Flight control method and device for an aircraft

Abstract

This application relates to the field of flight control technology, and in particular to a flight control method and apparatus for an aircraft. The method includes: acquiring multi-source sensing data of the constituent materials of the target aircraft; calculating the dynamic parameters of the constituent materials based on the multi-source sensing data; inputting the dynamic parameters into a pre-constructed aircraft dynamic model, and calculating the tracking error of the aircraft by comparing the real-time state data of the aircraft with the expected trajectory; calculating the adaptive weight of structural damping based on the tracking error, and determining the total command torque of the aircraft according to the weight; and calculating the motor speed of the aircraft based on the total command torque, so as to control the flight of the aircraft according to the motor speed. This solves the problem in related technologies where the structural differences of the natural materials constituting the aircraft lead to the inability to guarantee the stability and safety of the aircraft.

Description

Technical Field

[0001] This application relates to the field of flight control technology, and in particular to a flight control method and apparatus for an aircraft. Background Technology

[0002] In related technologies, traditional drone designs typically use industrial materials such as aluminum alloys or carbon fibers. These materials are relatively uniform in structure and easy to control precisely during manufacturing. With the promotion of sustainable development and green environmental protection concepts, the application of natural renewable materials (such as bamboo) in high-tech fields such as aerospace has gradually attracted attention. As a lightweight and high-strength material, bamboo has good mechanical properties and biodegradability, making it a potential alternative to traditional materials.

[0003] However, as a natural material, bamboo has differences in its external and internal structure, including density, crystal structure, cracks, and stress concentration. These differences introduce certain uncertainties when using bamboo to construct precision equipment such as drones. Specifically, in the use of natural materials such as bamboo, the differences in its internal structure and external characteristics result in different mechanical properties for each piece of bamboo. These structural differences not only affect the physical properties of bamboo, such as strength and stiffness, but may also lead to problems such as assembly errors and performance instability.

[0004] Currently, there is a lack of effective technology to identify and correct differences in the material structure of drones made of natural materials (especially bamboo). Therefore, how to accurately identify the external and internal structure of bamboo through sensing elements and dynamically adjust the parameters of the flight control algorithm based on these identification results to ensure the stability and safety of drones is an urgent problem to be solved in current technology. Summary of the Invention

[0005] This application provides a flight control method and apparatus for an aircraft to solve the problems in the related art, such as the inability to guarantee the stability and safety of the aircraft due to the structural differences of the natural materials constituting the aircraft.

[0006] The first aspect of this application provides a flight control method for an aircraft, comprising the following steps: acquiring multi-source sensing data of the constituent materials of the target aircraft; calculating the dynamic parameters of the constituent materials based on the multi-source sensing data; inputting the dynamic parameters into a pre-constructed aircraft dynamic model; and calculating the tracking error of the aircraft by comparing the real-time state data of the aircraft with the expected trajectory.

[0007] The adaptive weights of structural damping are calculated based on the tracking error, and the total command torque of the aircraft is determined according to the weights. The motor speed of the aircraft is calculated based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

[0008] Through the above-mentioned technical means, the embodiments of this application can obtain information on the constituent materials of the aircraft through multi-source sensing data, calculate its dynamic parameters and input them into a pre-built aircraft dynamic model, calculate the tracking error of the aircraft caused by the dynamic parameters in real time, adaptively calculate the structural damping weight based on the tracking error, determine the total command torque of the aircraft, and then calculate the motor speed and control the flight of the aircraft. This can achieve precise compensation for the influence of structural differences such as bamboo, and effectively improve the stability and accuracy of the aircraft flight control.

[0009] Optionally, in one embodiment of this application, the calculation of the dynamic parameters of the constituent material includes: extracting the geometric and surface features of the aircraft constituent material based on the lidar point cloud data and visual image sequence data in the multi-source sensing data, and determining the surface defect distribution factor of the aircraft constituent material based on the features; calculating the three-dimensional density matrix of the material based on the light projection intensity and infrared thermal image data in the multi-source sensing data; and calculating the dynamic parameter vector of the constituent material based on the surface defect distribution factor and the three-dimensional density matrix.

[0010] Through the above-mentioned technical means, the embodiments of this application can first extract the geometric and surface features of the materials constituting the aircraft and determine the surface defect distribution factor by fusing multi-source sensing data such as lidar point clouds, visual image sequences, light projection intensity and infrared thermal images. Then, based on light projection intensity and infrared thermal image data, the three-dimensional density matrix of the material is calculated. Finally, by combining the two, the material dynamic parameter vector can be accurately calculated. Thus, the mechanical properties and defect correlation information of the material can be obtained comprehensively and efficiently, providing data support for the performance evaluation of aircraft materials.

[0011] Optionally, in one embodiment of this application, the formula for calculating the dynamic parameter vector is:

[0012]

[0013]

[0014] in, This represents an inertial tensor containing off-diagonal coupling terms. Indicates the actual location of the center of mass. This represents the operator for constructing antisymmetric matrices. Indicates nominal stiffness, Indicates equivalent stiffness. Represents the surface defect distribution factor. This represents a vector of dynamic parameters of the materials that make up the aircraft. This represents the numbering of each part / node in the density field during the discrete integration process. Indicates the first Density values ​​at each node Indicates the first The position of each node relative to the actual centroid. This represents the stiffness correction factor.

