Unmanned aerial vehicle adaptive control method, device, equipment, storage medium and product

Abstract

The application discloses a kind of unmanned aerial vehicle adaptive control method, device, equipment, storage medium and product, involve unmanned aerial vehicle technical field, the unmanned aerial vehicle adaptive control method includes: according to gyroscope and accelerometer real-time acquisition sensor data carries out frequency spectrum analysis, determines noise index;Based on control signal and the angular velocity and angular acceleration in the sensor data, determine the maneuvering index indicating the current maneuvering situation of unmanned aerial vehicle;According to the maneuvering index and the noise index, determine the adjustment parameter for adjusting the performance of controller;Based on the identification dynamic model of pre-set and the adjustment parameter, determine the control parameter, based on the unmanned aerial vehicle control of the control parameter.This application can obtain control parameter according to the adjustment parameter determined according to flight condition of identification dynamic model, and unmanned aerial vehicle control can be carried out based on control parameter, can avoid the problem that control parameter adaptation situation is single.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an adaptive control method, apparatus, device, storage medium, and product for UAVs. Background Technology

[0002] In related technologies, traditional UAV control methods, such as the RLS-GMVC method, are not adaptable to dynamic environments. They cannot adjust GMVC parameters in real time according to flight conditions and cannot adapt to various flight conditions, resulting in the calculated control parameters having a limited range of adaptability. Summary of the Invention

[0003] The main objective of this application is to provide an adaptive control method, device, equipment, storage medium, and product for unmanned aerial vehicles (UAVs), aiming to solve the technical problem of the calculated control parameters having a single adaptability.

[0004] To achieve the above objectives, this application proposes an adaptive control method for unmanned aerial vehicles (UAVs), the UAV adaptive control method comprising:

[0005] Spectrum analysis is performed on the sensor data collected in real time by the gyroscope and accelerometer to determine the noise index;

[0006] Based on the control signals and the angular velocity and angular acceleration in the sensor data, a maneuver index characterizing the current maneuver status of the UAV is determined;

[0007] Based on the aforementioned maneuverability and noise levels, determine the adjustment parameters used to regulate the controller performance;

[0008] Based on the preset dynamic identification model and the adjustment parameters, control parameters are determined, and the UAV is controlled based on the control parameters.

[0009] In one embodiment, the adjustment parameters include a closed-loop response time constant, a damping coefficient, and a control weighting coefficient. The step of determining the adjustment parameters for adjusting the controller performance based on the maneuverability index and the noise index includes:

[0010] Obtain initial adjustment parameters, and adjust the initial adjustment parameters according to the maneuver index and the noise index to obtain adjustment parameters for adjusting the controller performance. The maneuver index is positively correlated with the closed-loop response time constant, negatively correlated with the damping coefficient, and negatively correlated with the control weight coefficient.

[0011] In one embodiment, the step of determining the adjustment parameters for adjusting the controller performance based on the maneuverability index and the noise index further includes:

[0012] Obtain the initial adjustment parameters and the angular velocity command in the control signal, and calculate the tracking error between the angular velocity in the angular velocity command and the real-time acquired angular velocity;

[0013] Based on the tracking error and the initial adjustment parameters, adjustment parameters that satisfy the constraints are determined, wherein the constraints are determined based on the maneuver index and the noise index.

[0014] In one embodiment, the step of performing spectrum analysis based on sensor data collected in real time by the gyroscope and accelerometer to determine the noise index includes:

[0015] Convert the sensor data collected in real time by the gyroscope and accelerometer into frequency domain data;

[0016] The power of the frequency domain data at different frequency points was calculated;

[0017] Summing the frequency points within the specified frequency band yields the energy integrals corresponding to each specified frequency band.

[0018] The energy integral is normalized to obtain the normalized energy integral, and the energy integral is weighted and summed according to the weight parameters corresponding to the specified frequency band to obtain the noise index.

[0019] In one embodiment, the step of determining the maneuver index characterizing the current maneuver status of the UAV based on the control signal and the angular velocity and angular acceleration in the sensor data includes:

[0020] Based on the angular velocity and angular acceleration in the sensor data, a maneuver index characterizing the current maneuver status of the UAV is determined;

[0021] The controller outputs control signals in real time, and determines a data vector based on the control signals and the angular velocity, wherein the data vector is a vector used to represent the input and output of the UAV;

[0022] Based on the data vector, a covariance matrix is ​​obtained to evaluate the uncertainty of the current control parameters;

[0023] Based on the covariance matrix, the initial identification dynamic model is iteratively updated to obtain a reasonably preset identification dynamic model that meets the accuracy requirements.

[0024] In one embodiment, the step of determining control parameters based on a preset dynamic identification model and the adjustment parameters, and controlling the UAV based on the control parameters, includes:

[0025] Extract the coefficients from the identified dynamic model;

[0026] The closed-loop response time constant and damping coefficient in the adjustment parameters are normalized to obtain the normalized closed-loop response time constant and damping coefficient.

[0027] Based on the normalized closed-loop response time constant and the damping coefficient, the real and imaginary parts of the dominant poles of the closed-loop transfer function are calculated.

[0028] Based on the real part and the imaginary part, the compensation term and the weight factor matrix are determined;

[0029] Based on the control weight coefficients in the adjustment parameters and the compensation term, the normalization factor is calculated.

