Graphical modeling and simulation platform for aircraft control system

Abstract

The invention discloses an aircraft control system graphical modeling and simulation platform, and relates to the technical field of flight control. The platform comprises a data processing module used for acquiring and preprocessing system related data of the flight control system; the model building module is used for carrying out framework management, configuration parameter setting and module selection on the basis of a flight control overall framework model and a flight control professional model library and generating a flight control system model according to the framework management, the configuration parameter setting and the module selection; the quality evaluation module is used for evaluating and optimizing a simulation result of the flight control system model; and the simulation test module is used for carrying out simulation test on the flight control system in each test period. A simulation platform for simulating operation is provided for the flight control system, full-process functions such as data preprocessing, part generation, rapid model construction and rapid system evaluation are achieved, and the development efficiency, robustness, safety and accuracy of the flight control system are improved; and practical guidance is provided for actual operation of the aircraft.

Description

Technical Field

[0001] The present application belongs to the field of flight control technology, and in particular, relates to a graphical modeling and simulation platform for aircraft control systems. Background Art

[0002] The Flight Control System (FCS) is a key component in aircraft (such as airplanes, aircraft, missiles, etc.), responsible for managing and adjusting the attitude, speed, direction and trajectory of the aircraft to ensure stable and safe flight, and to be able to perform various tasks in complex flight environments. The core task of the flight control system is to enable the aircraft to fly according to the predetermined target trajectory and attitude through precise adjustment of various controllers of the aircraft (such as control surfaces, engine thrust, etc.). With the continuous improvement of aircraft performance requirements, especially in the fields of aircraft, military missiles and commercial aircraft, the complexity and accuracy of flight control systems are also constantly improving. Therefore, effective improvement of flight control systems has become a key factor in improving the performance of various aircraft.

[0003] On the other hand, the design of flight control systems also faces multiple challenges, such as: nonlinearity and complexity: the dynamic system of aircraft is usually highly nonlinear, especially under high-speed and complex flight conditions. How to maintain stability and responsiveness in these complex environments is one of the difficulties in design; real-time: the flight control system needs to perform real-time calculations and control. Sensor data acquisition, control command generation, actuator adjustment, etc. all require the flight control system to respond within milliseconds; robustness and adaptability: the flight control system needs to have good adaptability to changes in the flight environment (such as airflow, air pressure, etc.) and hardware failures. The design must ensure that the system can remain stable even under partial failures or complex disturbances; safety and redundancy: the safety of the aircraft system is crucial. The flight control system needs to have a redundant design to ensure that when some components fail, it can automatically switch to the backup system to avoid flight accidents. In summary, how to model and simulate the flight control system has become a key issue that needs to be solved to improve the efficiency, robustness, safety and accuracy of flight control system development. Summary of the invention

[0004] The purpose of this application is to provide a graphical modeling and simulation platform for aircraft control systems. The purpose of this application can be achieved through the following technical solutions: The present application provides an aircraft control system graphical modeling and simulation platform, including: A data processing module, used for acquiring system-related data of the flight control system and preprocessing the data, wherein the preprocessing includes aerodynamic data processing, state point selection, state point balancing and state point linearization; Model building module, which manages the architecture, sets configuration parameters and selects modules based on the overall architecture model of flight control and the professional model library of flight control, and generates a flight control system model. A quality assessment module, used to evaluate and optimize the simulation results of the flight control system model; the evaluation includes active flight control evaluation and automatic flight control evaluation; A simulation test module, used for performing simulation test on the flight control system model in each test cycle; Wherein, the data processing module includes a pneumatic data processing unit, a state point selection unit, a state point balancing unit, a state point linearization unit and a system data acquisition unit; The aerodynamic data processing unit is used to process and analyze the aerodynamic characteristic data of the aircraft; The state point selection unit is used to select a state point from a flight simulation or a flight experiment; The state point balancing unit is used to dig out and determine the control input of the aircraft so that the aircraft remains stable in the set flight state; The state point linearization unit is used to linearize the nonlinear motion equation of the aircraft near the state point; The system data acquisition unit includes an image processing subunit, a CFD model subunit and a system data extraction subunit; The image processing subunit is used to obtain video image data of the aircraft in different flight states and process the video image data, including: The video image data is obtained, specifically: Acquire a three-dimensional real scene image and perform smoothing processing: establish a corresponding image template on the three-dimensional real scene image, determine the pixels on the image template and calculate the average value of the pixels, and replace the original pixel value with the average value of the pixels; Perform image segmentation on the three-dimensional real scene image: determine a segmentation threshold value according to the three-dimensional real scene image and determine the average gray value of the three-dimensional real scene image; iteratively perform the segmentation step until the difference between the sum of the grayscale means of the two types of images to be segmented and the average grayscale value is within a preset range; the segmentation step includes: segmenting the three-dimensional real scene image into two types of images according to the determined segmentation threshold value, determining the grayscale means of the two types of images to be segmented, and the sum of their grayscale means; updating the current segmentation threshold value, and performing the segmentation step; segmenting the three-dimensional real scene image using the current segmentation threshold value; Performing image stitching on the segmented three-dimensional real scene images: obtaining the image to be stitched from the segmented three-dimensional real scene images, extracting feature points of the image to be stitched for matching; registering the image to be stitched by the feature points, and constructing a transformation matrix between the images to be stitched to complete the stitching of the three-dimensional real scene images; Fusing the stitched images, and using the fused images as the video image data; The video image data is processed, specifically: Acquire a grayscale image sequence of the video image data; Based on a preset clarity threshold, images with a clarity greater than the clarity threshold are selected from the grayscale image sequence as clear images to obtain a first type of clear image sequence; Determine the grayscale mean of the difference image of the difference image of two consecutive frames of clear images in the first type of clear image sequence, and select two frames of clear images whose grayscale means of the difference images are greater than the first image threshold; If the two selected clear image frames are adjacent in the grayscale image sequence, the two frames of images are retained in the first type of clear image sequence; if the two selected clear image frames are not adjacent in the grayscale image sequence, a blurred image between the two frames of images is obtained, and blurred images between all non-adjacent two frames of clear images are obtained to form a second type of blurred image sequence; forming a key image sequence according to the second type of blurred image sequence and the first type of clear image sequence; Inputting the key image sequence into a network model and outputting simulated flow field data of the aircraft; The CFD model subunit constructs a CFD model based on the simulated flow field data, and performs simulation calculations to obtain CFD simulation results; The system data extraction subunit extracts the system related data from the CFD simulation results; The aerodynamic characteristic data include lift, drag, pitch moment and roll moment of the aircraft; and the state points include attitude, speed and acceleration of the aircraft.