[0015] Through the above-mentioned technical means, the embodiments of this application can accurately identify the differences in the external and internal structure of bamboo through multi-source sensing elements (such as visual sensors, X-rays, lidar, etc.), including surface texture, cracks, density distribution and microcracks, and then determine the physical parameter vector of the material. This information can further provide a precise basis for subsequent structural modeling of aircraft constituent materials (such as bamboo) and adjustment of flight control algorithms, so as to solve the technical drawbacks caused by the inhomogeneity of natural materials in traditional technologies.

[0016] Optionally, in one embodiment of this application, the pre-built aircraft dynamics model is:

[0017] in, and Indicates the nominal model. Represents the set of uncertain terms. This indicates the amount of compensation to be controlled. This indicates the update rate of the aircraft's status.

[0018] Using the aforementioned technical means, this application embodiment describes the impact of differences in bamboo structure on flight status. A nonlinear system equation containing a "nominal model" and "structural uncertainty" can be established as a dynamic model. Based on the calculated dynamic parameters of the aircraft's constituent materials, the model can calculate in real time the tracking error of the aircraft's flight status caused by the dynamic parameters; it can also estimate and offset structural uncertainty in real time, thereby accurately quantifying the impact of differences in bamboo structure on flight status and improving the aircraft's precise control and error compensation capabilities.

[0019] Optionally, in one embodiment of this application, the formula for calculating the motor speed of the aircraft is:

[0020]

[0021]

[0022]

[0023] in, Represents the regression vector. Indicates the learning item, Indicates tracking error. Indicates adaptive weights, Indicates the positive damping coefficient. Indicates the compensation control amount. Indicates the total commanded torque. Indicates the baseline control quantity. Indicates the motor speed. Represents the thrust distribution matrix. This indicates the offset of the center of gravity. This represents the dynamic update rate of the weight matrix of the neural network used.

[0024] Through the above-mentioned technical means, the embodiments of this application can dynamically adjust the control parameters in the flight control algorithm according to the differences in bamboo structure, so as to ensure that the drone made of natural renewable materials (such as bamboo) can still fly stably in the case of bamboo with uneven structure, thereby improving the stability and safety of the aircraft.

[0025] A second aspect of this application provides a flight control device for an aircraft, comprising: an acquisition module for acquiring multi-source sensing data of the constituent materials of a target aircraft; a first calculation module for calculating dynamic parameters of the constituent materials based on the multi-source sensing data; a second calculation module for inputting the dynamic parameters into a pre-constructed aircraft dynamic model and calculating the tracking error of the aircraft by comparing the real-time state data of the aircraft with the expected trajectory; a third calculation module for calculating adaptive weights of structural damping based on the tracking error and determining the total command torque of the aircraft according to the weights; and a control module for calculating the motor speed of the aircraft based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

[0026] Through the above-mentioned technical means, the embodiments of this application can obtain information on the constituent materials of the aircraft through multi-source sensing data, calculate its dynamic parameters and input them into a pre-built aircraft dynamic model, calculate the tracking error of the aircraft caused by the dynamic parameters in real time, adaptively calculate the structural damping weight based on the tracking error, determine the total command torque of the aircraft, and then calculate the motor speed and control the flight of the aircraft. This can achieve precise compensation for the influence of structural differences such as bamboo, and effectively improve the stability and accuracy of the aircraft flight control.

[0027] Optionally, in one embodiment of this application, the first calculation module includes: a geometric and surface feature extraction module, used to extract the geometric and surface features of the aircraft constituent material based on the lidar point cloud data and visual image sequence data in the multi-source sensing data, so as to determine the surface defect distribution factor of the aircraft constituent material based on the features; an internal non-uniform density field mapping module, used to calculate the three-dimensional density matrix of the material based on the light projection intensity and infrared thermal image data in the multi-source sensing data; and a dynamic parameter calculation module, used to calculate the dynamic parameter vector of the constituent material based on the surface defect distribution factor and the three-dimensional density matrix.

[0028] Through the above-mentioned technical means, the embodiments of this application can first extract the geometric and surface features of the materials constituting the aircraft and determine the surface defect distribution factor by fusing multi-source sensing data such as lidar point clouds, visual image sequences, light projection intensity and infrared thermal images. Then, based on light projection intensity and infrared thermal image data, the three-dimensional density matrix of the material is calculated. Finally, by combining the two, the material dynamic parameter vector can be accurately calculated. Thus, the mechanical properties and defect correlation information of the material can be obtained comprehensively and efficiently, providing data support for the performance evaluation of aircraft materials.

