A control method and system for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle

Abstract

The present invention discloses a control method and system for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle, and relates to the technical field of vibration control for unmanned aerial vehicles, including collecting multimodal data through a sensor and preprocessing it, calculating the external disturbance force according to the preprocessed data, and then constructing a BIRF dynamic characteristic model for response prediction; calculating a preliminary damping coefficient based on the prediction result, optimizing the preliminary damping coefficient to obtain an optimized compensation signal, and performing real-time early warning according to the obtained optimized compensation signal; the present invention uses the BIRF dynamic characteristic model to perform response prediction, thereby improving the dynamic modeling accuracy of the unmanned aerial vehicle in a complex multivariable environment, and adjusts the damping parameters through an optimization algorithm, thereby enhancing the real-time control capability of the electromagnetic damping shock absorbing device, and enhancing the vibration characteristic recognition and response suppression performance through deep learning and high-frequency signal optimization technology, so that the present invention can achieve the improvement of the stability of the unmanned aerial vehicle in a complex operating environment and the optimization of the shock absorption performance.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle vibration control, and in particular to a control method and system of an electromagnetic damping shock absorbing device for unmanned aerial vehicle. Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in logistics, environmental monitoring, rescue, agriculture, and military fields. However, UAVs are inevitably affected by external environmental factors during actual operation, such as vibration, wind speed, humidity, and temperature changes. These factors significantly interfere with the stability and control accuracy of UAVs, thereby affecting their flight performance and mission execution. In recent years, electromagnetic damping technology has gradually become an important research direction in the field of shock absorption due to its high efficiency, fast response, and precise control. Electromagnetic damping technology provides damping force through the interaction between electromagnetic coils and magnetic fields, and has significant advantages in dealing with vibration problems.

[0003] However, most existing traditional electromagnetic damping technologies can only handle single-mode vibrations and have poor adaptability to complex, multi-variable disturbance force environments. Especially in high-speed flight or drastic environmental changes, it is often difficult to achieve vibration suppression in real time and accurately. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method and system for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle to solve the problem that most of the existing traditional electromagnetic damping technologies can only handle single-mode vibrations and have poor adaptability to complex, multi-variable disturbance force environments, especially in high-speed flight or drastic environmental changes, and it is often difficult to achieve vibration suppression in real time and accurately.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle, comprising:

[0008] Multimodal data is collected by sensors and preprocessed. The external disturbance force is calculated based on the preprocessed data, and then the BIRF dynamic characteristic model is constructed to predict the response.

[0009] Calculate the preliminary damping coefficient based on the prediction result, optimize the preliminary damping coefficient to obtain the optimized compensation signal, and issue a real-time warning based on the obtained optimized compensation signal;

[0010] The optimized compensation signal is stored in a database and displayed through a visual interface.

[0011] As a preferred solution of the electromagnetic damping shock absorbing device control method for unmanned aerial vehicles of the present invention, wherein: the multi-modal data collected by the sensor and pre-processed includes:

[0012] Vibration data, magnetic field data, temperature and humidity data, and wind speed data are collected by installing acceleration, magnetic field, temperature and humidity data, and wind speed sensors on the drone;

[0013] After the collected data are time-aligned, the time-aligned data are normalized and denoised, and the preprocessed data are integrated into a data set.

[0014] As a preferred solution of the electromagnetic damping shock absorbing device control method for unmanned aerial vehicles of the present invention, wherein: the calculation of the external disturbance force based on the preprocessed data and then constructing the BIRF dynamic characteristic model for response prediction includes:

[0015] The actual response is extracted from the denoised vibration data using time domain analysis methods;

[0016] Calculate the external disturbance force of the drone based on the denoised temperature and humidity data combined with wind speed ;

[0017] Use independent component separation technology to extract independent components of vibration signals and independent components of magnetic field signals from the denoised vibration data and magnetic field data respectively;

[0018] Performing normalization operations on the independent components of the vibration signal and the independent components of the magnetic field signal by a normalization method to obtain normalized independent components of the vibration signal and the independent components of the magnetic field signal;

[0019] Based on the normalized independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, the superposition function of the input signal is defined:

[0020] ,

[0021] In the formula, represents the superposition function of the input signal, , , They represent the weight coefficients of the independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, , , They represent the independent components of vibration signal, the independent components of magnetic field signal and the external disturbance force of the UAV respectively;

[0022] The BIRF dynamic characteristic model is constructed by superimposing the function and the initial BIRF coefficients:

[0023] ,

[0024] In the formula, represents the predicted response at time t, represents the BIRF coefficient at the nth time step, represents the superposition function of the input signal, and N represents the total number of time steps;

[0025] Define the objective function and minimize the error between the predicted response and the actual response:

[0026] ,

[0027] In the formula, G represents the objective function, represents the actual response extracted, and N represents the total number of time steps;

[0028] The initial condition is set to zero, and the BIRF dynamic characteristic model parameters are iteratively optimized by the gradient descent method. In each time step, the BIRF coefficients are fitted by the least squares method, and the cumulative error is calculated by the error propagation method at each time step to guide the gradient optimization. When the objective function of the model no longer decreases significantly during the continuous iteration process, the iteration is stopped, and the final dynamic characteristic modeling is output to complete the optimization.