[0005] Preferably, the pneumatic data processing unit includes a data interpolation subunit, a pneumatic model fitting subunit, a control torque calculation subunit and a pneumatic nonlinear processing subunit; The data interpolation subunit is used to perform interpolation processing on the aerodynamic characteristic data under different flight states by using an interpolation method; The aerodynamic model fitting subunit is used to fit the aerodynamic model according to the measured aerodynamic measurement data to obtain more accurate aerodynamic characteristic data; wherein the fitting method used includes a polynomial fitting method and a piecewise linear fitting method; The control torque calculation subunit is used to calculate the control torque generated by the movement of the aircraft according to the aerodynamic model; The aerodynamic nonlinear processing subunit is used to process the aerodynamic nonlinear behavior of the aircraft under different flight conditions.

[0006] Preferably, the state point selection unit includes a flight state space division subunit and a key state point dynamic selection subunit; The flight state space division subunit is used to divide the flight space of the aircraft to form flight state subspaces with different working states according to the mission requirements and flight environment of the aircraft; The key state point dynamic selection subunit is used to dynamically select key state points in the flight state subspace; The key state points include the maximum lift point, the best cruising point and the boundary state point.

[0007] Preferably, the state point balancing unit includes a control input balancing subunit, a stability analysis subunit and a boundary condition processing subunit; The control input balancing subunit is used to adjust the initial input of each control surface according to the aerodynamic model of the aircraft to keep the aircraft in a balanced state; The stability analysis subunit is used to analyze the balance and stability of the aircraft in a specific state or in the absence of external disturbances; The boundary condition processing subunit is used to process and balance the flight state of the aircraft under boundary conditions to verify the impact of extreme conditions on the aircraft.

[0008] Preferably, the state point linearization unit includes a linearized dynamics equation subunit, a Jacobian matrix calculation subunit, a linearized control design subunit and an error analysis subunit; The linearized dynamic equation subunit is used to perform Taylor expansion on the nonlinear dynamic equation of the aircraft near the selected state point to obtain an approximate linear system model; The Jacobian matrix calculation subunit is used to calculate the Jacobian matrix in the state equation to obtain a linearized system dynamic matrix; wherein the system dynamic matrix is ​​used to describe the small disturbance response of the aircraft under a specific state; The linearization control design subunit is used to design a controller through the linear system model after linearization; The error analysis subunit is used to evaluate the applicability of the linear system model and analyze the impact of the linearization error on the control performance of the controller.

[0009] Preferably, the model building module includes an architecture management unit, a parameter setting unit and a module selection unit; The architecture management unit is used to select the system control architecture and divide the flight control system model into multiple functional modules; it is also used to design interfaces between different functional modules and define the data transmission method and control signal interaction method between functional modules; The parameter setting unit is used to adjust the gain parameters of the controller and configure the dynamic parameters of the aircraft; it is also used to set environmental parameters, filter parameters and signal processing parameters; The module selection unit is used to select functional modules according to actual needs, and connect and integrate the selected functional modules through appropriate interfaces to form a complete flight control system model; Wherein, the functional modules include a sensor module, a controller module and an actuator module.

[0010] Preferably, the quality assessment module includes an active flight control assessment unit, an automatic flight control assessment unit and an assessment optimization unit; The active flight control evaluation unit is used to evaluate the performance of the flight control system model in the active control mode, specifically: real-time monitoring of the flight performance of the aircraft in the flight control system model, and feedback and adjustment of the control of the aircraft; The automatic flight control evaluation unit is used to evaluate the performance of the flight control system model in the automatic control mode, specifically: evaluating the tracking of the autonomous flight path, the completion of the autonomous flight mission, and the fault detection during autonomous flight; The evaluation and optimization unit is used to analyze the evaluation results of the flight control system model to generate optimization suggestions, and optimize the control strategy and system parameters based on the evaluation results.

[0011] Preferably, the simulation test module includes a flight control unit, an actuator model and a navigation unit; The flight control unit sends the actuator data of the second test cycle to the actuator model based on the control instruction and the navigation data of the aircraft in the first test cycle output by the navigation unit; the navigation data of the aircraft in the first test cycle includes: at least one of the first position, the first attitude and the first speed of the aircraft; the actuator data is used to represent the kinetic energy parameters required to be output for executing the control instruction; the second test cycle is the next test cycle of the first test cycle; The actuator model determines the operating parameters of the power element of the aircraft based on the actuator data of the second test cycle of the flight control unit, and sends the operating parameters to the aircraft model; the actuator model is obtained based on fitting the real actuator data and the real power parameters; The navigation unit determines the navigation data of the aircraft in the second test cycle based on the sensor readings, and the navigation data of the aircraft in the second test cycle includes: at least one of the second position, second attitude and second speed of the aircraft; the navigation unit is used to simulate the guidance of the aircraft in any test cycle of the aircraft.

[0012] Preferably, the simulation test module also includes an aircraft model and a sensor model; The aircraft model acquires simulated state data of the aircraft based on the operating parameters, and sends the simulated state data to the sensor model; the simulated state data includes at least one of the calculated position, calculated attitude, calculated velocity, calculated acceleration and calculated angular velocity of the aircraft; The sensor model determines a sensor reading of at least one sensor based on the simulated state data and sends the sensor reading to the navigation unit.