[0030] Based on the weight factor matrix and the normalization factor, control parameters are determined, and UAV control is performed based on the control parameters.

[0031] Furthermore, to achieve the above objectives, this application also proposes an adaptive control device for unmanned aerial vehicles (UAVs), the UAV adaptive control device comprising:

[0032] The first determining module is used to perform spectrum analysis based on the sensor data collected in real time by the gyroscope and accelerometer to determine the noise index;

[0033] The second determining module is used to determine the maneuver index characterizing the current maneuver status of the UAV based on the control signal and the angular velocity and angular acceleration in the sensor data;

[0034] The third determining module is used to determine the adjustment parameters for adjusting the controller performance based on the motor index and the noise index.

[0035] The control module is used to determine control parameters based on a preset dynamic identification model and the adjustment parameters, and to control the UAV based on the control parameters.

[0036] In addition, to achieve the above objectives, this application also proposes an adaptive control device for unmanned aerial vehicles (UAVs), the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the UAV adaptive control method described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the UAV adaptive control method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the UAV adaptive control method described above.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] Compared to traditional UAV control methods, such as the RLS-GMVC method, which lack adaptability to dynamic environments and cannot adjust GMVC parameters in real time according to flight conditions, resulting in limited adaptability of calculated control parameters, this application uses spectral analysis based on real-time sensor data collected by gyroscopes and accelerometers to determine noise indices. Based on the control signal and the angular velocity and angular acceleration in the sensor data, it determines maneuver indices characterizing the current maneuvering status of the UAV. Based on the maneuver indices and noise indices, it determines adjustment parameters for adjusting controller performance. Based on a preset dynamic identification model and the adjustment parameters, it determines control parameters and performs UAV control based on these parameters. This application obtains noise and maneuver indices through quantitative analysis of real-time sensor data collected by gyroscopes and accelerometers, and then obtains adjustment parameters for adjusting controller performance based on these indices. Therefore, the dynamic identification model can obtain control parameters based on the adjustment parameters determined by flight conditions, and UAV control can be performed based on these control parameters, avoiding the problem of limited adaptability of control parameters. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating an embodiment of the adaptive control method for unmanned aerial vehicles (UAVs) provided in this application.

[0044] Figure 2 This diagram illustrates the GMVC parameter adjustment method for the UAV in the UAV adaptive control method of this application.

[0045] Figure 3 This is a flowchart of the UAV adaptive control method of this application;

[0046] Figure 4 This is a flowchart illustrating Embodiment 2 of the UAV adaptive control method of this application;

[0047] Figure 5 This is a schematic diagram of the module structure of the UAV adaptive control device according to an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the UAV adaptive control method in the embodiments of this application.

[0049] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0051] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solution of this application embodiment is as follows: perform spectrum analysis based on sensor data collected in real time by gyroscope and accelerometer to determine noise index; determine maneuver index characterizing the current maneuver status of UAV based on control signal and angular velocity and angular acceleration in the sensor data; determine adjustment parameters for adjusting controller performance based on maneuver index and noise index; determine control parameters based on preset dynamic identification model and adjustment parameters; and control UAV based on control parameters.

[0053] In related technologies, traditional UAV control methods, such as the RLS-GMVC method, are not adaptable to dynamic environments. They cannot adjust GMVC parameters in real time according to flight conditions and cannot adapt to various flight conditions, resulting in the calculated control parameters having a limited range of adaptability.

[0054] This application quantifies and analyzes sensor data collected in real time by gyroscopes and accelerometers to obtain noise and maneuverability indicators. Based on these indicators, adjustment parameters for regulating controller performance are derived. Therefore, the dynamic model can be identified based on the adjustment parameters determined by the flight conditions to obtain control parameters. UAV control can then be performed based on these control parameters, avoiding the problem of control parameters adapting to only one situation.

[0055] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or UAV adaptive control device capable of the above functions. The following description uses a UAV adaptive control device as an example to illustrate this embodiment and the subsequent embodiments.

[0056] Based on this, embodiments of this application provide an adaptive control method for unmanned aerial vehicles (UAVs), referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the adaptive control method for unmanned aerial vehicles (UAVs) of this application.

[0057] In this embodiment, the UAV adaptive control method includes steps S10 to S40:

[0058] Step S10: Perform spectrum analysis based on the sensor data collected in real time by the gyroscope and accelerometer to determine the noise index;

[0059] It should be noted that the execution entity in this embodiment is the UAV adaptive control device. A gyroscope is a sensor used to measure the angular velocity of a UAV. An accelerometer is a sensor used to measure the linear acceleration of a UAV. Noise metrics are used to characterize the current sensor noise level. The UAV adaptive control device performs real-time spectrum analysis on the sensor data collected in real time by the gyroscope and accelerometer, divides and weights the noise energy according to frequency bands, and generates a unified noise metric. This achieves objective quantification of the noise level, providing a basis for subsequent adaptive adjustment of GMVC parameters and improving the system's control robustness in environments with varying noise levels.