[0029] The superposition function is input into the BIRF dynamic characteristic model to obtain the predicted response signal ;

[0030] Based on the obtained predicted response signal minus the extracted actual response signal, a response deviation value S is obtained;

[0031] Set the judgment threshold to D, and compare the response deviation value with the threshold D. When the response deviation value is greater than the threshold D, it indicates that there is abnormal vibration in the operation state of the drone. When the response deviation value is less than or equal to the threshold D, it indicates that the operation state of the drone is normal.

[0032] As a preferred solution of the electromagnetic damping shock absorbing device control method for a drone of the present invention, wherein: the calculation of the preliminary damping coefficient based on the prediction result and the optimization of the preliminary damping coefficient to obtain the optimized compensation signal include:

[0033] Construct the fractional-order chaotic recursive equation based on the predicted response signal:

[0034] ,

[0035] In the formula, represents the chaotic signal value related to the predicted response signal, s represents the amplification factor, represents the time-dependent sinusoidal modulation frequency, T represents the time delay, b represents the nonlinear parameter, represents the chaotic signal at the previous moment, represents the offset, c represents the recursive weight coefficient, represents the predicted response;

[0036] Based on the chaotic signal value, a discrete chaotic signal sequence is generated by using a numerical iteration method, and the discrete value of the chaotic signal at the previous discrete moment is sampled from the chaotic signal sequence by a time step sampling method;

[0037] Based on the discrete value of the chaotic signal at the previous moment of discretization, the Grünwald-Letnikov discretization method is used to calculate the fractional derivative value of the chaotic signal at the next moment:

[0038] ,

[0039] In the formula, represents the fractional derivative value of the chaotic signal at the next moment, h represents the discretization step size, q represents the order of the fractional derivative, z represents the upper limit of the discretization summation, k represents the current discretization step number, Indicates that the chaotic signal is discrete before the moment The discrete value of represents the combination coefficient;

[0040] The obtained fractional derivative values ​​and predicted response signals are standardized using a standardization method, and a preliminary enhanced signal is obtained using a linear weighting method;

[0041] Using the sliding window technique, the obtained preliminary enhanced signal is subjected to sliding averaging to obtain a smooth optimized signal;

[0042] According to the obtained smooth optimized signal, the amplitude is dynamically amplified and adjusted to obtain a signal after the amplitude is dynamically adjusted ;

[0043] Based on the signal after the amplitude is dynamically adjusted, the time derivative enhancement method is used to enhance the transient characteristics to obtain the transient enhanced signal ;

[0044] The time characteristics of the transient enhancement signal are enhanced using the improved linear enhancement formula to obtain a time characteristic enhanced signal:

[0045] ,

[0046] In the formula, represents the time characteristic enhancement signal, represents the transient enhancement signal, represents the time characteristic enhancement coefficient, represents the first-order time derivative;

[0047] The time characteristic enhanced signal is converted into a frequency domain signal by using fast Fourier transform, and the high frequency of the converted frequency domain signal is enhanced by using frequency domain weighting;

[0048] The frequency domain signal after high-frequency enhancement is reconstructed by inverse Fourier transform to obtain the final time domain enhanced signal;

[0049] The final time-domain enhanced signal is normalized using a normalization method, and the normalized signal is segmented using a sliding window technique to generate training samples;

[0050] Build a prediction model based on the convolutional neural network model architecture, including input layer, convolution layer, pooling layer, fully connected layer and output layer;

[0051] Input the training samples into the prediction model, and calculate the gradient of the loss function for each weight through the back propagation algorithm. Use the gradient descent method to update the weights and model parameters. Update the model parameters in each iteration to minimize the loss function. Calculate the loss function value after each iteration. When the loss function no longer decreases significantly, stop training and output the final optimized model.

[0052] The obtained abnormal operation data is input into the convolutional neural network model to obtain the predicted damping coefficient;

[0053] The actual damping coefficient is extracted from the denoised vibration data by modal analysis;

[0054] Subtracting the predicted damping coefficient from the actual damping coefficient to obtain a damping error;

[0055] The damping error is calculated using the PID control algorithm, and the control signal is converted into current after being generated;

[0056] Based on the obtained current, an opposite vibration compensation signal is generated ;

[0057] The obtained compensation signal is optimized using the H∞ control method to obtain the optimized compensation signal .

[0058] As a preferred solution of the electromagnetic damping shock absorbing device control method for unmanned aerial vehicles of the present invention, the real-time early warning according to the obtained optimized compensation signal includes:

[0059] Based on the extracted actual response signal, the mass conversion of the actual response signal is performed using Newton's second law method;

[0060] The sliding window technique is used to obtain the actual response signal of the previous time step from the actual response signal of the conversion. ;

[0061] Based on the actual response signal converted in the previous time step combined with the optimized compensation signal, the response change rate is calculated:

[0062] ,

[0063] Where L represents the response change rate, represents the actual response signal converted at time step t, represents the adjustment coefficient, represents the time step;

[0064] Based on the response change rate, the response value at the next time step is predicted using the linear extrapolation method ;

[0065] Using the response value at the next time step , calculate the prediction deviation value:

[0066] ,

[0067] In the formula, represents the predicted deviation value of the next time step, represents the theoretical response value;

[0068] Set the warning threshold to B, compare the predicted deviation value with threshold B, when the predicted deviation value is greater than or equal to threshold B, trigger the warning signal, and recalculate the optimization compensation signal, when the predicted deviation value is less than threshold B, record the operation data.