[0013] Preferably, the aircraft model comprises: The attitude sub-model obtains the calculated angular velocity of the aircraft based on the spiral torque and the moment of inertia of the aircraft and calculates the Euler angle of the aircraft based on the calculated angular velocity of the aircraft, wherein the Euler angle is used to represent the calculated attitude of the aircraft; The velocity sub-model obtains the rotation matrix of the aircraft based on the Euler angles; obtains the calculated acceleration of the aircraft based on the rotation matrix and the operating parameters, and then obtains the calculated velocity of the aircraft based on the acceleration; The position sub-model obtains the wind speed in the second test cycle based on the position of the aircraft in the first test cycle; and obtains the calculated position of the aircraft based on the wind speed in the second test cycle and the calculated speed.

[0014] The beneficial effects of the present application are as follows: the present application provides a simulation platform for the flight control system that can perform simulation operations, realizes full-process functions such as preprocessing of system-related data, generation of components, rapid construction of models, and rapid evaluation of system performance, greatly improves the efficiency, robustness, safety, accuracy and stability of flight control system development, and also provides practical guidance for the actual operation of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0016] Figure 1 A schematic diagram of the structure of a graphical modeling and simulation platform for an aircraft control system provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a data processing module provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a quality assessment module provided in an embodiment of the present application; Figure 4 A flowchart of the steps of a method for graphical modeling and simulation of an aircraft control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0018] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to any or all possible combinations of one or more associated listed items.

[0019] The specific implementation methods, features and functions of the present application are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0020] Example 1 See also Figure 1 The present application provides an aircraft control system graphical modeling and simulation platform, including: The data processing module is used to obtain system-related data of the flight control system and preprocess it. The preprocessing includes aerodynamic data processing, state point selection, state point balancing and state point linearization.

[0021] The model building module performs architecture management, configuration parameter settings, and module selection based on the flight control overall architecture model and the flight control professional model library, and generates a flight control system model.

[0022] The quality assessment module is used to evaluate and optimize the simulation results of the flight control system model. The assessment includes active flight control assessment and automatic flight control assessment.

[0023] The simulation test module is used to perform simulation tests on the flight control system model in each test cycle.

[0024] Specifically, since there are many problems in the design of the existing flight control system, which will greatly affect the simulation effect and practical application, in order to improve the simulation effect, this application uses the MBDA-4FC (MBDA is a simulation platform for aircraft, 4FC is the version number of MBDA) platform to graphically model and simulate the flight control system, so as to establish a simulation platform for the flight control system. Starting from the needs of users, the full process functions of preprocessing system-related data, generating components, rapidly building models, and rapidly evaluating system performance are realized, and finally the flight control system (prototype) model is obtained to guide engineering practice, which specifically includes the following contents: First, the system-related data of the flight control system is obtained and preprocessed through the data processing module, including aerodynamic data processing, state point selection, state point balancing and state point linearization. Secondly, the model construction module is used to manage the architecture, set configuration parameters and select modules based on the overall architecture model of the flight control and the professional model library of the flight control, and the flight control system model is generated. Then, the simulation results of the above-mentioned flight control system model are evaluated and optimized through the quality evaluation module, including active flight control evaluation and automatic flight control evaluation. Finally, the simulation test module is used to simulate the flight control system model in each simulation test cycle. Through the above content, this application provides a simulation platform for the flight control system that can perform simulation operations, greatly improving the efficiency, robustness, safety, accuracy and stability of the flight control system development. At the same time, it also provides practical guidance for the actual operation of the aircraft.

[0025] like Figure 2 As shown, in one embodiment provided in the present application, the data processing module includes a pneumatic data processing unit, a state point selection unit, a state point balancing unit, a state point linearization unit and a system data acquisition unit.

[0026] The system data acquisition unit acquires system-related data of the flight control system based on image processing technology and CFD models. The system-related data includes aerodynamic characteristic data and state point data.

[0027] The aerodynamic data processing unit is used to process and analyze the aerodynamic characteristics data of the aircraft.

[0028] The state point selection unit is used to select state points from flight simulation or flight experiment.

[0029] The state point balancing unit is used to explore and determine the control input of the aircraft so that the aircraft remains stable in the set flight state.

[0030] The state point linearization unit is used to linearize the nonlinear motion equation of the aircraft near the state point.

[0031] Among them, aerodynamic characteristic data include the lift, drag, pitch moment and roll moment of the aircraft.

[0032] The state points include the attitude, velocity and acceleration of the aircraft.

[0033] Specifically, the data processing module of this embodiment can realize the preprocessing of system-related data of the flight control system, including an aerodynamic data processing unit, a state point selection unit, a state point balancing unit, a state point linearization unit and a system data acquisition unit, specifically: the system-related data is acquired through the system data acquisition unit, and the aerodynamic characteristic data of the aircraft is processed and analyzed through the aerodynamic data processing unit. Then, the state point selection unit selects a suitable state point from the flight simulation or flight experiment. Then, the control input of the aircraft is mined through the state point balancing unit, so that the aircraft can maintain stable flight under the set flight state (such as a certain speed, angle of attack, pitch angle, etc.). Finally, the nonlinear motion equation of the aircraft is linearized near a certain state point through the state point linearization unit, which is convenient for the subsequent linear controller design and analysis. In this embodiment, the above-mentioned aerodynamic characteristic data include the lift, drag, pitch moment and roll moment of the aircraft.

[0034] The status points include the attitude (pitch angle, roll angle, yaw angle, etc.), velocity and acceleration of the aircraft.

[0035] In an embodiment provided in the present application, the system data acquisition unit includes an image processing subunit, a CFD model subunit and a system data extraction subunit.