[0029] Optionally, in one embodiment of this application, the formula for calculating the dynamic parameter vector is:

[0030]

[0031]

[0032] in, This represents an inertial tensor containing off-diagonal coupling terms. Indicates the actual location of the center of mass. This represents the operator for constructing antisymmetric matrices. Indicates nominal stiffness, Indicates equivalent stiffness. Represents the surface defect distribution factor. This represents a vector of dynamic parameters of the materials that make up the aircraft. This represents the numbering of each part / node in the density field during the discrete integration process. Indicates the first Density values ​​at each node Indicates the first The position of each node relative to the actual centroid. This represents the stiffness correction factor.

[0033] Through the above-mentioned technical means, the embodiments of this application can accurately identify the differences in the external and internal structure of bamboo through multi-source sensing elements (such as visual sensors, X-rays, lidar, etc.), including surface texture, cracks, density distribution and microcracks, and then determine the physical parameter vector of the material. This information can further provide a precise basis for subsequent structural modeling of aircraft constituent materials (such as bamboo) and adjustment of flight control algorithms, so as to solve the technical drawbacks caused by the inhomogeneity of natural materials in traditional technologies.

[0034] Optionally, in one embodiment of this application, the pre-built aircraft dynamics model is:

[0035] in, and Indicates the nominal model. Represents the set of uncertain terms. This indicates the amount of compensation to be controlled. This indicates the update rate of the aircraft's status.

[0036] Using the aforementioned technical means, this application embodiment describes the impact of differences in bamboo structure on flight status. A nonlinear system equation containing a "nominal model" and "structural uncertainty" can be established as a dynamic model. Based on the calculated dynamic parameters of the aircraft's constituent materials, the model can calculate in real time the tracking error of the aircraft's flight status caused by the dynamic parameters; it can also estimate and offset structural uncertainty in real time, thereby accurately quantifying the impact of differences in bamboo structure on flight status and improving the aircraft's precise control and error compensation capabilities.

[0037] Optionally, in one embodiment of this application, the formula for calculating the motor speed of the aircraft is:

[0038]

[0039]

[0040]

[0041] in, Represents the regression vector. Indicates the learning item, Indicates tracking error. Indicates adaptive weights, Indicates the positive damping coefficient. Indicates the compensation control amount. Indicates the total commanded torque. Indicates the baseline control quantity. Indicates the motor speed. Represents the thrust distribution matrix. This indicates the offset of the center of gravity. This represents the dynamic update rate of the weight matrix of the neural network used.

[0042] Through the above-mentioned technical means, the embodiments of this application can dynamically adjust the control parameters in the flight control algorithm according to the differences in bamboo structure, so as to ensure that the drone made of natural renewable materials (such as bamboo) can still fly stably in the case of bamboo with uneven structure, thereby improving the stability and safety of the aircraft.

[0043] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the flight control method for an aircraft as described in the above embodiments.

[0044] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the flight control method for the aircraft described above.

[0045] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the flight control method for the aircraft described above.

[0046] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a flight control method for an aircraft according to an embodiment of this application; Figure 2 This is a schematic diagram of the framework for flight layered control of an aircraft according to a specific embodiment of this application; Figure 3 This is a schematic diagram of the flight control device of an aircraft according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0048] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0049] The flight control method and apparatus for an aircraft according to embodiments of this application are described below with reference to the accompanying drawings. Addressing the problem mentioned in the background section of the related art, where the structural differences in the natural materials constituting the aircraft lead to the inability to guarantee the stability and safety of the aircraft, this application provides a flight control method for an aircraft. In this method, external and internal structural information of bamboo is collected through multiple sensors. Based on the acquired data, different characteristics of the bamboo are identified, and control parameters in the flight control algorithm are adaptively fine-tuned to ensure stable operation of the aircraft among different individual bamboo samples. This solves the problem in the related art where the structural differences in the natural materials constituting the aircraft lead to the inability to guarantee the stability and safety of the aircraft.

[0050] Specifically, Figure 1 This is a schematic flowchart illustrating a flight control method for an aircraft provided in an embodiment of this application.

[0051] like Figure 1 As shown, the flight control method of this aircraft includes the following steps: In step S101, multi-source sensing data of the materials constituting the target aircraft are acquired.

[0052] In actual operation, the sensing data of the aircraft's constituent materials can be collected through a variety of sensors.

[0053] For example, when the aircraft is a drone and the constituent material is bamboo, the embodiments of this application can acquire external and internal structural data of bamboo through various sensors such as visual sensors, X-ray sensors, lidar, and infrared sensors.

[0054] Among them, visual sensors can be used to capture defects such as texture and cracks on the surface of bamboo; X-ray sensors can be used to detect the internal density distribution and micro-cracks of bamboo; lidar can be used to scan the geometry of the bamboo surface; and infrared sensors can be used to identify temperature changes in bamboo.

[0055] The following are several methods for collecting multi-source data on the internal and external structures of natural renewable materials using sensing elements, taking bamboo as an example: 1. Visual sensor (camera) In the embodiments of this application, external images of bamboo can be acquired by high-precision visual sensors (such as RGB cameras, depth cameras, etc.), and image processing methods can be used to analyze the surface features, cracks, textures and density changes of bamboo.

[0056] Specifically, computer vision techniques (such as SIFT, SURF, CNN, etc.) can be used to analyze the surface features of bamboo, extract texture features, and classify them to help identify surface defects in bamboo.