[0060] Step S20: Based on the control signal and the angular velocity and angular acceleration in the sensor data, determine the maneuver index characterizing the current maneuver status of the UAV;

[0061] As can be understood, angular velocity refers to the angular velocity of a UAV rotating around its body axis, typically measured by a gyroscope. Angular acceleration refers to the rate of change of angular velocity. The identified dynamic model is a mathematical model describing the dynamic characteristics of a system, estimated in real-time based on input and output data using system identification methods such as recursive least squares (RLS). The maneuver index is a comprehensive scalar value used to quantify the intensity of the UAV's current maneuvers, calculated by weighting the normalized values ​​of angular velocity and angular acceleration. The UAV adaptive control device acquires the UAV's dynamic model through real-time system identification and generates maneuver indices by integrating angular velocity and angular acceleration information. This accurately perceives the aircraft's maneuvering state, providing crucial information for subsequent adaptive adjustments to GMVC parameters, enabling the control system to better adapt to various operating conditions of the UAV, from stable flight to intense maneuvers.

[0062] It should be noted that the UAV adaptive control device generates a comprehensive maneuver index (Escore) by sensing and fusing two key dynamic information, angular velocity and angular acceleration, in real time. This enables precise and quantitative evaluation of the UAV's maneuver status, allowing the control system to accurately identify whether the UAV is engaged in smooth cruise or violent maneuvers (such as rapid turns or evasive maneuvers).

[0063] Specifically, the Escore calculation formula is as follows:

[0064]

[0065] Where α and β are weighting coefficients, and α + β = 1. Angular velocity, Angular acceleration, and This is the maximum allowed value.

[0066] Step S30: Determine the adjustment parameters for adjusting the controller performance based on the motor index and the noise index;

[0067] It should be noted that the adjustment parameters refer to the key design parameters in the Generalized Minimum Variance Control (GMVC) algorithm, mainly including: closed-loop response time constant σ, damping coefficient δ, and control weight coefficient λ. The UAV adaptive control device obtains the GMVC adjustment parameters by reflecting flight noise and maneuverability indicators. By establishing a real-time mapping relationship between these indicators and the core design parameters of GMVC, the controller parameters are adaptively adjusted according to the external flight environment and the UAV's own dynamics. This results in excellent control performance under various complex operating conditions with varying noise and maneuverability requirements, improving the system's adaptability and robustness.

[0068] Specifically, refer to Figure 2 , Figure 2 A diagram illustrating the GMVC parameter adjustment method for UAVs is provided. The parameter adjustment module receives the calculated maneuver index Escore and noise index Nscore.

[0069] Step S40: Based on the preset dynamic identification model and the adjustment parameters, determine the control parameters, and control the UAV based on the control parameters.

[0070] It is understandable that the control parameters are the proportional gain Kc, integral gain Ki, and derivative gain Kd of the PID controller. The UAV adaptive control device combines the identified dynamic model with the adaptively adjusted GMVC parameters to generate optimal PID control parameters in real time, which are then applied to the UAV's closed-loop control. This method effectively overcomes the performance degradation problem of traditional fixed-parameter controllers under model uncertainty, noise interference, and maneuver variations, significantly improving the system's adaptive capability, control accuracy, and flight stability. Figure 3 , Figure 3 A flowchart for the adaptive control of unmanned aerial vehicles (UAVs) is provided.

[0071] In one feasible implementation, step S30 includes:

[0072] Obtain initial adjustment parameters, and adjust the initial adjustment parameters according to the maneuver index and the noise index to obtain adjustment parameters for adjusting the controller performance. The maneuver index is positively correlated with the closed-loop response time constant, negatively correlated with the damping coefficient, and negatively correlated with the control weight coefficient.

[0073] It should be noted that the initial adjustment parameters refer to the default initial values ​​of the three key parameters in the Generalized Minimum Variance Control (GMVC) algorithm. The UAV adaptive control device adjusts the σ, δ, and λ parameters in the GMVC algorithm in real time based on the real-time calculated maneuver index (Escore) and noise index (Nscore). By introducing a parameter adjustment mechanism based on dual indices (maneuver and noise), the core parameters of GMVC can deviate from fixed default values ​​and adaptively change with the flight environment and state.

[0074] The initial adjustment parameters include:

[0075] σ: Closed-loop response time constant, σ∈[0.01,1], default σ=0.1;

[0076] δ: Damping coefficient, δ∈[0,1], default δ=0.5;

[0077] λ: Control weight coefficient, λ∈[0,1], default δ=0.5.

[0078] Furthermore, the initial adjustment parameter adjustment strategy:

[0079] When the maneuverability index increases, σ should be decreased to make the system respond faster, and vice versa when it decreases; when the noise index increases, σ should be increased to avoid high-frequency oscillations, and vice versa when it decreases.

[0080]

[0081]

[0082] When noise levels increase, δ should be increased to suppress oscillations, and vice versa when noise levels decrease; when maneuverability levels increase, δ should be decreased to maintain agile response, and vice versa when maneuverability levels decrease.

[0083]

[0084]

[0085] When the maneuverability index increases, λ should be increased to strengthen the control input, and vice versa when it decreases; when the noise index increases, λ should be decreased to avoid aggressive control, and vice versa when it decreases.