[0036] The image processing subunit is used to obtain video image data of the aircraft in different flight states and process the video image data, including: (1) Obtain video image data, specifically: Acquire a three-dimensional real scene image and perform smoothing: create a corresponding image template on the three-dimensional real scene image, determine the pixels on the image template and calculate the average value of the pixels, and replace the original pixel value with the average value of the pixels.

[0037] Perform image segmentation on the three-dimensional real scene image: determine the segmentation threshold according to the three-dimensional real scene image and determine the average grayscale value of the three-dimensional real scene image. Iterate the segmentation step until the difference between the sum of the grayscale means of the two types of images to be segmented and the average grayscale value is within a preset range. The segmentation step includes: segmenting the three-dimensional real scene image into two types of images according to the determined segmentation threshold, determining the grayscale means of the two types of images to be segmented, and the sum of their grayscale means. Update the current segmentation threshold and perform the segmentation step. Use the current segmentation threshold to segment the three-dimensional real scene image.

[0038] Image stitching of segmented 3D real scene images: obtain the image to be stitched from the segmented 3D real scene images, extract the feature points of the image to be stitched for matching. Register the image to be stitched through the feature points, and construct the transformation matrix between the images to be stitched to complete the stitching of the 3D real scene images.

[0039] The stitched images are fused and the fused images are used as video image data.

[0040] (2) Processing the video image data, specifically: Get a grayscale image sequence of video image data.

[0041] Based on a preset clarity threshold, images with a value greater than the clarity threshold are screened out from the grayscale image sequence as clear images, thereby obtaining a first type of clear image sequence.

[0042] Determine the grayscale mean of the difference image of two consecutive clear images in the first type of clear image sequence, and select two clear images whose grayscale means of the difference images are greater than the first image threshold.

[0043] If the two selected clear images are adjacent in the grayscale image sequence, the two images are retained in the first type of clear image sequence. If the two selected clear images are not adjacent in the grayscale image sequence, the blurred image between the two images is obtained, and the blurred images between all non-adjacent two clear images are obtained to form a second type of blurred image sequence.

[0044] It should be noted that the two consecutive clear image frames in the first type of clear image sequence may be adjacent or non-adjacent in the grayscale image sequence. When the two clear image frames are non-adjacent in the grayscale image sequence, there is still a blurred image between the two non-adjacent clear image frames. Since the grayscale mean of the difference image of the difference image of the two non-adjacent clear images is greater than the first image threshold, the two non-adjacent clear images belong to the images that need to be selected. Then, the blurred image between the two non-adjacent clear images may also be the image that needs to be selected. Therefore, the embodiment of the present application regards the blurred images between all the non-adjacent two clear images in the non-adjacent situation as the second type of blurred image sequence. In addition, the first image threshold is the mean corresponding to the median of the grayscale mean of the difference image of each difference image after sorting in order from small to large.

[0045] A key image sequence is formed according to the second type of blurred image sequence and the first type of clear image sequence.

[0046] The key image sequence is input into the network model, and the simulated flow field data of the aircraft is output.

[0047] The CFD model subunit builds a CFD model based on the simulated flow field data and performs simulation calculations to obtain CFD simulation results.

[0048] The system data extraction subunit extracts system related data from CFD simulation results.

[0049] Specifically, since the system-related data of the flight control system plays a very important role in the subsequent flight control system model, and the acquisition of system-related data in the prior art is relatively complex and inaccurate, the accuracy of subsequent data processing is reduced. Therefore, in order to improve the accuracy of system-related data acquisition and data processing, this embodiment combines image processing technology with CFD (Computational Fluid Dynamics) technology to provide accurate and reliable data for the design of the flight control system. In content (1), this embodiment obtains more comprehensive data by smoothing, image segmentation, image splicing and image fusion processing on the acquired three-dimensional real-scene image, thereby improving the quality and corresponding accuracy of image data processing. In content (2), the key image sequence is obtained by image processing the acquired video image data. Then, by improving the loss function of the network model, the state data of the aircraft in the key image sequence is accurately extracted and used as the subsequent simulation flow field data, and then the CFD model is used for simulation calculation and the system-related data is extracted from the simulation results, thereby improving the accuracy of obtaining the system-related data.

[0050] In an embodiment provided in the present application, the pneumatic data processing unit includes a data interpolation subunit, a pneumatic model fitting subunit, a control torque calculation subunit and a pneumatic nonlinear processing subunit.

[0051] The data interpolation subunit is used to perform interpolation processing on the aerodynamic characteristic data under different flight conditions through an interpolation method.

[0052] The aerodynamic model fitting subunit is used to fit the aerodynamic model according to the measured aerodynamic measurement data to obtain more accurate aerodynamic characteristic data. The fitting methods used include polynomial fitting method and piecewise linear fitting method.

[0053] The control torque calculation subunit is used to calculate the control torque generated by the control input on the aircraft movement according to the aerodynamic model.

[0054] The aerodynamic nonlinear processing subunit is used to process the aerodynamic nonlinear behavior of the aircraft under different flight conditions.

[0055] Specifically, the main function of the aerodynamic data processing unit of this embodiment is to process and analyze the aerodynamic characteristic data of the aircraft. These data usually come from wind tunnel experiments, flight tests or high-fidelity simulation models, including the lift, drag, pitch moment, rolling moment, etc. of the aircraft. The specific contents include: Data interpolation: interpolation processing of aerodynamic characteristic data under different flight conditions is performed by interpolation methods, especially aerodynamic characteristic data under flight conditions such as the speed, angle of attack, and attitude of the aircraft. Aerodynamic model fitting: fitting the aerodynamic model according to the measured data or theoretical model to obtain more accurate aerodynamic characteristic data, usually using methods such as polynomials and piecewise linearization. Calculation of control torque: Calculation of the influence of control input (such as ailerons, elevators, etc.) on the motion of the aircraft according to the aerodynamic model, and providing it to the subsequent control algorithm for use. Processing of aerodynamic nonlinearity: Considering the aerodynamic nonlinear behavior of the aircraft under different flight conditions, such as supersonic airflow at high speed and stall at low speed.