[0057]

[0058] in, Indicates the image in Pixel value at time, and For the horizontal and vertical displacement in the image, For time difference.

[0059] 2. X-ray sensor X-ray sensors can be used to deeply probe the internal structure of bamboo, obtaining information such as density distribution, crystal structure, and porosity. The imaging formula for X-rays is:

[0060] in, Initial X-ray intensity, The absorption coefficient of bamboo is given. This represents the penetration depth of X-rays. By calculating the intensity of X-rays penetrating the bamboo, the density and internal structure information of the bamboo can be obtained.

[0061] 3. LiDAR (Light Detection and Ranging) Sensor LiDAR can be used to scan the surface of bamboo, measuring its geometry and potential minute defects (such as cracks, deformation, etc.). The LiDAR ranging formula is:

[0062] in, For the measured distance, At the speed of light, This represents the time difference between the return of the laser signal and the actual signal. This method allows us to obtain information about minute cracks and uneven areas on the bamboo surface.

[0063] 4. Infrared sensor Infrared sensors can be used to capture the heat distribution of bamboo and identify potential structural defects, such as surface temperature differences. The imaging formula for an infrared sensor is as follows:

[0064] in, The radiation intensity of a surface object. This is the Stuart-Boltzmann constant. Let emissivity be the object's emissivity. Temperature is used as the criterion for detecting minute changes and defects inside bamboo using thermal imaging technology.

[0065] The data from the above sensors can be collected synchronously and calibrated with timestamps to ensure data consistency and synchronization.

[0066] Furthermore, to ensure the accuracy of the data acquisition, the sensor system can adopt an adaptive sampling strategy, performing multiple scans at different locations on the bamboo, especially focusing on areas where there are significant differences between the surface and internal structure of the bamboo.

[0067] After collecting multi-source data using multiple sensors, the multi-source data can be further fused to estimate the aircraft's state.

[0068] As a feasible approach, to ensure that the aircraft can dynamically adjust according to the structural characteristics of bamboo, this embodiment can use data fusion algorithms such as Kalman Filter to synchronously process and fuse data from various sensors.

[0069] The Kalman filter state prediction formula is as follows:

[0070] The Kalman filter covariance prediction formula is:

[0071] The Kalman filter gain formula is:

[0072] The Kalman filter state update formula is:

[0073] By fusing multi-source data, the Kalman filter algorithm can estimate the aircraft's state (such as attitude, speed, thrust, etc.) in real time and accurately identify changes in the bamboo structure. This data fusion process can effectively improve the flight control system's ability to adapt to differences in natural materials such as bamboo.

[0074] In step S102, the dynamic parameters of the constituent materials are calculated based on multi-source sensing data.

[0075] Optionally, in one embodiment of this application, calculating the dynamic parameters of the constituent materials includes: extracting the geometric and surface features of the aircraft constituent materials based on lidar point cloud data and visual image sequence data from multi-source sensing data, so as to determine the surface defect distribution factor of the aircraft constituent materials based on the features; calculating the three-dimensional density matrix of the material based on light projection intensity and infrared thermal image data from multi-source sensing data; and calculating the dynamic parameter vector of the constituent materials based on the surface defect distribution factor and the three-dimensional density matrix.

[0076] The formula for calculating the dynamic parameter vector can be set as follows:

[0077]

[0078]

[0079] in, This represents an inertial tensor containing off-diagonal coupling terms. Indicates the actual location of the center of mass. This represents the operator for constructing antisymmetric matrices. Indicates nominal stiffness, Indicates equivalent stiffness. Represents the surface defect distribution factor. This represents a vector of dynamic parameters of the materials that make up the aircraft. This represents the numbering of each part / node in the density field during the discrete integration process. Indicates the first Density values ​​at each node Indicates the first Each node relative to the centroid Location, This represents the stiffness correction factor.

[0080] This embodiment can integrate multi-source sensing data such as lidar point clouds, visual image sequences, light projection intensity, and infrared thermal images to first extract the geometric and surface features of the materials constituting the aircraft and determine the surface defect distribution factor. Then, based on the light projection intensity and infrared thermal image data, the three-dimensional density matrix of the material is calculated. Finally, by combining the two, the material dynamic parameter vector can be accurately calculated, thereby comprehensively and efficiently obtaining information on the mechanical properties and defect correlation of the material, providing data support for the performance evaluation of aircraft materials.

[0081] Specifically, in actual implementation, this embodiment can further determine the corresponding dynamic parameter vector by performing differential modeling on the structure of the natural materials constituting the aircraft.

[0082] As a feasible approach, the aforementioned multi-source sensing data can be used to construct a high-precision mathematical model that includes the geometry, mass, and mechanical properties of bamboo. This modeling process can be divided into three levels, such as... Figure 2 As shown in region 2, each level integrates specific signal processing algorithms and physical inversion models.

[0083] Level 1. Geometric and Surface Feature Extraction Module Function Description: Using visual sensors and LiDAR data, the three-dimensional geometric envelope of bamboo is reconstructed, and surface defects are quantified using image processing algorithms.