[0086]

[0087]

[0088] In one feasible implementation, step S30 further includes:

[0089] Obtain the initial adjustment parameters and the angular velocity command in the control signal, and calculate the tracking error between the angular velocity in the angular velocity command and the real-time acquired angular velocity;

[0090] As can be understood, control signals refer to the complete set of control commands generated by the flight control system and sent to actuators (such as servos and motors), which includes "angular velocity commands" used to control the UAV's attitude, i.e., the target angular velocity value that the UAV is expected to achieve. Tracking error refers to the difference between the target angular velocity command and the real-time measured angular velocity value, used to quantify the tracking performance of the current control system. After acquiring the initial adjustment parameters, the UAV adaptive control device calculates the tracking error between the angular velocity in the angular velocity command and the real-time acquired angular velocity.

[0091] Based on the tracking error and the initial adjustment parameters, adjustment parameters that satisfy the constraints are determined, wherein the constraints are determined based on the maneuver index and the noise index.

[0092] It should be noted that constraints refer to limitations on the numerical range or adjustment magnitude of the final adjustment parameters (σ, δ, λ). The UAV adaptive control system, based on the tracking error and the initial adjustment parameters, restricts the optimization process of the adjustment parameters to a reasonable and safe range.

[0093] Specifically, the UAV adaptive control device can also adjust the strategy based on the initial adjustment parameters and set the dynamic value range of the parameters. The UAV adaptive control device dynamically maps the current operating condition indicators (E_score, N_score) to a set of optimal, continuously changing "ideal target parameters" through mapping functions (such as Mix_σ=E_score) and linear interpolation. Based on the ideal target parameters and the operating condition indicators (E_score, N_score), it determines the allowable range, i.e., the dynamic boundary, of each parameter. For example, during high maneuverability, it automatically reduces the lower limit of the σ value range, guiding the adaptive algorithm to adjust the σ value more quickly.

[0094] Furthermore, the UAV adaptive control device employs a perturbation-observation method to estimate in real time the impact of GMVC parameter changes on the UAV output, i.e., the gradient. (The same applies to other parameters), specifically:

[0095] 1. Periodic disturbance: To avoid coupled interference, a small disturbance Δ is applied to only one parameter in each control cycle (e.g., ...). ).

[0096] Period k: Disturbance σ, i.e.

[0097] Period k+1: Disturbance δ, i.e.

[0098] Period k+2: Perturbation λ, i.e.

[0099] Period k+3: No perturbation, used for observing and calculating gradients.

[0100] 2. Observe the change in output: In the next cycle after the disturbance is applied, the change in the output y(k) (angular velocity) of the UAV adaptive control device is calculated, and the change in output Δy is calculated.

[0101] 3. Calculate the gradient estimate:

[0102] For parameter σ, its gradient is approximately: (Here, y(k) is the perturbed output), and the calculation is similar. and ,because Therefore, the gradient of the error with respect to the parameter is - ,in, For tracking error The partial derivatives of .

[0103] Furthermore, the UAV adaptive control device substitutes all the information obtained in the above steps into the update formula derived based on Lyapunov stability to calculate the GMVC parameters at the next moment. Specifically:

[0104] 1. Regarding the response time constant σ:

[0105] If the current error A positive value and an increase in the parameter σ will cause the output y to increase ( >0), then * If the value is positive, the update term is negative, thus reducing σ, which helps to reduce the error.

[0106] 2. The damping coefficient δ and control weight λ are updated in accordance with the response time constant σ.

[0107] Furthermore, the UAV adaptive control device constrains the updated parameters within the dynamic boundary and transmits the new GMVC parameters after constraint processing to the GMVC controller.

[0108] Furthermore, since drones may encounter unexpected situations during missions, such as bird strikes, component damage (e.g., propeller damage), or sudden payload drops / grabbing (e.g., logistics delivery, fire-fighting material scattering), these situations can cause drastic changes in the aircraft's mass, center of gravity, and aerodynamic characteristics. The original control model may fail instantly, and traditional controllers are prone to causing aircraft instability or even crashes. Therefore, a residual monitor is added to the RLS system identification module to calculate and monitor the residuals (prediction errors) identified by the system in real time. When the model and system are matched, the residuals are small and stable. When structural damage or sudden load changes occur, the system dynamics change drastically, causing the mean and variance of the residuals ε(k) to increase sharply in a short period of time. When the residuals exceed the threshold ε_thresh within M consecutive sampling periods, or the sliding variance of the residuals increases dramatically, a "model mutation" flag signal is immediately generated, triggering the emergency adaptive control process.

[0109] Once the "model mutation" flag is triggered, the system immediately performs the following actions:

[0110] Reset the RLS identifier: Quickly increase the forgetting factor λ in the RLS algorithm (e.g., set it to 0.99 or higher), give new data higher weight, and prompt the identification model to quickly "forget" the old model and track new system dynamics.

[0111] Switch to a conservative GMVC parameter set: In the initial phase after mutation (e.g., the first 2 seconds), ignore the current Escore and Nscore, and force the use of a preset, highly robust GMVC parameter set.

[0112] Increasing the damping coefficient δ can significantly suppress potentially violent oscillations.

[0113] Increase the control weight λ: Limit the magnitude of the control input to prevent dangerous control commands from being output due to model inaccuracies.

[0114] Increase the response time constant σ appropriately: slow down the system response speed and prioritize stability.