[0056] In an embodiment provided in the present application, the state point selection unit includes a flight state space division subunit and a key state point dynamic selection subunit.

[0057] The flight state space division subunit is used to divide the flight space of the aircraft to form flight state subspaces with different working states according to the mission requirements and flight environment of the aircraft.

[0058] The key state point dynamic selection subunit is used to dynamically select the key state point in the flight state subspace.

[0059] Among them, the key state points include the maximum lift point, the optimal cruising point and the boundary state point.

[0060] Specifically, the function of the state point selection unit of this embodiment is to select appropriate "state points" from flight simulation or experiment for system modeling and control design. State points usually include key parameters such as the attitude (pitch angle, roll angle, yaw angle, etc.), speed, acceleration, etc. of the aircraft. The specific contents include: Flight state space division: According to the mission requirements and flight environment of the aircraft, the flight space of the aircraft is divided to form a flight state subspace with different working states, providing data support for the subsequent selection of different working state points of the aircraft, such as climbing, cruising, and descending. Select key flight points: Select representative flight state points, such as maximum lift points, optimal cruising points, or boundary state points, for control system modeling and simulation. Dynamic state point selection: Dynamically select representative and challenging flight state points through simulation or experimental data to ensure that the designed flight control system model has good robustness and stability.

[0061] In an embodiment provided in the present application, the state point balancing unit includes a control input balancing subunit, a stability analysis subunit and a boundary condition processing subunit.

[0062] The control input balancing subunit is used to adjust the initial input of each control surface according to the aerodynamic model of the aircraft, so that the aircraft is in a balanced state under a certain flight state.

[0063] The stability analysis subunit is used to analyze the balance and stability of the aircraft under specific conditions or without external disturbances.

[0064] The boundary condition processing subunit is used to process and balance the flight state of the aircraft under boundary conditions to verify the impact of extreme conditions on the aircraft.

[0065] Specifically, the main function of the state point balancing unit of this embodiment is to make the aircraft achieve stable balance under a specific flight state. Balancing is a common step in aircraft simulation design, and its goal is to find the control input of the aircraft so that the aircraft can still remain stable under a specific state (such as a specific speed, angle of attack, pitch angle, etc.). The specific contents include: Control input balancing: adjust the initial input of each control surface (such as elevator, rudder, aileron, etc.) according to the aerodynamic model of the aircraft so that the aircraft is in a stable balance state under a certain flight state. Stability analysis: ensure that the stability requirements of the aircraft are met under a specific state, that is, the flight control system can maintain balance without external disturbances. Boundary condition processing: In the process of aircraft simulation design, the balancing unit needs to take into account the influence of extreme conditions (such as high angle of attack, large crosswind, etc.) and ensure that the aircraft can maintain balance under these conditions.

[0066] In one embodiment provided in the present application, the state point linearization unit includes a linearized dynamic equation subunit, a Jacobian matrix calculation subunit, a linearized control design subunit and an error analysis subunit.

[0067] The linearized dynamic equation subunit is used to perform Taylor expansion on the nonlinear dynamic equations of the aircraft near the selected state point to obtain an approximate linear system model.

[0068] The Jacobian matrix calculation subunit is used to calculate the Jacobian matrix in the state equation and obtain the linearized system dynamic matrix, which is used to describe the small disturbance response of the aircraft under a specific state.

[0069] The linearization control design subunit is used to design the controller through the linearized linear system model.

[0070] The error analysis subunit is used to evaluate the applicability of the linear system model and analyze the impact of the linearization error on the control performance of the controller.

[0071] Specifically, obtaining an approximate linear system model includes the following: (1) Define the nonlinear dynamic equations of the aircraft: The nonlinear dynamics of the aircraft can usually be expressed as follows: .

[0072] in, is the derivative of the state, which indicates the change of the state over time, is the state vector of the aircraft (such as position, velocity, attitude, etc.), are control inputs (such as rudder angle, thrust, etc.), is a and A nonlinear function that describes the dynamic behavior of the vehicle.

[0073] (2) Select the state point: Assume that linearization is performed around a certain equilibrium point x0 and the corresponding control input u0. The equilibrium point is the state where the aircraft can maintain a stable state when there is no external disturbance or input change (i.e., the state point selected in this application). At the equilibrium point, the nonlinear function usually satisfies: .

[0074] (3) Taylor expansion of nonlinear equations: For nonlinear functions , this application is in Perform Taylor expansion at , ignore higher-order terms, and keep only first-order terms: .

[0075] because , can be simplified to: .

[0076] in, It is a nonlinear function of x and u, which describes the dynamic behavior of the aircraft, where x represents the state point, u represents the control input, x0 represents a certain equilibrium point, and u0 represents the control input corresponding to the equilibrium point.

[0077] It is the Jacobian matrix of the state, which represents the influence of the state change of the flight control system model on the dynamics of the aircraft.

[0078] is the Jacobian matrix of the control input, which represents the effect of changes in the control input on the vehicle dynamics.

[0079] (4) Linearized dynamic equation: After linearization, the dynamic equation of the aircraft can be expressed as: .

[0080] in, is the derivative of the state, which represents the change of the state over time.

[0081] The linearized system model is a state space model where: A is the Jacobian matrix of the state, which represents the effect of the state change of the flight control system model on the dynamics of the aircraft. B is the Jacobian matrix of the control input, which represents the effect of the change of the control input on the dynamics of the aircraft.

[0082] The linearized dynamic equation is: .

[0083] Further simplification: If you use and To represent the deviation between the state and the control input, the linearized equation can be written as: This is the linear system model of the aircraft system model.

[0084] Through the above method, a linear system model of the aircraft system model near a certain equilibrium point can be obtained, which facilitates further analysis and control design of the flight control system model.