[0069] As a preferred solution of the control method of the electromagnetic damping shock absorbing device for unmanned aerial vehicles described in the present invention, the use of a database to store the optimized compensation signal refers to using a database to store the obtained optimized compensation signal and the recorded operation data, and formatting them during the storage process, generating a unique ID for each calculation record, and supporting the export of optimized compensation signal data according to specific query conditions by setting an API interface.

[0070] As a preferred solution of the control method of the electromagnetic damping shock absorbing device for unmanned aerial vehicles described in the present invention, the display through a visual interface refers to extracting the stored optimization compensation signal data from a database, and using a Python tool to generate a line graph, in which the X-axis in the line graph represents time and damping error parameters, and the Y-axis represents the optimization compensation signal.

[0071] In a second aspect, the present invention provides an electromagnetic damping shock absorbing device control system for an unmanned aerial vehicle, comprising:

[0072] The acquisition and processing module is used to acquire multimodal data and perform preprocessing;

[0073] The calculation and output module is used to calculate the external disturbance force, build the BIRF dynamic characteristic model and output the predicted response;

[0074] The optimization and early warning module is used to calculate the initial damping coefficient and then optimize it, and to issue a real-time early warning based on the optimization compensation signal;

[0075] The storage and display module is used to store data and display it through a visual interface.

[0076] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the control method of the electromagnetic damping shock absorbing device for an unmanned aerial vehicle as described in the first aspect of the present invention is implemented.

[0077] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the electromagnetic damping and shock absorbing device control method for an unmanned aerial vehicle as described in the first aspect of the present invention is implemented.

[0078] The beneficial effects of the present invention are as follows: the present invention utilizes the BIRF dynamic characteristic model for response prediction, which significantly improves the dynamic modeling accuracy of the UAV in a complex multivariable environment, and adaptively adjusts the damping parameters through an optimization algorithm, thereby enhancing the real-time control capability of the electromagnetic damping shock absorption device, and effectively enhances the vibration characteristic recognition and response suppression performance through deep learning and high-frequency signal optimization technology, so that the present invention achieves the improvement of the stability of the UAV in a complex operating environment and the optimization of the shock absorption performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0080] Figure 1 The figure is a flow chart of the control method of the electromagnetic damping shock absorbing device used for the UAV in the present invention.

[0081] Figure 2 The figure is a schematic diagram of the structure of the control system of the electromagnetic damping shock absorbing device used for the UAV in the present invention. DETAILED DESCRIPTION

[0082] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0083] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0084] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0085] Example, see Figure 1 and Figure 2 , this embodiment provides a control method for an electromagnetic damping shock absorbing device for a drone, comprising the following steps:

[0086] S1, collect multimodal data through sensors and preprocess them, calculate the external disturbance force based on the preprocessed data, and then build a BIRF dynamic characteristic model for response prediction;

[0087] Specifically, the multimodal data is collected by sensors and preprocessed, including:

[0088] Vibration data, magnetic field data, temperature and humidity data, and wind speed data are collected by installing acceleration, magnetic field, temperature and humidity data, and wind speed sensors on the drone;

[0089] After the collected data are time-aligned, the time-aligned data are normalized and denoised, and the preprocessed data are integrated into a data set.

[0090] By integrating multimodal data such as acceleration, magnetic field, temperature and humidity, and wind speed, it provides comprehensive environmental perception capabilities, and uses time alignment, normalization, and denoising techniques to ensure data consistency and high signal-to-noise ratio, providing high-quality input for subsequent modeling and control. The integration of multimodal data sets further enhances the modeling accuracy of external disturbance forces and improves the system's adaptability and dynamic response capabilities to complex and changing environments.

[0091] Furthermore, the BIRF dynamic characteristic model is constructed based on the preprocessed data to calculate the external disturbance force and perform response prediction, including:

[0092] The actual response is extracted from the denoised vibration data using time domain analysis methods;

[0093] Based on the denoised temperature and humidity data combined with wind speed, the external disturbance force of the drone is calculated:

[0094] ,

[0095] In the formula, represents the external disturbance force, represents the air density, Indicates wind speed, represents the windward area;

[0096] The frontal area A can be determined based on the geometric dimensions of the UAV;

[0097] Independent component separation technology is used to extract independent components of vibration signals and independent components of magnetic field signals from the denoised vibration data and magnetic field data respectively;

[0098] Independent components of vibration signals, for example, rotational vibration, impact vibration;

[0099] Independent components of magnetic field signals, for example, the earth's magnetic field and the equipment's magnetic field;