[0084] Module input: LiDAR point cloud set With visual image sequences . Detailed implementation method: i) Feature extraction algorithm: Scale-invariant feature transform (SIFT) or accelerated robust feature (SURF) algorithm is used to extract texture feature points on the bamboo surface for image registration and stitching.

[0086] ii) Surface defect quantification: Cracks and texture abrupt changes are identified using the image gradient method. The texture gradient calculation formula is as follows:

[0087] in, For pixel grayscale values, The modulus can reflect the details of the surface texture and the edges of cracks.

[0088] Module output: Bamboo geometric volume and surface defect distribution factor (Dimensionless normalized parameter, 0 indicates intact, 1 indicates severe defect).

[0089] The geometric volume of bamboo can be obtained by 3D reconstruction of the fused data of lidar point cloud and visual image sequence, and the surface defect distribution factor can be obtained by calculating the magnitude of the image gradient and performing normalization.

[0090] Level 2. Internal Non-Uniform Density Field Mapping Module Function Description: This function addresses the issues of bamboo's "hard exterior and soft interior" properties and uneven distribution of bamboo nodes by establishing an internal density distribution model.

[0091] Module input: X-ray transmission intensity matrix With infrared thermal imaging data. Detailed implementation method: i) Density inversion model: Based on the Beer-Lambert Law, a mapping relationship between transmission intensity and material density is established.

[0093]

[0094] in Initial X-ray intensity, The absorption coefficient of bamboo is given by the formula, which can be used to invert a two-dimensional X-ray image into a three-dimensional voxel density matrix.

[0095] ii) Crack propagation prediction: Combine infrared data with fracture mechanics models to assess stress concentration in the microcrack region.

[0096]

[0097] Module output: 3D density matrix This includes spatial coordinates and local density values. .

[0098] Level 3. Dynamic Parameter Calculation Module Function Description: Converts geometric and density information into rigid body dynamic parameters required for aircraft flight control algorithms.

[0099] Module input: 3D density matrix With defect distribution factor . Detailed implementation method: i) Centroid offset calculation: The actual centroid position is calculated by weighted summation of voxel densities. This parameter will serve as the basis for subsequent thrust allocation adjustments.

[0101] ii) Calculation of rotational inertia tensor: Based on the actual center of mass position, calculate the inertia tensor containing off-diagonal coupling terms. The formula is described as a weighted discrete integral over the density field:

[0102] in, Operators for constructing antisymmetric matrices.

[0103] iii) Equivalent stiffness correction: Correcting nominal stiffness based on defect factor :

[0104] Module output: Bamboo physical parameter vector This vector will be directly input into the flight control system.

[0105] This embodiment can accurately identify the differences in the external and internal structure of bamboo through multi-source sensing elements (such as visual sensors, X-rays, lidar, etc.), including surface texture, cracks, density distribution, and microcracks, and then determine the physical parameter vector of the material. This information can further provide a precise basis for subsequent structural modeling of aircraft components (such as bamboo) and adjustment of flight control algorithms, so as to solve the technical drawbacks caused by the inhomogeneity of natural materials in traditional technologies.

[0106] In step S103, the dynamic parameters are input into the pre-built aircraft dynamic model, and the tracking error of the aircraft is calculated by comparing the real-time state data of the aircraft with the expected trajectory.

[0107] The real-time status data of the aircraft can be acquired in real time by corresponding sensors, and may include, but is not limited to: real-time position data (such as three-dimensional rectangular coordinates, geographic coordinates), real-time velocity data (such as linear velocity, relative ground velocity, relative air velocity), real-time attitude data (such as pitch angle, yaw angle), etc., without specific limitations. Furthermore, the expected trajectory can be understood as setting a starting point and an ending point before the aircraft performs its mission, using a target aircraft trajectory model (corresponding to...). Figure 2 The reference trajectory generated by the reference model shown is not entirely in line with the expected trajectory during flight due to material deviations, which leads to the tracking error of the aircraft in this application.

[0108] Optionally, in one embodiment of this application, the pre-built aircraft dynamics model is as follows:

[0109] in, and Indicates the nominal model. Represents the set of uncertain terms. This indicates the amount of compensation to be controlled. This indicates the update rate of the aircraft's status.

[0110] Specifically, this section of the embodiments will detail how the flight control system utilizes the aforementioned output physical parameters. An adaptive control architecture is used to compensate for the dynamic uncertainties caused by individual differences in bamboo materials.

[0111] The adaptive control architecture can be configured to include four parts: construction of a dynamic model with uncertainties, design of an adaptive control law, adaptive parameter update law, and thrust allocation reconstruction based on centroid reconstruction.

[0112] Regarding the construction of dynamic models with uncertainties: To describe the impact of differences in bamboo structure on flight behavior, this embodiment establishes a nonlinear system equation that includes a "nominal model" and "structural uncertainties," describing the dynamic behavior of the quadcopter UAV in the following state-space form:

[0113] in: (State variables): include position, velocity, attitude, and angular velocity vectors; , (Nominal Model): Ideal dynamic equations derived based on standard industrial materials (completely homogeneous and symmetrical).