[0085] Specifically, the state point linearization unit of this embodiment is used to linearize the nonlinear motion equation of the aircraft near a certain state point, which is convenient for the subsequent linear controller design and analysis. The dynamic and aerodynamic characteristics of the aircraft are usually nonlinear, and linearization can simplify the system modeling, so that linear control methods (such as PID) can be applied to the design of the flight control system. The specific contents include: linearized dynamic equation: Taylor expansion is performed on the nonlinear dynamic equation of the aircraft near the selected state point to obtain an approximate linear system model. Jacobian matrix calculation: The Jacobian matrix in the state equation is calculated to obtain the linearized system dynamic matrix. These matrices describe the small disturbance response of the aircraft under a specific state. Linearized control design: Through the linearized model, controller design (such as linear quadratic regulator control, optimal control, etc.) is performed to ensure that the aircraft can achieve a stable control target near the state point. Error analysis: Evaluate the applicability of the linearized model and analyze the impact of the linearization error on the control performance, especially the effectiveness when the flight state changes greatly.

[0086] In summary, the collaborative workflow of the above data processing modules is as follows: Data input: All sensor data of the flight control system model (such as speed, attitude, control input, etc.) and the initial data of the aerodynamic model are used as input.

[0087] Pneumatic data processing: The pneumatic data processing module first interpolates and fits the pneumatic data to ensure the accuracy of the pneumatic model.

[0088] State point selection: According to the flight mission and flight environment, select the appropriate state point for subsequent balancing and linearization.

[0089] State Point Balancing: Trim each state point to ensure that the control input of the aircraft can remain stable in these states.

[0090] State point linearization: For the selected stable state point, perform system linearization to obtain a linear system model for subsequent control design and analysis.

[0091] Control design: Based on the linearized linear system model, design an appropriate controller to ensure that the aircraft has good performance in various flight conditions.

[0092] This embodiment ensures that the flight control system model can operate stably under different flight conditions and can quickly respond to changes in the aircraft and external disturbances through the joint action of multiple units in the above-mentioned data processing module. Each unit has different functions, and the specific tasks and goals are complementary to each other. Through the design and simulation of the flight control system model, the flight control system model of the aircraft can be fully verified during the design phase, thereby ensuring the reliability and performance of the final system.

[0093] In an embodiment provided in the present application, the model construction module includes an architecture management unit, a parameter setting unit and a module selection unit.

[0094] The architecture management unit is used to select the system control architecture and divide the flight control system into multiple functional modules. It is also used to design the interface between different functional modules and define the data transmission method and control signal interaction method between functional modules.

[0095] The parameter setting unit is used to adjust the controller gain parameters and configure the dynamic parameters of the aircraft. It is also used to set environmental parameters, filter parameters and signal processing parameters.

[0096] The module selection unit is used to select functional modules according to actual needs, and connect and integrate the selected functional modules through appropriate interfaces to form a complete flight control system model.

[0097] Among them, the functional modules include sensor modules, controller modules and actuator modules.

[0098] Specifically, in this embodiment, the model building module is the core part of the flight control system design. Its main function is to select the control law architecture, configure parameters, select modules, and combine these elements into a complete and usable flight control system model. The goal of the model building module is to provide a fast and flexible way to generate a flight control system model, thereby laying the foundation for subsequent simulation, verification and hardware implementation. Specifically, the model building module usually includes the following three main parts: architecture management, parameter setting, and module selection. Each part corresponds to a different link in the design of the flight control system model. Specifically include the following contents: The main purpose of the architecture management unit is to select the overall architecture of the flight control system model and to manage and organize it effectively. This part determines the structural framework of the flight control system model by selecting a suitable control law architecture to ensure that the controller and the various parts of the aircraft can work together in design. Specific functions include: Control architecture selection: Select a suitable control architecture (for example: PID control, optimal control, robust control, sliding mode control, etc.), and determine the type and structure of control laws according to the requirements of the flight control system model. Single controller architecture: For example, the architecture of PID controller (Proportional-Integral-Derivative Controller) is used for simple tasks (such as attitude control). Multi-layer control architecture: For example, an architecture based on hierarchical control, which is divided into low-level attitude control, high-level trajectory tracking control, etc. Distributed control architecture: If the aircraft is a multi-machine system, a distributed control architecture needs to be selected.

[0099] System functional module division: The entire flight control system model is divided into multiple functional modules, such as sensor module, controller module, actuator module, etc. These modules include aircraft attitude control, position control, speed control, etc.

[0100] Interface and data flow management: Design interfaces between different modules and define the data transmission and control signal interaction between modules. For example, how to transmit aircraft sensor data to the controller module and how to transmit control signals to the actuator.

[0101] The parameter setting unit is used to set the parameters of each module in the flight control system model. These parameters are crucial to the performance of the flight control system model. By configuring the parameters, the response and stability of the flight control system model can be adjusted according to the specific flight mission, aircraft characteristics and environmental conditions. Specific functions include: Controller parameter setting: adjust the controller gain parameters, such as the proportional (P), integral (I) and differential (D) gains of the PID controller, or other control algorithm related parameters. PID gain adjustment: set different gains for different control targets (such as pitch, roll, yaw, etc.).

[0102] Optimal control parameters: If you use an optimal control method such as LQR (Linear Quadratic Regulator), which is a method for optimal control problems that aims to design a controller by minimizing a cost function (usually the weighted sum of squares of states and control inputs), you need to set the cost function weights, state and control input weights.

[0103] Robust control parameters: If a robust control strategy is adopted, the choice of parameters can affect the adaptability of the flight control system model to external disturbances and model uncertainties.

[0104] Aircraft dynamics parameter configuration: including the aircraft's mass, aerodynamic characteristics, inertia matrix, control surface amplitude limit, etc. For example, set the aircraft's maximum pitch angle, roll angle and other limits to ensure that the aircraft can still fly stably under extreme conditions.