[0100] Performing normalization operations on the independent components of the vibration signal and the independent components of the magnetic field signal by a normalization method to obtain normalized independent components of the vibration signal and the independent components of the magnetic field signal;

[0101] Based on the normalized independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, the superposition function of the input signal is defined:

[0102] ,

[0103] In the formula, represents the superposition function of the input signal, , , They represent the weight coefficients of the independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, , , They represent the independent components of vibration signal, the independent components of magnetic field signal and the external disturbance force of the UAV respectively;

[0104] The weight coefficients of the independent components of the vibration signal, the independent components of the magnetic field signal, and the external disturbance force of the drone can be set based on the experience and knowledge of field experts , , ;

[0105] The BIRF dynamic characteristics model is constructed by using the superposition function of the input signal and the initial BIRF coefficients:

[0106] ,

[0107] In the formula, represents the predicted response at time t, represents the BIRF coefficient at the nth time step, represents the superposition function of the input signal, and N represents the total number of time steps;

[0108] Define the objective function and minimize the error between the predicted response and the actual response:

[0109] ,

[0110] In the formula, G represents the objective function, represents the actual response extracted, and N represents the total number of time steps;

[0111] The initial condition is set to zero, and the BIRF dynamic characteristic model parameters are iteratively optimized by the gradient descent method. In each time step, the BIRF coefficients are fitted by the least squares method, and the cumulative error is calculated by the error propagation method at each time step to guide the gradient optimization. When the objective function of the model no longer decreases significantly during the continuous iteration process, the iteration is stopped, and the final dynamic characteristic modeling is output to complete the optimization.

[0112] The superposition function is input into the BIRF dynamic characteristic model to obtain the predicted response signal ;

[0113] The predicted response signal refers to a signal related to vibration acceleration predicted by the drone under the action of external disturbance force, such as a transient response signal of vibration, a periodic signal of vibration, and a random disturbance component signal in vibration;

[0114] Based on the obtained predicted response signal minus the extracted actual response signal, the response deviation value is obtained:

[0115] ,

[0116] In the formula, represents the response deviation value, represents the predicted response, represents the actual response extracted;

[0117] According to the statistical analysis method and experimental verification, the judgment threshold is set to D, and the response deviation value is compared with the threshold D. When the response deviation value is greater than the threshold D, it means that there is abnormal vibration in the operation state of the UAV. When the response deviation value is less than or equal to the threshold D, it means that the operation state of the UAV is normal.

[0118] Through innovative multimodal data fusion and dynamic modeling technology, the performance and reliability of the UAV vibration control system are significantly improved. The invention introduces external disturbance force calculation and accurately models the external disturbance force based on multimodal data, providing high-quality input for vibration response prediction and solving the problem of inaccurate description of external disturbance force in the prior art. Secondly, the independent component separation technology is used to extract key independent signal components from vibration and magnetic field data, remove data redundancy and interference noise, and combine normalization processing to improve signal comparability and modeling accuracy. Based on the optimized signal and external disturbance force, a BIRF dynamic characteristic model is constructed to achieve accurate prediction of UAV vibration response. The model can capture transient response, periodic signal and random disturbance characteristics. The dynamic adaptability to complex nonlinear environments is improved through the superposition function of the input signal and the optimized BIRF coefficient. The iterative optimization of the model adopts the gradient descent method and the error propagation method to effectively minimize the error between the predicted response and the actual response, further improving the real-time and stability of the prediction. In addition, by calculating the response deviation value and comparing it with the set threshold, the present invention realizes the efficient detection and real-time monitoring of UAV vibration anomalies. When the response deviation exceeds the threshold, the operation anomaly can be quickly identified and an adjustment basis can be provided, which significantly enhances the robustness of the UAV system and the safety of mission execution. Compared with the prior art, the present invention not only improves the accuracy and efficiency of dynamic response prediction, but also provides more comprehensive and reliable technical support for UAV vibration control in complex environments.

[0119] S2. Calculate a preliminary damping coefficient based on the prediction result, optimize the preliminary damping coefficient to obtain an optimized compensation signal, and issue a real-time warning based on the obtained optimized compensation signal;

[0120] Specifically, the preliminary damping coefficient is calculated based on the prediction result, and the preliminary damping coefficient is optimized to obtain the optimized compensation signal, including:

[0121] Construct the fractional-order chaotic recursive equation based on the predicted response signal:

[0122] ,

[0123] In the formula, represents the chaotic signal value related to the predicted response signal, s represents the amplification factor, represents the time-dependent sinusoidal modulation frequency, T represents the time delay, b represents the nonlinear parameter, represents the chaotic signal at the previous moment, represents the offset, c represents the recursive weight coefficient, represents the predicted response;

[0124] The amplification factor s is adjusted according to the normalization of the maximum amplitude of the signal. The time-related sinusoidal modulation frequency p is determined according to the main frequency component of the input signal. The time delay T is obtained from the period or vibration characteristics of the signal. The nonlinear parameter b can be determined by the amplitude change rate of the signal. The offset can be determined by taking 1% of the signal amplitude. , set the recursive weight coefficient c through experience;