[0114] (Lumped uncertainty term): This term represents the uncertainty arising from the physical parameters of bamboo. The core objective of this algorithm is to estimate and offset the deviation from the nominal model in flight state (tracking error) caused by factors such as inertial product coupling, center of gravity offset moment, and vibration caused by insufficient stiffness.

[0115] Using the aforementioned technical means, this application embodiment describes the impact of bamboo structural differences on flight status by establishing a nonlinear system equation containing a "nominal model" and "structural uncertainty terms" as a dynamic model. Then, based on the calculated dynamic parameters of the aircraft's constituent materials, the model calculates in real time the tracking error of the aircraft's flight status caused by the dynamic parameters; it also estimates and cancels out structural uncertainty terms in real time, thereby accurately quantifying the impact of bamboo structural differences on flight status and improving the aircraft's precise control and error compensation capabilities for flight status.

[0116] In step S104, the adaptive weight of structural damping is calculated based on the tracking error, and the total command torque of the aircraft is determined according to the weight.

[0117] In the embodiments of this application, this step can correspond to the design of adaptive control laws and the adaptive update law of parameters in an adaptive control architecture.

[0118] Specifically, regarding the parameter adaptive update law: To address the slow changes in bamboo properties with environmental factors (such as humidity) and potential noise interference from sensors, a parameter update law with "error-driven" and "robust damping" characteristics is designed, employing adaptive weighting. The dynamic adjustment rate can be set and described by the following differential equation:

[0119] in, This is a learning term, derived from the tracking error. Driven by this factor, when the actual trajectory of the system deviates from the preset trajectory, this value increases, prompting the weights to... Rapid updates to "learn" about the structural characteristics of bamboo. For robust damping / leakage term, this term introduces a negative feedback mechanism. (The positive damping coefficient) has the physical meaning of preventing the adaptive parameters from "parameter drift" or diverging infinitely when faced with sudden disturbances or measurement noise, while ensuring error convergence, thus ensuring the bounded stability of the flight control system when the bamboo structure has high uncertainty.

[0120] Furthermore, regarding the design of adaptive control laws: To achieve the goal of dealing with uncertainties The compensation can be designed in this embodiment using a composite control law of the following form:

[0121] in, This can be understood as the total command torque. This represents the baseline control quantity, which can be understood as the baseline controller. It uses classic algorithms such as PID or LQR and is responsible for the stability of the nominal model. This represents the compensation control quantity, which can be understood as an adaptive compensator, utilizing an adaptive parameter matrix. Approximating the uncertain terms:

[0122] in, This is the regression vector.

[0123] After obtaining the controlled compensation amount Afterwards, it needs to be brought back middle, yes The correction term can then be calculated. .

[0124] In summary, the control logic of this technical solution can be summarized as follows: The weights are determined based on the adaptive update rate of the parameters → the adaptive parameter matrix is ​​obtained → compensation is performed using an adaptive control law. Specifically: 1. Calculate the update rate: The system first calculates the update rate based on the tracking error. Using the formula of the parameter adaptive update law, that is Calculate the change in the current parameters.

[0125] 2. Determine the parameter matrix: The system updates and determines the current adaptive parameter matrix. The adaptive weights and the adaptive parameter matrix refer to the same mathematical object.

[0126] 3. Compensation Implementation: The system uses the updated parameter matrix, substitutes it into the adaptive idling rate formula, and calculates the specific compensation control quantity. This amount can be used to offset the uncertainty caused by differences in bamboo structure.

[0127] In step S105, the motor speed of the aircraft is calculated based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

[0128] Optionally, in one embodiment of this application, the formula for calculating the motor speed of the aircraft is:

[0129]

[0130]

[0131]

[0132] in, Represents the regression vector. Indicates the learning item, Indicates tracking error. Indicates adaptive weights, Indicates the positive damping coefficient. Indicates the compensation control amount. Indicates the total commanded torque. Indicates the baseline control quantity. Indicates the motor speed. Represents the thrust distribution matrix. This indicates the offset of the center of gravity. This represents the dynamic update rate of the weight matrix of the neural network used.

[0133] In actual implementation, this step corresponds to the thrust allocation reconfiguration part based on centroid reconfiguration in the adaptive control architecture.

[0134] Specifically, this embodiment can be based on the above-mentioned adaptive parameters (specifically referring to the physical parameters of bamboo) to... ), thrust distribution matrix Based on the real-time calculated center of gravity offset Perform dynamic refactoring:

[0135] When the algorithm detects that the density of one side of the bamboo is higher, causing the center of gravity to deviate from the geometric center, it will automatically increase the basic thrust offset of the motor on that side, thereby achieving "hardware defects compensated by software" at the underlying physical level.

[0136] Output It is the specific execution instruction after being mapped by the allocation matrix. It directly corresponds to the target speed of the four rotor motors, that is, the calculation result is converted into motor execution signals that are adapted to the current physical center of gravity.