[0105] Environmental parameter settings: including meteorological data of the flight environment (such as wind speed, air pressure, temperature, etc.), ground height, gravity acceleration, etc. These parameters directly affect the flight state, control law design and simulation results of the aircraft.

[0106] Filter and signal processing parameters: Kalman filters, accelerometer / gyroscope fusion algorithms, etc. are usually used in flight control system models to process sensor data, so relevant filter parameters need to be set.

[0107] The module selection unit is involved in selecting the various functional modules in the flight control system model. These modules can be independent subsystems or composite functional modules composed of multiple submodules. By selecting appropriate modules, all functions of the aircraft can be managed modularly to ensure that the flight control system model can flexibly adapt to different flight missions and control requirements. Specific functions include: Control law module selection: select a specific controller or control algorithm, such as: Classic PID controller: suitable for simple control tasks (such as attitude stability, simple trajectory tracking, etc.). Optimal control module (such as linear quadratic regulator control, i.e. LQR control): suitable for complex trajectory planning and optimal control tasks. Robust control module, such as sliding mode control: suitable for tasks with high requirements for external disturbances and uncertainties. Nonlinear control module: For example, control methods based on feedback linearization are suitable for aircraft with strong nonlinear characteristics.

[0108] It should be noted that the above control law is a mathematical rule or algorithm that describes how to calculate the control input (such as torque, thrust, speed, etc.) based on the state of the flight control system model (such as position, speed, attitude, etc.). Simply put, the control law defines how a flight control system adjusts its actuators (such as motors, control surfaces, etc.) based on measured input and output data (such as sensor feedback) to achieve the desired system behavior or performance. In this application, the control law refers to the control law of the flight control system model.

[0109] Sensor module selection: Select the sensor model used to obtain the aircraft status, such as: IMU (Inertial Measurement Unit): used to measure attitude, speed and other data. GPS module: used to measure position and speed. Radar / LiDAR: used for height and obstacle detection. Vision sensor: used for visual navigation and object recognition.

[0110] Actuator module selection: According to the dynamic characteristics of the aircraft, select the actuator module output by the controller, such as: Servo module: used to control the attitude and movement of the aircraft. Thruster module: used to control the thrust and movement of the aircraft.

[0111] System integration and connection: Connect and integrate the selected modules (such as sensors, controllers, actuators, etc.) through appropriate interfaces to form a complete flight control system model.

[0112] Overall generation and simulation: After completing the architecture management, parameter configuration and module selection, the construction process of the flight control system model will be quickly generated, and the stability, performance and robustness of the flight control system model will be verified through simulation. The simulation can be performed under multiple flight states and environmental conditions to help designers evaluate the performance of the flight control system model in different flight missions.

[0113] In summary, the model building module of this embodiment enables the flight control system model to be quickly built through architecture management, parameter setting, and module selection, and can flexibly adapt to different control requirements and flight missions. Ultimately, the entire flight control system model will be used in subsequent simulation verification, hardware implementation, flight testing, and other links, thereby providing protection for the safety and performance of the aircraft.

[0114] like Figure 3 As shown, in one embodiment provided in the present application, the quality assessment module includes an active flight control assessment unit, an automatic flight control assessment unit and an assessment optimization unit.

[0115] The active flight control evaluation unit is used to evaluate the performance of the flight control system model in the active control mode. Specifically, the flight performance of the aircraft is monitored in real time in the flight control system model, and feedback and adjustments are made to the control of the aircraft.

[0116] The automatic flight control evaluation unit is used to evaluate the performance of the flight control system model in the automatic control mode, specifically: evaluating the tracking of the autonomous flight path, the completion of the autonomous flight mission, and the fault detection during autonomous flight.

[0117] The evaluation and optimization unit is used to analyze the evaluation results of the flight control system model to generate optimization suggestions, and optimize the control strategy and system parameters based on the evaluation results.

[0118] Specifically, the quality assessment module of this embodiment is a crucial part of the platform design process. Its main function is to evaluate, optimize and verify the flight control system model generated by the platform model building function. Through the statistics, analysis and optimization of the index results of the flight control system model, it can be ensured that the flight control system meets the performance requirements, stability requirements and safety requirements in actual applications, thereby improving the overall quality of the flight control system. The quality assessment module of this embodiment includes the following three main parts: 1) Active flight control evaluation unit, used to evaluate the performance of the flight control system model in active control mode, including the flight control system model's response, stability, control performance, etc. of the aircraft under different flight conditions. This unit performs real-time flight performance monitoring and control feedback adjustments in the flight control system model to ensure that the flight control system model can maintain stability and good response under various flight conditions. Specific functions include: Flight mission performance evaluation: Evaluate the performance of the flight control system model in different flight missions, including attitude control, trajectory tracking, heading control, speed control, etc. For example: Evaluate the dynamic response characteristics, transition time, overshoot, steady-state error, etc. of the flight control system model under different operating conditions such as cruising, turning, climbing, and descending.

[0119] Dynamic performance evaluation: Evaluate the flight control system model's response to different dynamic environments, including external disturbances (such as wind and airflow changes) and adaptability to changes in aircraft dynamics. For example, robustness testing of external disturbances such as wind speed changes and airflow instability ensures that the flight control system model can still work stably under changing environmental conditions. During the flight, simulate sensor errors, faults or failures to evaluate the robustness of the flight control system model.

[0120] Control law performance analysis: Evaluate the performance of the selected control algorithm (such as PID control, LQR control, robust control, etc.) under different flight conditions to determine whether the flight control system model can achieve the expected control objectives. The control objectives include: Control accuracy: such as attitude, position and other control accuracy, whether they meet the flight mission requirements. Stability: Check whether the flight control system model remains stable during long-term flight and does not show instability. Response speed: Whether the response time and transition process of the flight control system model to the control input are reasonable.