[0125] Based on the chaotic signal value, a discrete chaotic signal sequence is generated by using a numerical iteration method, and the discrete value of the chaotic signal at the previous discrete moment is sampled from the chaotic signal sequence by a time step sampling method;

[0126] Based on the discrete value of the chaotic signal at the previous moment of discretization, the Grünwald-Letnikov discretization method is used to calculate the fractional derivative value of the chaotic signal at the next moment:

[0127] ,

[0128] In the formula, represents the fractional derivative value of the chaotic signal at the next moment, h represents the discretization step size, q represents the order of the fractional derivative, z represents the upper limit of the discretization summation, k represents the current discretization step number, Indicates that the chaotic signal is discrete before the moment The discrete value of represents the combination coefficient;

[0129] The discretization step size h is calculated according to the sampling frequency of the signal, and the order q of the fractional derivative is set according to the vibration characteristics of the signal. The combination coefficient can be directly calculated based on the gamma function. ;

[0130] The obtained fractional derivative values ​​and predicted response signals are standardized using a standardization method, and a preliminary enhanced signal is obtained using a linear weighting method;

[0131] Using the sliding window technique, the obtained preliminary enhanced signal is subjected to sliding averaging to obtain a smooth optimized signal;

[0132] The amplitude is dynamically amplified and adjusted according to the obtained smooth optimized signal to obtain a signal with dynamically adjusted amplitude:

[0133] ,

[0134] In the formula, Represents the signal after the amplitude is dynamically adjusted, represents the smooth optimization signal, represents the dynamic disturbance factor, w represents the frequency of dynamic adjustment, and t represents time;

[0135] Select dynamic perturbation factor based on enhancement effect , obtain the dynamically adjusted frequency w from the main frequency component of the signal;

[0136] Based on the signal after the amplitude is dynamically adjusted, the time derivative enhancement method is used to enhance the transient characteristics to obtain the transient enhanced signal ;

[0137] ,

[0138] In the formula, Indicates the first-order derivative operation on time t;

[0139] Transient characteristics such as instantaneous amplitude, instantaneous frequency, instantaneous phase;

[0140] The time characteristics of the transient enhancement signal are enhanced using the improved linear enhancement formula to obtain a time characteristic enhanced signal:

[0141] ,

[0142] In the formula, represents the time characteristic enhancement signal, represents the transient enhancement signal, represents the time characteristic enhancement coefficient, represents the first-order time derivative;

[0143] The improvement process is as follows:

[0144] Optimize the smoothing signal Calculate the time derivative to obtain its first-order time rate of change:

[0145] ,

[0146] In the formula, represents the smooth optimization signal, Indicates that the time interval tends to infinitesimal. Represents a smooth optimization signal At the point in time The value of represents the difference of the smoothed optimized signal, represents the first-order time rate of change;

[0147] And introduce a time characteristic enhancement coefficient , as a tuning parameter, is used to control the weight of the first-order derivative enhancement term, while the time characteristic enhancement coefficient Can be tuned through experiments;

[0148] Use the linear enhancement formula to enhance the transient signal Combined with the first-order time derivative enhancement An improved linear enhancement formula is obtained;

[0149] In the prior art, only the signal itself is processed in the time domain, ignoring the importance of the rate of change (derivative) of the signal. The first time derivative of the sum signal , which strengthens the focus on the dynamic characteristics of the signal, so as to more accurately characterize the characteristic changes of the signal in the time dimension, and the improved formula is more effective in transient enhancement of the signal. Based on this, an enhancement term related to the first-order derivative of the signal is added. , which makes the processed signal It can respond more sensitively to the instantaneous changes of dynamic signals. By adding derivative enhancement items, the improved signal can more accurately reflect the changing characteristics of the signal over time, especially showing higher responsiveness when dealing with sudden changes or non-stationary signals;

[0150] The time characteristic enhanced signal is converted into a frequency domain signal using fast Fourier transform, and the high frequency of the converted frequency domain signal is enhanced using frequency domain weighting:

[0151] ,

[0152] In the formula, represents the signal after high frequency enhancement, represents the transformed frequency domain signal, represents the high frequency enhancement factor, f represents the frequency, represents the cut-off frequency;

[0153] Adjust the high frequency enhancement factor by experience , set the cutoff frequency according to the main frequency component of the signal , usually 1 / 2 of the main frequency;

[0154] The frequency domain signal after high-frequency enhancement is reconstructed by inverse Fourier transform to obtain the final time domain enhanced signal;

[0155] The final time-domain enhanced signal is normalized using a normalization method, and the normalized signal is segmented using a sliding window technique to generate training samples;

[0156] Build a prediction model based on the convolutional neural network model architecture, including input layer, convolution layer, pooling layer, fully connected layer and output layer;

[0157] Input the training samples into the prediction model, and calculate the gradient of the loss function for each weight through the back propagation algorithm. Use the gradient descent method to update the weights and model parameters. Update the model parameters in each iteration to minimize the loss function. Calculate the loss function value after each iteration. When the loss function no longer decreases significantly, stop training and output the final optimized model.