[0115] Accelerated Re-identification and Gradual Recovery: After stabilizing the aircraft using conservative parameters, the system enters the "accelerated re-identification" phase. As the new model gradually converges, the adjustment of the GMVC parameters is gradually returned to the adaptive strategy driven by Escore and Nscore, allowing the performance to gradually recover to the optimal level.

[0116] In one feasible implementation, step S10 includes:

[0117] Convert the sensor data collected in real time by the gyroscope and accelerometer into frequency domain data;

[0118] As can be understood, sensor data refers to the time-domain signal sequence measured and output in real time by gyroscopes and accelerometers, typically containing angular velocity and linear acceleration information. The UAV adaptive control device converts the time-domain signal sequence into frequency-domain data using discrete Fourier transform.

[0119] Specifically, the formula for the Discrete Fourier Transform is:

[0120] The power of the frequency domain data at different frequency points was calculated;

[0121] It should be noted that frequency points refer to the discrete frequency components corresponding to the FFT transformation, and their number is related to the length of the data window used in the FFT calculation. Power refers to the estimated value of the power spectral density of the signal at each frequency point. The UAV adaptive control device determines the distribution of signal power at different frequencies, i.e., power.

[0122] Specifically, the formula for calculating power spectral density (PSD) is as follows:

[0123] Summing the frequency points within the specified frequency band yields the energy integrals corresponding to each specified frequency band.

[0124] Understandably, a specified frequency band refers to several specific frequency ranges pre-divided for noise analysis, such as the low-frequency band (1-20Hz), the mid-frequency band (20-100Hz), and the high-frequency band (>100Hz). The UAV adaptive control device accumulates the power spectral density (PSD) values ​​at all frequency points falling within a specified frequency band to obtain the total energy of that frequency band.

[0125] Specifically, the key frequency band division is shown in Table 1:

[0126] Table 1

[0127]

[0128] Specifically, the energy E per frequency band b (b=1,2,3):

[0129] The energy integral is normalized to obtain the normalized energy integral, and the energy integral is weighted and summed according to the weight parameters corresponding to the specified frequency band to obtain the noise index.

[0130] It should be noted that the weighting parameter refers to a coefficient pre-assigned to each specified frequency band. As shown in Table 1, the weighting for low frequency is γ1=0.4, for mid-frequency is γ2=0.5, and for high frequency is γ3=0.1. The original energy integral values ​​calculated by the UAV adaptive control device for each frequency band (low frequency, mid frequency, and high frequency) are mapped to a unified, dimensionless numerical range (e.g., within the [0,1] interval) through mathematical transformation to eliminate the difference in magnitude between the original energy values. The normalized energy values ​​are then multiplied by the weighting parameter and summed to obtain the noise index.

[0131] Specifically, the UAV adaptive control device maps the frequency band energy to the [0,1] interval and defines the noise level L. b :

[0132] Furthermore, the UAV adaptive control device weights the energy of each frequency band to generate the total noise index Nscore:

[0133] Among them, Weighting coefficients.

[0134] In this embodiment, the noise and maneuvering conditions of the UAV are quantified in real time, and the parameters of the GMVC method are adjusted according to the noise and maneuvering conditions so that the calculated control parameters can better adapt to the situation of drastic changes in UAV noise.

[0135] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Before step S20, the UAV adaptive control method further includes steps S01 to S04:

[0136] Step S01: Real-time acquisition of control signals output by the controller, and determination of data vectors based on the control signals and the angular velocity, wherein the data vectors are vectors used to represent the input and output of the UAV;

[0137] Understandably, the UAV adaptive control device constructs data vectors containing system input and output information in real time, providing the necessary data foundation for subsequent online system identification based on the RLS algorithm.

[0138] Step S02: Based on the data vector, obtain the covariance matrix for evaluating the uncertainty of the current control parameters;

[0139] It should be noted that the covariance matrix is ​​a key matrix in the Recursive Least Squares (RLS) algorithm. Its dimension is the same as that of the system parameter vector θ to be identified. Each element of this matrix reflects the uncertainty and correlation between the estimated system parameters and within itself. The UAV adaptive control device updates and maintains this covariance matrix based on the data vector. This matrix dynamically adjusts the parameter update step size in the algorithm, directly affecting the convergence speed and robustness of the system identification.

[0140] Step S03: Based on the covariance matrix, the initial identification dynamic model is iteratively updated to obtain a preset identification dynamic model that meets the accuracy requirements and is reasonable.

[0141] It is understandable that the initial identification dynamic model refers to the initial estimate θ(0) of a system parameter vector set at the beginning of the system identification stage. This initial value can be set based on prior knowledge or set as a zero vector. In each sampling period, the UAV adaptive control device recursively corrects the system parameter estimate using new input and output data (i.e., data vectors) and the current covariance matrix. After multiple iterations, an identification dynamic model that meets the accuracy requirements is obtained.

[0142] Furthermore, the UAV adaptive control device will determine whether the identified dynamic model that meets the accuracy requirements is reasonable. If the system identification fails to fit a reasonable dynamic and the model does not converge, then the identification will end and the default control parameters will be used.