[0137] The thrust distribution matrix is ​​constructed based on the general rigid body dynamics equations (Newton-Euler equations) for multi-rotor aircraft, establishing a mapping relationship between motor thrust and the resultant force / torque of the airframe. However, existing general formulas typically assume uniform mass distribution of the aircraft, resulting in a constant distribution matrix. This application's embodiment addresses the problem of uneven bamboo structure causing the center of gravity to deviate from the geometric center by further improving this general formula: modifying the originally fixed lever arm parameter in the matrix to vary with the center of gravity offset. Changing variables.

[0138] Through the above-mentioned technical means, the embodiments of this application can dynamically adjust the control parameters in the flight control algorithm according to the differences in bamboo structure, so as to ensure that the drone made of natural renewable materials (such as bamboo) can still fly stably in the case of bamboo with uneven structure, thereby improving the stability and safety of the aircraft.

[0139] The technical effects of the aforementioned flight control method also include: Enhancing the adaptability of drones to differences in natural materials: By adaptively fine-tuning the parameters of the flight control system, drones can maintain consistent flight performance across different individual bamboo specimens, thereby avoiding flight instability or performance degradation caused by material differences.

[0140] Optimize the control accuracy and robustness of the flight control system: By combining real-time data feedback with updates to the bamboo structure model, the control accuracy and robustness of the flight control system can be enhanced, ensuring that the UAV can be precisely controlled when dealing with differences in natural materials, thereby improving flight performance.

[0141] Promoting the application of natural materials such as bamboo in high-tech fields: Providing technical support for the application of natural materials such as bamboo in high-precision fields (such as drones, spacecraft, etc.), and promoting the high-value-added utilization of green and environmentally friendly materials, has important environmental and social significance.

[0142] The flight control method for an aircraft proposed in this application can collect external and internal structural information of bamboo through multiple sensors, identify different characteristics of bamboo based on the acquired data, and ensure stable operation of the aircraft among different individual bamboo samples by adaptively fine-tuning the control parameters in the flight control algorithm. This solves the problem in related technologies where the structural differences in the natural materials constituting the aircraft lead to the inability to guarantee the stability and safety of the aircraft.

[0143] Next, refer to the appendix. Figure 3 This application describes a flight control device for an aircraft according to an embodiment.

[0144] Figure 3 This is a block diagram of the flight control device of an aircraft according to an embodiment of this application.

[0145] like Figure 3 As shown, the flight control device 10 of the aircraft includes: an acquisition module 100, a first calculation module 200, a second calculation module 300, a third calculation module 400, and a control module 500.

[0146] The acquisition module 100 is used to acquire multi-source sensing data of the materials constituting the target aircraft.

[0147] The first calculation module 200 is used to calculate the dynamic parameters of the constituent materials based on multi-source sensing data.

[0148] The second calculation module 300 is used to input dynamic parameters into a pre-built aircraft dynamics model, and calculate the tracking error of the aircraft by comparing the real-time state data of the aircraft with the expected trajectory.

[0149] The third calculation module 400 is used to calculate the adaptive weight of structural damping based on the tracking error, and determine the total command torque of the aircraft according to the weight.

[0150] The control module 500 is used to calculate the motor speed of the aircraft based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

[0151] Optionally, in one embodiment of this application, the first calculation module 200 includes: a geometric and surface feature extraction module, an internal non-uniform density field mapping module, and a dynamic parameter calculation module; wherein, the geometric and surface feature extraction module is used to extract the geometric and surface features of the aircraft constituent materials based on lidar point cloud data and visual image sequence data in multi-source sensing data, so as to determine the surface defect distribution factor of the aircraft constituent materials based on the features; the internal non-uniform density field mapping module is used to calculate the three-dimensional density matrix of the material based on light projection intensity and infrared thermal image data in multi-source sensing data; the dynamic parameter calculation module is used to calculate the dynamic parameter vector of the constituent materials based on the surface defect distribution factor and the three-dimensional density matrix.

[0152] Optionally, in one embodiment of this application, the formula for calculating the dynamic parameter vector is:

[0153]

[0154]

[0155] in, This represents an inertial tensor containing off-diagonal coupling terms. Indicates the actual location of the center of mass. This represents the operator for constructing antisymmetric matrices. Indicates nominal stiffness, Indicates equivalent stiffness. Represents the surface defect distribution factor. This represents a vector of dynamic parameters of the materials that make up the aircraft. This represents the numbering of each part / node in the density field during the discrete integration process. Indicates the first Density values ​​at each node Indicates the first Each node relative to the centroid Location, This represents the stiffness correction factor.

[0156] Optionally, in one embodiment of this application, the pre-built aircraft dynamics model is as follows:

[0157] in, and Indicates the nominal model. Represents the set of uncertain terms. This indicates the amount of compensation to be controlled. This indicates the update rate of the aircraft's status.

[0158] Optionally, in one embodiment of this application, the formula for calculating the motor speed of the aircraft is:

[0159]

[0160]

[0161]

[0162] in, Represents the regression vector. Indicates the learning item, Indicates tracking error. Indicates adaptive weights, Indicates the positive damping coefficient. Indicates the compensation control amount. Indicates the total commanded torque. Indicates the baseline control quantity. Indicates the motor speed. Represents the thrust distribution matrix. This indicates the offset of the center of gravity. This represents the dynamic update rate of the weight matrix of the neural network used.