[0158] The obtained abnormal operation data is input into the convolutional neural network model to obtain the predicted damping coefficient;

[0159] The actual damping coefficient is extracted from the denoised vibration data by modal analysis;

[0160] Subtracting the predicted damping coefficient from the actual damping coefficient to obtain a damping error;

[0161] The damping error is calculated using the PID control algorithm, and the control signal is converted into current:

[0162] ,

[0163] ,

[0164] In the formula, Indicates the PID control signal, , , Respectively represent the proportional control parameter, integral control parameter, and differential control parameter, represents the damping error, represents the integral variable, Represents the current signal, represents the control coefficient, represents the electromagnetic constant;

[0165] Based on the obtained current, an opposite vibration compensation signal is generated :

[0166] ,

[0167] In the formula, Indicates the negative value of the electromagnetic constant;

[0168] The obtained compensation signal is optimized using the H∞ control method to obtain the optimized compensation signal :

[0169] ,

[0170] In the formula, represents the optimized compensation signal, Indicates the input signal The transfer operation, represents the H∞ norm, Represents the optimization variable, which is set empirically.

[0171] Through fractional-order chaotic recursive equations and fractional-order derivative methods, the ability to describe the dynamic characteristics of complex vibration signals is enhanced, and nonlinear and long memory characteristics are effectively captured, laying the foundation for accurate modeling. Secondly, the sliding window technology and transient enhancement method are used to smooth and dynamically optimize the signal, thereby improving the accuracy of signal time characteristic analysis. At the same time, the frequency domain high-frequency enhancement technology is combined to enhance the frequency band performance of the signal, providing support for the multi-dimensional optimization of the vibration signal. The present invention uses a convolutional neural network to predict the damping coefficient, extracts the actual damping coefficient through modal analysis, and calculates the damping error to generate a compensation signal. The compensation signal is optimized in combination with the H∞ control method, which significantly improves the effectiveness of vibration suppression and the system's adaptability to complex environments. The PID control algorithm is used to generate accurate control signals and convert them into current signals to achieve dynamic adjustment of vibration compensation signals, ensuring the real-time and response speed of the system. The present invention has made breakthroughs in modeling accuracy, signal optimization capability, control accuracy and environmental adaptability, providing comprehensive and reliable technical support for vibration control of drones in complex environments.

[0172] Furthermore, the real-time early warning is performed according to the obtained optimized compensation signal, including:

[0173] Based on the extracted actual response signal, the mass conversion of the actual response signal is performed using Newton's second law:

[0174] ,

[0175] In the formula, represents the actual response signal converted at time step t, represents the inertia coefficient, represents the actual response extracted, represents the time step;

[0176] The coefficient of inertia can be determined by weighing the drone ;

[0177] The sliding window technique is used to obtain the actual response signal of the previous time step from the actual response signal of the conversion. ;

[0178] Based on the actual response signal converted in the previous time step combined with the optimized compensation signal, the response change rate is calculated:

[0179] ,

[0180] Where L represents the response change rate, represents the actual response signal converted at time step t, represents the adjustment coefficient, represents the time step;

[0181] Based on the response change rate, the response value at the next time step is predicted using the linear extrapolation method :

[0182] ,

[0183] In the formula, represents the correction factor;

[0184] The adjustment coefficient can be set by empirical rules and correction factor ;

[0185] Using the response value at the next time step , calculate the prediction deviation value:

[0186] ,

[0187] In the formula, represents the predicted deviation value of the next time step, represents the theoretical response value,

[0188] Preset through experiment;

[0189] According to the experimental data, the warning threshold is set to B, and the predicted deviation value is compared with the threshold B. When the predicted deviation value is greater than or equal to the threshold B, the warning signal is triggered and the optimization compensation signal is recalculated. When the predicted deviation value is less than the threshold B, the operation data is recorded.

[0190] The dynamic changes of the vibration signal are reflected in real time through the calculation of the response change rate, providing a high-precision basis for response prediction. Secondly, the linear extrapolation method realizes the accurate prediction of the response value of the next time step, enhancing the system's sensitivity to short-term dynamic changes. By calculating the predicted deviation value and comparing it with the set warning threshold, the system can accurately judge the normality or abnormality of the UAV's operating status, reducing the possibility of false alarms and missed alarms. After the warning signal is triggered, the system automatically recalculates the optimized compensation signal and dynamically adjusts the control parameters to ensure the real-time and stability of the UAV vibration control. At the same time, when the warning is not triggered, the operation data is recorded for model optimization to enhance the long-term adaptability of the system.

[0191] S3, using a database to store the optimized compensation signal and displaying it through a visual interface;

[0192] Specifically, using a database to store the optimized compensation signal means using the database to store the obtained optimized compensation signal and the recorded operation data, formatting them during the storage process, generating a unique ID for each calculation record, and supporting the export of optimized compensation signal data according to specific query conditions by setting an API interface.

[0193] Through efficient data storage and management mechanisms, reliable support is provided for optimizing the recording, tracking and querying of compensation signals. Formatting processing and unique ID generation ensure the structured and standardized data storage, facilitating rapid retrieval and analysis. With the help of API interfaces, users can flexibly query and export data according to their needs to meet the needs of diverse application scenarios.