[0143] Specifically, the steps for the UAV adaptive control device to perform real-time system identification during flight using the RLS method are as follows:

[0144] The parameter vector is in the form of the parameter vector of the system being identified, expressed as θ as follows, and can be defined in different orders depending on the system:

[0145]

[0146] The control input and angular velocity measurements are used as a data vector, which is represented as follows: The form is as follows, where u represents the control input and y represents the measured angular velocity:

[0147]

[0148] Update the covariance matrix:

[0149] Where λ is the forgetting factor, λ∈[0,1]

[0150] System parameter estimation and updating yields a real-time system model of the identification system during flight:

[0151] Determine whether the dynamic model is reasonable.

[0152]

[0153] Among them, Var( Let ) be the variance of the parameter in the most recent N iterations; This is the variance threshold.

[0154] In one feasible implementation, step S40 includes:

[0155] Extract the coefficients from the identified dynamic model;

[0156] It should be noted that the coefficients are those of the polynomials in the numerator and denominator of the identification dynamic model. The UAV adaptive control device extracts the coefficients of the polynomials in the numerator and denominator of the latest updated and validated identification dynamic model at the current moment.

[0157] The closed-loop response time constant and damping coefficient in the adjustment parameters are normalized to obtain the normalized closed-loop response time constant and damping coefficient.

[0158] Understandably, the UAV adaptive control device normalizes the closed-loop response time constant and damping coefficient to obtain the normalized closed-loop response time constant and damping coefficient.

[0159] Based on the normalized closed-loop response time constant and the damping coefficient, the real and imaginary parts of the dominant poles of the closed-loop transfer function are calculated.

[0160] It should be noted that the dominant pole refers to the conjugate complex pole pair that plays the most significant role in the system's dynamic response (such as a step response) among all poles in the closed-loop system. The real and imaginary parts refer to the coordinate representation of this conjugate complex pole in the complex plane. The UAV adaptive control device converts the normalized time constant (σ_n) and damping coefficient (δ) representing performance requirements into the specific locations (real and imaginary parts) of the desired dominant pole in the closed-loop system, thus accurately mapping the user's performance indications (speed, oscillation level) into specific mathematical objectives in the control system design.

[0161] Based on the real part and the imaginary part, the compensation term and the weight factor matrix are determined;

[0162] Understandably, the compensation term refers to a dynamic term introduced in the Generalized Minimum Variance Control (GMVC) algorithm to compensate for the influence of system zeros on the closed-loop response. The weighting factor matrix is ​​used to balance the system output tracking error with the control input energy. The UAV adaptive control device constructs the weighting factor matrix by utilizing the specific locations (real and imaginary parts) of the desired dominant poles.

[0163] Based on the control weight coefficients in the adjustment parameters and the compensation term, the normalization factor is calculated.

[0164] It should be noted that the control weighting coefficient is used in the performance index function to weigh the cost of system output tracking error against the cost of control input energy. A larger λ indicates a greater penalty on control energy, tending to use more lenient control actions. The UAV adaptive control device calculates the normalization factor based on the control weighting coefficient and the compensation term.

[0165] Based on the weight factor matrix and the normalization factor, control parameters are determined, and UAV control is performed based on the control parameters.

[0166] Understandably, the UAV adaptive control device integrates the intermediate variables obtained from the aforementioned steps (including the weighting factor matrix Γ, the normalization factor η, the compensation term F, and the identified model coefficients) to calculate the proportional (Kc), integral (Ki), and derivative (Kd) gain values ​​that can be directly applied to the UAV flight controller. The calculated set of PID parameters Kc, Ki, and Kd is then updated to the underlying control loop of the flight control system. The flight control system uses these new parameters to calculate the control quantities acting on the servos or motors in real time, thereby achieving precise and adaptive tracking control of the UAV's attitude or trajectory.

[0167] Specifically, the steps for providing PID control parameters using the GMVC method are as follows:

[0168] Extract the coefficients of each term in the numerator and denominator of the model.

[0169] The general form of the identified model is:

[0170]

[0171] Parameter normalization

[0172] Closed-loop response time constant normalization:

[0173] Where T s σ is the sampling time, and σ is the closed-loop response time constant.

[0174] Damping adjustment parameters

[0175]

[0176] Where δ is the damping adjustment parameter

[0177] Desired closed-loop pole configuration

[0178] Dominant poles: real and imaginary parts

[0179]

[0180] Calculate intermediate variables

[0181] System dynamic compensation items:

[0182]

[0183] Weighting factor matrix:

[0184]

[0185] Calculate the normalization factor

[0186]

[0187] Where λ is the control input weighting factor

[0188] Calculate the control parameter PID gain

[0189] Furthermore, after obtaining the PID parameters, the following steps are also included:

[0190] Determine the rationality of the PID parameter values.

[0191] A reasonable parameter range is If the parameters do not meet this range, the parameter tuning is considered to have failed, and the pre-set default control parameters will be used.

[0192] Time-domain response of a closed-loop system under segmented PID parameter control

[0193] Calculate time-domain performance metrics:

[0194] Calculate overshoot:

[0195] Where: y peak y is the peak value of the step response. steady This is the steady-state value.

[0196] Calculate the adjustment time:

[0197]

[0198] in =0.05.

[0199] If the overshoot is less than 20% and the settling time is less than 1.0s, the parameter tuning is considered to have failed, and the pre-set default control parameters are used.