[0163] It should be noted that the foregoing explanation of the flight control method embodiment for the aircraft also applies to the flight control device of the aircraft in this embodiment, and will not be repeated here.

[0164] The flight control device for an aircraft proposed in this application can collect external and internal structural information of bamboo through various sensors, identify different characteristics of bamboo based on the acquired data, and ensure stable operation of the aircraft among different individual bamboo samples by adaptively fine-tuning the control parameters in the flight control algorithm. This solves the problem in related technologies where the structural differences in the natural materials constituting the aircraft lead to the inability to guarantee the stability and safety of the aircraft.

[0165] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0166] When the processor 402 executes the program, it implements the flight control method for the aircraft provided in the above embodiments.

[0167] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0168] The memory 401 is used to store computer programs that can run on the processor 402.

[0169] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0170] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0171] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0172] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0173] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the flight control method for the aircraft described above.

[0174] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the flight control method for the aircraft described above.

[0175] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0176] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0177] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0179] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0180] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0181] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0182] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A flight control method for an aircraft, characterized in that, Includes the following steps: Acquire multi-source sensing data on the constituent materials of the target aircraft; The dynamic parameters of the constituent materials are calculated based on the multi-source sensing data. The dynamic parameters are input into a pre-built aircraft dynamics model, and the tracking error of the aircraft is calculated by comparing the real-time state data of the aircraft with the expected trajectory. The adaptive weights of the structural damping are calculated based on the tracking error, and the total command torque of the aircraft is determined according to the weights. The motor speed of the aircraft is calculated based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

2. The method of claim 1, wherein, The calculation of the dynamic parameters of the constituent materials includes: Based on the lidar point cloud data and visual image sequence data in the multi-source sensing data, the geometric and surface features of the aircraft constituent materials are extracted, and the surface defect distribution factor of the aircraft constituent materials is determined based on the features. Based on the light projection intensity and infrared thermal imaging data in the multi-source sensing data, the three-dimensional density matrix of the material is calculated; The dynamic parameter vector of the constituent material is calculated based on the surface defect distribution factor and the three-dimensional density matrix.

3. The method of claim 2, wherein, The formula for calculating the dynamic parameter vector is: in, This represents an inertial tensor containing off-diagonal coupling terms. Indicates the actual location of the center of mass. This represents the operator for constructing antisymmetric matrices. Indicates nominal stiffness, Indicates equivalent stiffness. Represents the surface defect distribution factor. This represents a vector of dynamic parameters of the materials that make up the aircraft. This represents the numbering of each part / node in the density field during the discrete integration process. Indicates the first Density values ​​at each node Indicates the first The position of each node relative to the actual centroid. This represents the stiffness correction factor.

4. The method of claim 1, wherein, The pre-constructed aircraft dynamics model is as follows: in, and Indicates the nominal model. Represents the set of uncertain terms. This indicates the amount of compensation to be controlled. This indicates the update rate of the aircraft's status.

5. The method according to claim 1, characterized in that, The formula for calculating the motor speed of the aircraft is: in, Represents the regression vector. Indicates the learning item, Indicates tracking error. Indicates adaptive weights, Indicates the positive damping coefficient. Indicates the compensation control amount. Indicates the total commanded torque. Indicates the baseline control quantity. Indicates the motor speed. Represents the thrust distribution matrix. This indicates the offset of the center of gravity. This represents the dynamic update rate of the weight matrix of the neural network used.

6. A flight control device for an aircraft, characterized in that, include: The acquisition module is used to acquire multi-source sensing data on the constituent materials of the target aircraft. The first calculation module is used to calculate the dynamic parameters of the constituent material based on the multi-source sensing data; The second calculation module is used to input the dynamic parameters into the pre-built aircraft dynamic model and calculate the tracking error of the aircraft by comparing the real-time state data of the aircraft with the expected trajectory. The third calculation module is used to calculate the adaptive weight of the structural damping based on the tracking error, and determine the total command torque of the aircraft according to the weight; The control module is used to calculate the motor speed of the aircraft based on the total command torque, so as to control the flight of the aircraft according to the motor speed.

7. The apparatus of claim 6, wherein, The first computing module includes: The geometric and surface feature extraction module is used to extract the geometric and surface features of the aircraft constituent materials based on the lidar point cloud data and visual image sequence data in the multi-source sensing data, so as to determine the surface defect distribution factor of the aircraft constituent materials based on the features. An internal non-uniform density field mapping module is used to calculate the three-dimensional density matrix of the material based on the light projection intensity and infrared thermal image data in the multi-source sensing data. The dynamic parameter calculation module is used to calculate the dynamic parameter vector of the constituent material based on the surface defect distribution factor and the three-dimensional density matrix.

8. An electronic device, comprising: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the flight control method for an aircraft as described in any one of claims 1-5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the flight control method for the aircraft as described in any one of claims 1-5.

10. A computer program product, characterised in that, The computer program is executed to implement the flight control method for the aircraft as described in any one of claims 1-5.

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