculated to be 2 degrees. If the deviation exceeds a preset threshold of 5 degrees, a control parameter recalibration process is triggered. The PID parameters are optimized using the least squares method, resulting in a proportional gain of 15, an integral time of 2 seconds, and a derivative time of 48 seconds. This ensures that the deviation between the control command and the flight status remains within the allowable range. The entire process is automated through an embedded system, ensuring the accuracy and stability of flight control.

[0138] S108 monitors the operating status of the heading control system in real time. If abnormal jitter or deviation is detected, it triggers the data fusion and disturbance observer re-optimization process to ensure the smoothness and stability of the heading channel.

[0139] Specifically, sensors acquire real-time operational data of the heading control system, and the heading control parameters within this data are analyzed. Based on preset thresholds, the system checks for abnormal fluctuations or deviations in the heading control parameters; if an anomaly is detected, a trigger bar is marked. For the marked trigger bars, multi-source sensor data is extracted from the data fusion module and fused. The fused data is analyzed by the disturbance observation module to identify the source of disturbances and their impact on the heading control. Based on the disturbance analysis results, an optimization algorithm is used to readjust the parameters of the disturbance observation module. The optimized disturbance observation module is integrated with the heading control system to generate new control commands and adjust the channel state. The real-time monitoring module continuously tracks the adjusted heading control system to confirm whether the smoothness and stability meet expectations.

[0140] The technical solutions described in the embodiments of this application above have at least the following technical effects or advantages:

[0141] By fusing and filtering data from the Instrument Landing System (ILS), magnetic heading sensor, and fiber optic gyroscope, a more accurate heading angle estimation is achieved. A dynamic threshold detection mechanism and eddy current disturbance observer are introduced to effectively identify and compensate for abnormal heading changes. An adaptive control algorithm is employed to adjust periodic pitch compensation, counteracting heading deflection caused by ground effects. This invention significantly improves the accuracy and stability of heading control through multi-sensor data fusion, dynamic threshold detection, eddy current disturbance modeling, and adaptive control technologies. It is particularly suitable for flight control at night and in complex terrain conditions, providing a strong guarantee for enhancing flight safety.

[0142] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A helicopter-based automatic approach control method, characterized in that, include: S101: Acquire the heading signals from the instrument landing system and magnetic heading sensor, extract the high-frequency jitter components in the signals, and use a low-pass filter to smooth the signals and remove high-frequency interference; S102, the processed heading signal and the data from the fiber optic gyroscope are time-aligned, an angle fusion Kalman filter is constructed, and the fusion result of the sensor data is optimized by weight allocation to obtain the heading angle estimate; S103 introduces a dynamic threshold detection mechanism to address heading angle jitter caused by electromagnetic interference at night. If the heading angle change exceeds a preset threshold, the sensor data is recalibrated to eliminate the impact of outliers on the fusion results. S104. Based on the interaction model between runway topography and rotor wake, analyze the dynamic characteristics of vortex disturbance, construct a mathematical expression for the propagation law of vortex disturbance, and determine the changing trends of disturbance intensity and direction. S105 introduces an eddy current angle disturbance observer into the heading control law to estimate the impact of eddy current disturbance on heading control in real time. If the disturbance intensity exceeds the preset range, a periodic pitch compensation mechanism is triggered. S106 employs an adaptive control algorithm to adjust the amplitude and frequency of periodic pitch compensation. If crosswind conditions change significantly, the compensation parameters are dynamically updated to counteract the heading deflection moment caused by the ground effect. S107: The optimized heading angle estimate and periodic compensation result are input into the flight control system to generate heading control commands. If the control commands deviate too much from the current flight status, the control parameters are recalibrated. S108, by monitoring the operating status of the heading control system in real time, triggers the data fusion and disturbance observer re-optimization process if abnormal jitter or deviation is detected.

2. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S101 specifically includes: The heading signal is obtained based on the landing system and the magnetic heading sensor; High-frequency jitter components are extracted from the heading signal to determine the characteristic frequency range of the high-frequency jitter; The filter is configured using preset low-pass filter parameters to filter high-frequency jitter components. Based on the filtered signal, determine whether the high-frequency interference has been completely removed. If not, adjust the filtering parameters. The filtered signal is smoothed using a smoothing algorithm to obtain a smooth signal free of high-frequency interference. Based on the characteristics of the smoothed signal, determine whether the signal meets the preset smoothness threshold. If it does not meet the threshold, readjust the smoothing processing parameters. The output is a smooth heading signal with high-frequency interference removed, completing the signal processing flow.

3. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S102 specifically includes: The sampling timestamps of the heading signal and fiber optic gyroscope data are obtained, and the two data sources are time-aligned using an interpolation algorithm to obtain synchronized time series data. Construct an angle fusion Kalman filter, set the state transition matrix and observation matrix, and initialize the filter parameters, including process noise covariance and observation noise covariance; The heading angle information is extracted from the time-aligned data and used as the observation input for the Kalman filter. At the same time, the angular velocity information of the fiber optic gyroscope is obtained and used as the state prediction input. The Kalman filter algorithm is used for iterative calculation to obtain the optimal estimate of the heading angle, and the filter state and error covariance matrix are updated. Based on the sensor accuracy indicators, the weighting coefficients of the heading signal and fiber optic gyroscope data are calculated to determine the data fusion ratio; through a weighted fusion algorithm, combining the Kalman filter output and the original sensor data, the optimized heading angle estimate is obtained. If there is a large deviation between the heading signal and the fiber optic gyroscope data, the weight allocation coefficients are adjusted and the fusion result is recalculated until the deviation is less than the preset threshold.

4. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S103 specifically includes: Acquire the heading angle data stream, calculate the heading angle jitter value, and obtain the change; Based on the dynamic threshold detection mechanism, it is determined whether the change exceeds the preset threshold; If the change exceeds the preset threshold, acquire the sensor data stream and recalculate the calibration value; if it does not exceed the preset threshold, no further operation is required. Eliminate outliers and retain the calibrated sensor data stream; The calibrated sensor data stream is fused with the fusion result; Analyze the impact of interference sources on the heading angle data stream and extract interference features; Based on the interference characteristics, the dynamic threshold detection mechanism is updated, and the preset threshold is updated.

5. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S104 specifically includes: For the eddy current model, the frequency characteristics of the disturbance wave are extracted by Fourier transform to determine the main propagation direction of the eddy current disturbance; based on the propagation direction characteristics of the disturbance wave, a mathematical formula for the propagation of the eddy current disturbance is constructed using partial differential equations to obtain the spatial distribution of the disturbance intensity value. Based on the mathematical calculation results, the trend of intensity value change is analyzed through gradient operation to determine the increase or decrease pattern of disturbance intensity at different locations; Based on the intensity value distribution characteristics, the vector analysis method is used to calculate the change characteristics of the direction angle and determine the main direction change law of the eddy current disturbance; Based on the characteristics of directional angle variation, a dynamic model of eddy current disturbance is constructed through statistical analysis to obtain the spatiotemporal evolution law of the disturbance field; For the dynamic model, a machine learning algorithm is used to fit the relationship between the force and the law of change, and to predict the evolution trend of eddy current disturbance.

6. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S105 specifically includes: Acquire real-time heading data from the heading control system and, in conjunction with a pre-set heading control model, determine the heading deviation value; By using an eddy angle disturbance observer, the eddy disturbance angle is extracted from the heading deviation value to obtain the eddy disturbance intensity value; Based on the preset disturbance intensity range, determine whether the eddy current disturbance intensity value exceeds the preset range; If the eddy current disturbance intensity value exceeds the preset range, obtain the execution parameters of the periodic pitch compensation mechanism; if it does not exceed the preset range, no further operation is required. A periodic pitch compensation mechanism is used to generate pitch compensation signals to adjust the dynamic response of the heading control system. Based on the pitch compensation signal, update the output of the heading control model to obtain the corrected heading data; Using the corrected heading data, the heading deviation value is recalculated to complete the closed-loop adjustment of the heading control system.

7. The helicopter-based automatic approach control method as described in claim 1, characterized in that, Specifically, S106 includes: Acquire real-time crosswind data of the aircraft and determine whether the crosswind conditions exceed a preset threshold. If the crosswind conditions exceed the threshold, the adaptive control algorithm is used to calculate the amplitude and frequency of periodic pitch compensation; if the crosswind conditions do not exceed the preset threshold, no further operation is required. Update the compensation parameters based on the calculation results and generate new control commands; Acquire the aircraft's heading deflection data to determine if there is a torque caused by the ground effect; If a ground effect torque exists, an adaptive control algorithm is used to adjust the compensation parameters; if no ground effect torque exists, no further action is required. The adjusted compensation parameters are applied to the periodic pitch control system; The yaw moment is counteracted by performing compensation actions through a periodic pitch control system.

8. The helicopter-based automatic approach control method as described in claim 1, characterized in that, S107 specifically involves: obtaining the optimized heading angle estimate and periodic compensation result, and inputting them into the flight control system to generate heading control commands; The deviation between the control command and the current flight status is compared using a preset threshold to determine whether the deviation exceeds the preset threshold. If the deviation value exceeds the preset threshold, the control parameters are recalculated based on the deviation value, and the control parameter table is updated. The heading control command is regenerated using the updated control parameters and input into the flight control system. Obtain the deviation between the updated heading control command and the current flight status, and determine whether the deviation is within a preset threshold. If the deviation value is within the preset threshold, the current heading control command is determined to be the final control command; Flight control is executed using final control commands to complete the heading control process.

9. The helicopter-based automatic approach control method as described in claim 1, characterized in that, Specifically, S108 includes: Sensors are used to acquire real-time operational data of the heading control system, and the heading control parameters in the data are analyzed. The heading control parameters are checked for abnormal jitter or deviation based on a preset threshold. If an abnormality is detected, a trigger bar is marked. For the marked trigger bar, multi-source sensor data is extracted from the data fusion module and processed for data fusion. By analyzing the fused data using the disturbance observation module, the source of the disturbance and its impact on heading control can be identified. Based on the disturbance analysis results, the parameters of the disturbance observation module are readjusted using an optimization algorithm; The optimized disturbance observation module is integrated with the heading control system to generate new control commands and adjust the channel configuration. The heading control system is continuously tracked and adjusted through a real-time monitoring module.

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