[0200] In this embodiment, since the traditional RLS-GMVC method lacks stability constraints, the commonly used parameter rationality detection method is to judge whether the parameters are within a reasonable range by numerical judgment, which lacks the analysis of the control status index of the closed-loop system. In addition to the numerical judgment of parameter rationality, this application adds the judgment of the rationality of the time domain index of the closed-loop system to avoid the situation of control divergence caused by the use of unstable parameters.

[0201] This application also provides an adaptive control device for unmanned aerial vehicles (UAVs). Please refer to [link / reference]. Figure 5 The UAV adaptive control device includes:

[0202] The first determining module 10 is used to perform spectrum analysis based on the sensor data collected in real time by the gyroscope and accelerometer to determine the noise index;

[0203] The second determining module 20 is used to determine a maneuver index characterizing the current maneuver status of the UAV based on the control signal and the angular velocity and angular acceleration in the sensor data;

[0204] The third determining module 30 is used to determine the adjustment parameters for adjusting the performance of the controller based on the motor index and the noise index.

[0205] The control module 40 is used to determine control parameters based on a preset dynamic identification model and the adjustment parameters, and to control the UAV based on the control parameters.

[0206] Optionally, the third determining module includes:

[0207] The first adjustment submodule is used to obtain initial adjustment parameters, and adjust the initial adjustment parameters according to the maneuver index and the noise index to obtain adjustment parameters for adjusting the controller performance. The maneuver index is positively correlated with the closed-loop response time constant, negatively correlated with the damping coefficient, and negatively correlated with the control weight coefficient.

[0208] The second adjustment submodule is used to acquire initial adjustment parameters and angular velocity commands in the control signal, and calculate the tracking error between the angular velocity in the angular velocity command and the real-time acquired angular velocity; based on the tracking error and the initial adjustment parameters, it determines adjustment parameters that satisfy the constraints, wherein the constraints are determined based on the maneuver index and the noise index.

[0209] Optionally, the first determining module includes:

[0210] The weighted summation submodule is used to convert sensor data collected in real time by the gyroscope and accelerometer into frequency domain data; calculate the power of the frequency domain data at different frequency points; sum the frequency points within a specified frequency band to obtain the energy integral corresponding to the specified frequency band; normalize the energy integral to obtain the normalized energy integral; and perform weighted summation on the energy integral according to the weight parameters corresponding to the specified frequency band to obtain the noise index.

[0211] Optionally, the second determining module includes:

[0212] The update submodule is used to collect the control signals output by the controller in real time, and determine the data vector based on the control signals and the angular velocity, wherein the data vector is a vector used to represent the input and output of the UAV; based on the data vector, a covariance matrix for evaluating the uncertainty of the current control parameters is obtained; based on the covariance matrix, the initial identification dynamic model is iteratively updated to obtain a preset identification dynamic model that meets the accuracy requirements and is reasonable.

[0213] Optionally, the control module includes:

[0214] The closed-loop control submodule is used to extract the coefficients of each term in the identified dynamic model; normalize the closed-loop response time constant and damping coefficient in the adjustment parameters to obtain the normalized closed-loop response time constant and damping coefficient; calculate the real and imaginary parts of the dominant poles of the closed-loop transfer function based on the normalized closed-loop response time constant and damping coefficient; determine the compensation term and weight factor matrix based on the real and imaginary parts; calculate the normalization factor based on the control weight coefficient and the compensation term in the adjustment parameters; determine the control parameters based on the weight factor matrix and the normalization factor; and perform UAV control based on the control parameters.

[0215] The UAV adaptive control device provided in this application, employing the UAV adaptive control method in the above embodiments, can solve the technical problem of UAV adaptive control. Compared with the prior art, the beneficial effects of the UAV adaptive control device provided in this application are the same as those of the UAV adaptive control method provided in the above embodiments, and other technical features in the UAV adaptive control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0216] This application provides an adaptive control device for unmanned aerial vehicles (UAVs), comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the UAV adaptive control method in Embodiment 1 described above.

[0217] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the drone adaptive control device of the embodiments of this application. The drone adaptive control device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, tablets, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The drone adaptive control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0218] like Figure 6 As shown, the UAV adaptive control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the UAV adaptive control device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the UAV adaptive control device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows a UAV adaptive control device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0219] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0220] The UAV adaptive control device provided in this application, employing the UAV adaptive control method in the above embodiments, can solve the technical problem of UAV adaptive control. Compared with the prior art, the beneficial effects of the UAV adaptive control device provided in this application are the same as those of the UAV adaptive control method provided in the above embodiments, and other technical features in this UAV adaptive control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0221] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0222] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0223] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the UAV adaptive control method in the above embodiments.

[0224] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0225] The aforementioned computer-readable storage medium may be included in the UAV adaptive control device; or it may exist independently and not be assembled into the UAV adaptive control device.

[0226] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the UAV adaptive control device, cause the UAV adaptive control device to: perform spectrum analysis based on sensor data collected in real time by gyroscopes and accelerometers to determine noise indicators; determine maneuver indicators characterizing the current maneuvering status of the UAV based on control signals and the angular velocity and angular acceleration in the sensor data; determine adjustment parameters for adjusting controller performance based on the maneuver indicators and the noise indicators; determine control parameters based on a preset dynamic identification model and the adjustment parameters; and perform UAV control based on the control parameters.