[0194] Furthermore, displaying through a visual interface refers to extracting the stored optimization compensation signal data from a database, and using a Python tool to generate a line graph, where the X-axis in the line graph represents time and damping error parameters, and the Y-axis represents the optimization compensation signal.

[0195] Through the visualization interface based on Python tools, the dynamic changes of the optimized compensation signal over time or damping error parameters are intuitively displayed, realizing efficient analysis and real-time monitoring of UAV vibration control data. In addition, by extracting the optimized compensation signal data from the database and generating a line graph, the changing trend of the compensation signal is clearly displayed. The X-axis represents time or damping error parameters, and the Y-axis represents the optimized compensation signal value.

[0196] This embodiment also provides an electromagnetic damping shock absorbing device control system for a drone, comprising:

[0197] The acquisition and processing module is used to acquire multimodal data and perform preprocessing;

[0198] The calculation and output module is used to calculate the external disturbance force, build the BIRF dynamic characteristic model and output the predicted response;

[0199] The optimization and early warning module is used to calculate the initial damping coefficient and then optimize it, and to issue a real-time early warning based on the optimization compensation signal;

[0200] The storage and display module is used to store data and display it through a visual interface.

[0201] This embodiment also provides a computer device, which is suitable for the control method of the electromagnetic damping and shock absorbing device of an unmanned aerial vehicle, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the control method of the electromagnetic damping and shock absorbing device for an unmanned aerial vehicle as proposed in the above embodiment.