[0227] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0228] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0229] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0230] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described UAV adaptive control method, thereby solving the technical problem of UAV adaptive control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the UAV adaptive control method provided in the above embodiments, and will not be repeated here.

[0231] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described UAV adaptive control method.

[0232] The computer program product provided in this application can solve the technical problem of adaptive control of unmanned aerial vehicles (UAVs). Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the UAV adaptive control method provided in the above embodiments, and will not be repeated here.

[0233] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for adaptive control of unmanned aerial vehicles, characterized in that, The UAV adaptive control method includes: Real-time spectrum analysis is performed based on sensor data collected in real time by gyroscopes and accelerometers. Noise energy is divided into frequency bands and weighted and integrated to generate a noise index, which is used to characterize the current sensor noise level. Based on the angular velocity and angular acceleration in the control signal and the sensor data, a normalized weighted calculation is performed to obtain a maneuver index that characterizes the current maneuver status of the UAV. The maneuver index is a comprehensive scalar value used to quantify the intensity of the current maneuver of the UAV. Based on the aforementioned maneuverability and noise levels, determine the adjustment parameters used to regulate the controller performance; Based on the preset dynamic identification model and the adjustment parameters, control parameters are determined, and the UAV is controlled based on the control parameters. 2.The UAV adaptive control method of claim 1, wherein, The adjustment parameters include the closed-loop response time constant, damping coefficient, and control weighting coefficient. The step of determining the adjustment parameters for adjusting the controller performance based on the maneuverability index and the noise index includes: Obtain initial adjustment parameters, and adjust the initial adjustment parameters according to the maneuver index and the noise index to obtain adjustment parameters for adjusting the controller performance. The maneuver index is positively correlated with the closed-loop response time constant, negatively correlated with the damping coefficient, and negatively correlated with the control weight coefficient. 3.The UAV adaptive control method of claim 1, wherein, The step of determining the adjustment parameters for adjusting the controller performance based on the maneuverability index and the noise index further includes: Obtain the initial adjustment parameters and the angular velocity command in the control signal, and calculate the tracking error between the angular velocity in the angular velocity command and the real-time acquired angular velocity; Based on the tracking error and the initial adjustment parameters, adjustment parameters that satisfy the constraints are determined, wherein the constraints are determined based on the maneuver index and the noise index. 4.The UAV adaptive control method of claim 1, wherein, The step of performing spectrum analysis based on real-time sensor data collected by the gyroscope and accelerometer to determine the noise index includes: Convert the sensor data collected in real time by the gyroscope and accelerometer into frequency domain data; The power of the frequency domain data at different frequency points was calculated; Summing the frequency points within the specified frequency band yields the energy integrals corresponding to each specified frequency band. The energy integral is normalized to obtain the normalized energy integral, and the energy integral is weighted and summed according to the weight parameters corresponding to the specified frequency band to obtain the noise index. 5.The UAV adaptive control method of claim 1, wherein, Prior to the step of determining the maneuvering indicators characterizing the current maneuvering status of the UAV based on the control signals and the angular velocity and angular acceleration in the sensor data, the following steps are included: The controller outputs control signals in real time, and determines a data vector based on the control signals and the angular velocity, wherein the data vector is a vector used to represent the input and output of the UAV; Based on the data vector, a covariance matrix is ​​obtained to evaluate the uncertainty of the current control parameters; Based on the covariance matrix, the initial identification dynamic model is iteratively updated to obtain a reasonably preset identification dynamic model that meets the accuracy requirements. 6.The UAV adaptive control method of claim 1, wherein, The steps of determining control parameters based on the preset dynamic identification model and the adjustment parameters, and controlling the UAV based on the control parameters, include: Extract the coefficients from the identified dynamic model; The closed-loop response time constant and damping coefficient in the adjustment parameters are normalized to obtain the normalized closed-loop response time constant and damping coefficient. Based on the normalized closed-loop response time constant and the damping coefficient, the real and imaginary parts of the dominant poles of the closed-loop transfer function are calculated. Based on the real part and the imaginary part, the compensation term and the weight factor matrix are determined; Based on the control weight coefficients in the adjustment parameters and the compensation term, the normalization factor is calculated. Based on the weight factor matrix and the normalization factor, control parameters are determined, and UAV control is performed based on the control parameters.

7. An unmanned aerial vehicle adaptive control device, characterized in that, The device includes: The first determining module is used to perform real-time spectrum analysis based on sensor data collected in real time by the gyroscope and accelerometer, divide noise energy by frequency band and integrate it with weights to generate a noise index, which is used to characterize the current sensor noise level. The second determining module is used to perform normalized value weighted calculation based on the angular velocity and angular acceleration in the control signal and the sensor data to obtain a maneuver index characterizing the current maneuver status of the UAV. The maneuver index is a comprehensive scalar value used to quantify the intensity of the current maneuver of the UAV. The third determining module is used to determine the adjustment parameters for adjusting the controller performance based on the motor index and the noise index. The control module is used to determine control parameters based on a preset dynamic identification model and the adjustment parameters, and to control the UAV based on the control parameters.

8. An unmanned aerial vehicle adaptive control device, comprising: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the UAV adaptive control method as described in any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the UAV adaptive control method as described in any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the UAV adaptive control method as described in any one of claims 1 to 6.

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