[0202] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0203] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the control method of the electromagnetic damping shock absorption device for a drone as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0204] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control method for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle, characterized in that: include, Multimodal data is collected by sensors and preprocessed. The external disturbance force is calculated based on the preprocessed data, and then the BIRF dynamic characteristic model is constructed to predict the response. Calculate the preliminary damping coefficient based on the prediction result, optimize the preliminary damping coefficient to obtain the optimized compensation signal, and issue a real-time warning based on the obtained optimized compensation signal; The optimized compensation signal is stored in a database and displayed through a visual interface; The collecting of multimodal data by sensors and preprocessing includes: Vibration data, magnetic field data, temperature and humidity data, and wind speed data are collected by installing acceleration, magnetic field, temperature and humidity data, and wind speed sensors on the drone; After the collected data are time-aligned, the time-aligned data are normalized and denoised, and the pre-processed data are integrated into a data set; The calculation of the external disturbance force based on the preprocessed data and then constructing the BIRF dynamic characteristic model for response prediction includes: The actual response is extracted from the denoised vibration data using time domain analysis methods; Calculate the external disturbance force of the drone based on the denoised temperature and humidity data combined with wind speed ; Independent component separation technology is used to extract independent components of vibration signals and independent components of magnetic field signals from the denoised vibration data and magnetic field data respectively; Performing normalization operations on the independent components of the vibration signal and the independent components of the magnetic field signal by a normalization method to obtain normalized independent components of the vibration signal and the independent components of the magnetic field signal; Based on the normalized independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, the superposition function of the input signal is defined: ， In the formula, represents the superposition function of the input signal, , , They represent the weight coefficients of the independent components of the vibration signal, the independent components of the magnetic field signal and the external disturbance force of the drone, , , They represent the independent components of vibration signal, the independent components of magnetic field signal and the external disturbance force of the UAV respectively; The BIRF dynamic characteristic model is constructed by superimposing the function and the initial BIRF coefficients: ， In the formula, represents the predicted response at time t, represents the BIRF coefficient at the nth time step, represents the superposition function of the input signal, and N represents the total number of time steps; Define the objective function and minimize the error between the predicted response and the actual response: ， In the formula, G represents the objective function, represents the actual response extracted, and N represents the total number of time steps; The initial condition is set to zero, and the BIRF dynamic characteristic model parameters are iteratively optimized by the gradient descent method. In each time step, the BIRF coefficients are fitted by the least squares method, and the cumulative error is calculated by the error propagation method at each time step to guide the gradient optimization. When the objective function of the model no longer decreases significantly during the continuous iteration process, the iteration is stopped, and the final dynamic characteristic modeling is output to complete the optimization. The superposition function is input into the BIRF dynamic characteristic model to obtain the predicted response signal ; Based on the obtained predicted response signal minus the extracted actual response signal, a response deviation value S is obtained; Set the judgment threshold to D, and compare the response deviation value with the threshold D. When the response deviation value is greater than the threshold D, it means that there is abnormal vibration in the operation state of the drone. When the response deviation value is less than or equal to the threshold D, it means that the operation state of the drone is normal. The calculating of the preliminary damping coefficient based on the prediction result and optimizing the preliminary damping coefficient to obtain the optimized compensation signal comprises: Construct the fractional-order chaotic recursive equation based on the predicted response signal: ， In the formula, represents the chaotic signal value related to the predicted response signal, s represents the amplification factor, represents the time-dependent sinusoidal modulation frequency, T represents the time delay, b represents the nonlinear parameter, represents the chaotic signal at the previous moment, represents the offset, c represents the recursive weight coefficient, represents the predicted response; Based on the chaotic signal value, a discrete chaotic signal sequence is generated by using a numerical iteration method, and the discrete value of the chaotic signal at the previous discrete moment is sampled from the chaotic signal sequence by a time step sampling method; Based on the discrete value of the chaotic signal at the previous moment of discretization, the Grünwald-Letnikov discretization method is used to calculate the fractional derivative value of the chaotic signal at the next moment: ， In the formula, represents the fractional derivative value of the chaotic signal at the next moment, h represents the discretization step size, q represents the order of the fractional derivative, z represents the upper limit of the discretization summation, k represents the current discretization step number, Indicates that the chaotic signal is discrete before the moment The discrete value of represents the combination coefficient; The obtained fractional derivative values ​​and predicted response signals are standardized using a standardization method, and a preliminary enhanced signal is obtained using a linear weighting method; Using the sliding window technique, the obtained preliminary enhanced signal is subjected to sliding averaging to obtain a smooth optimized signal; According to the obtained smooth optimized signal, the amplitude is dynamically amplified and adjusted to obtain a signal after the amplitude is dynamically adjusted ; Based on the signal after the amplitude is dynamically adjusted, the time derivative enhancement method is used to enhance the transient characteristics to obtain the transient enhanced signal ; The time characteristics of the transient enhancement signal are enhanced using the improved linear enhancement formula to obtain a time characteristic enhanced signal: ， In the formula, represents the time characteristic enhancement signal, represents the transient enhancement signal, represents the time characteristic enhancement coefficient, represents the first-order time derivative, represents a smooth optimization signal; The time characteristic enhanced signal is converted into a frequency domain signal by using fast Fourier transform, and the high frequency of the converted frequency domain signal is enhanced by using frequency domain weighting; The frequency domain signal after high-frequency enhancement is reconstructed by inverse Fourier transform to obtain the final time domain enhanced signal; The final time-domain enhanced signal is normalized using a normalization method, and the normalized signal is segmented using a sliding window technique to generate training samples; Build a prediction model based on the convolutional neural network model architecture, including input layer, convolution layer, pooling layer, fully connected layer and output layer; Input the training samples into the prediction model, and calculate the gradient of the loss function for each weight through the back propagation algorithm. Use the gradient descent method to update the weights and model parameters. Update the model parameters in each iteration to minimize the loss function. Calculate the loss function value after each iteration. When the loss function no longer decreases significantly, stop training and output the final optimized model. The obtained abnormal operation data is input into the convolutional neural network model to obtain the predicted damping coefficient; The actual damping coefficient is extracted from the denoised vibration data by modal analysis; Subtracting the predicted damping coefficient from the actual damping coefficient to obtain a damping error; The damping error is calculated using the PID control algorithm, and the control signal is converted into current after being generated; Based on the obtained current, an opposite vibration compensation signal is generated ; The obtained compensation signal is optimized using the H∞ control method to obtain the optimized compensation signal ; The real-time early warning according to the obtained optimized compensation signal includes: Based on the extracted actual response signal, the mass conversion of the actual response signal is performed using Newton's second law method; The sliding window technique is used to obtain the actual response signal of the previous time step from the actual response signal of the conversion. ; Based on the actual response signal converted in the previous time step combined with the optimized compensation signal, the response change rate is calculated: ， Where L represents the response change rate, represents the actual response signal converted at time step t, represents the adjustment coefficient, represents the time step; Based on the response change rate, the response value at the next time step is predicted using the linear extrapolation method ; Using the response value at the next time step , calculate the prediction deviation value: ， In the formula, represents the predicted deviation value of the next time step, represents the theoretical response value; Set the warning threshold to B, compare the predicted deviation value with threshold B, when the predicted deviation value is greater than or equal to threshold B, trigger the warning signal, and recalculate the optimization compensation signal, when the predicted deviation value is less than threshold B, record the operation data.

2. The electromagnetic damping shock absorbing device control method for an unmanned aerial vehicle according to claim 1, characterized in that: The use of a database to store the optimized compensation signal refers to using a database to store the obtained optimized compensation signal and recorded operation data, formatting them during the storage process, generating a unique ID for each calculation record, and supporting the export of optimized compensation signal data according to specific query conditions by setting an API interface.

3. The electromagnetic damping shock absorbing device control method for an unmanned aerial vehicle according to claim 2, characterized in that: The display through the visual interface refers to extracting the stored optimization compensation signal data from the database and using the Python tool to generate a line graph, in which the X-axis of the line graph represents time and damping error parameters, and the Y-axis represents the optimization compensation signal.

4. A control system for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle, based on the control method for an electromagnetic damping shock absorbing device for an unmanned aerial vehicle according to any one of claims 1 to 3, characterized in that: include, The acquisition and processing module is used to acquire multimodal data and perform preprocessing; The calculation and output module is used to calculate the external disturbance force, build the BIRF dynamic characteristic model and output the predicted response; The optimization and early warning module is used to calculate the initial damping coefficient and then optimize it, and to issue a real-time early warning based on the optimization compensation signal; The storage and display module is used to store data and display it through a visual interface.

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