[0121] 2) Automatic flight control evaluation unit, which is aimed at the performance evaluation of automatic flight tasks, focusing on the performance of the flight control system model under automatic driving or autonomous flight conditions. This unit module is mainly used to verify the accuracy, robustness and safety of the automatic flight control system model, and ensure that the flight control system model can perform the intended tasks in a complex and dynamic flight environment. Specific functions include: Autonomous flight path tracking: evaluates whether the flight control system model can accurately fly along the predetermined trajectory without human intervention. The evaluation includes: Position tracking error: the error between the actual position of the aircraft and the predetermined path during the path tracking process, and whether it meets the accuracy requirements. Speed ​​tracking error: whether the speed of the aircraft can accurately track the preset speed instructions. Altitude and attitude tracking error: whether the altitude and attitude of the aircraft are stable and accurately follow the set flight trajectory.

[0122] Autonomous flight mission completion: Evaluate whether the flight control system model can complete complex automatic flight missions (such as autonomous takeoff, cruising, landing, obstacle avoidance, etc.), focusing on evaluating the stability, safety and mission success rate of the aircraft when performing the mission. Mission completion: Whether the aircraft can perform according to the predetermined mission requirements and complete the mission within the specified time. Trajectory accuracy: The error between the actual trajectory of the aircraft and the predetermined trajectory in the automatic flight mission. Flight control system model robustness: Whether the flight control system model can safely and reliably complete the automatic flight mission in the face of disturbances, environmental changes or system anomalies (such as sensor failures, actuator failures, etc.).

[0123] Fault detection and fault tolerance assessment: Evaluate the flight control system model’s response and handling capabilities when facing various possible faults (such as sensor failure, actuator failure, etc.). The flight control system model must have good fault tolerance and be able to continue flying or land safely when some modules fail.

[0124] 3) Evaluation and optimization unit, which is mainly used to analyze the evaluation results of the flight control system model, provide system performance indicators, problem diagnosis and optimization suggestions, and optimize the system according to the evaluation results. This unit module not only evaluates the flight control system model, but also optimizes the control strategy and parameters based on the evaluation results to meet all the requirements and performance goals of the flight control system model. Specific functions include: Performance indicator statistics and analysis: According to the evaluation results of the flight control system model, the key performance indicators of the flight control system model are counted and analyzed, such as: Overshoot: evaluate the overshoot of the system. Stabilization time: the time it takes for the flight control system model to reach a steady state. Error range: the error size of the flight control system model in a steady state. Control input limit: whether the input of the control system is restricted, such as power, speed, etc. Performance bottleneck identification: Through in-depth analysis of the evaluation data, possible performance bottlenecks in the system are found, such as slow response speed, insufficient control accuracy, etc., and the source of the problem is identified (such as controller parameter settings, model errors, actuator insensitivity, etc.). For example, if the system performs poorly under a specific flight state, it may be that the controller business gain parameter settings are unreasonable, or the dynamic modeling of the aircraft is inaccurate.

[0125] Optimization algorithms and strategies: According to the performance analysis results, optimization solutions are provided automatically or manually. The optimization process may include: Control parameter tuning: Adjust the control law (such as the gain of the PID controller, the weight matrix of the LQR controller, etc.) to improve system stability and response speed. Nonlinear control optimization: For nonlinear control problems, optimize the control strategy to reduce the nonlinear error of the flight control system model and improve control accuracy. Multi-objective optimization: Multi-objective optimization is performed for different flight mission requirements, such as: maximizing the response speed of the flight control system model and reducing energy consumption while ensuring stability. Iterative design and simulation: During the optimization process, the flight control system model can be iteratively optimized multiple times. After each optimization, the flight control system model needs to be re-evaluated and simulated to ensure that the optimization results can play a role in actual flight. Through simulation testing of various flight conditions, it is verified whether the optimized flight control system model can achieve the expected performance in actual applications.

[0126] In general, the quality assessment module of this embodiment evaluates and optimizes system performance through three parts: active flight control assessment unit, automatic flight control assessment unit and assessment optimization unit. Through these assessment functions, the performance of the flight control system model can be monitored in real time during the construction of the flight control system model, and problems can be diagnosed and optimization solutions can be proposed. Ultimately, it ensures that the flight control system model can meet the requirements of performance, stability, safety and other aspects under different flight missions and environmental conditions, providing reliable guarantees for subsequent simulation, hardware implementation and flight testing.

[0127] In one embodiment provided in the present application, the simulation test module includes a flight control unit, an actuator model, an aircraft model, a sensor model and a navigation unit.

[0128] The flight control unit sends the actuator data of the second test cycle to the actuator model based on the control instruction and the navigation data of the aircraft in the first test cycle output by the navigation unit. The navigation data of the aircraft in the first test cycle includes: at least one of the first position, the first attitude and the first speed of the aircraft. The actuator data is used to represent the kinetic energy parameters required to execute the control instruction. The second test cycle is the next test cycle of the first test cycle.

[0129] The actuator model determines the operating parameters of the power element of the aircraft based on the actuator data of the second test cycle of the flight control unit, and sends the operating parameters to the aircraft model. The actuator model is obtained by fitting the real actuator data and the real power parameters.

[0130] The aircraft model obtains simulated state data of the aircraft based on the operating parameters and sends the simulated state data to the sensor model, wherein the simulated state data includes at least one of the calculated position, calculated attitude, calculated speed, calculated acceleration and calculated angular velocity of the aircraft.

[0131] The sensor model determines a sensor reading of at least one sensor based on the simulated state data and sends the sensor reading to the navigation unit.

[0132] The navigation unit determines navigation data of the aircraft in the second test cycle based on the sensor readings, wherein the navigation data of the aircraft in the second test cycle includes at least one of a second position, a second attitude, and a second speed of the aircraft. The navigation unit is used to simulate guidance of the aircraft in any test cycle of the aircraft.

[0133] In one embodiment provided in the present application, the aircraft model includes: The attitude sub-model obtains the calculated angular velocity of the aircraft based on the spiral torque and moment of inertia of the aircraft and uses it to calculate the Euler angle of the aircraft. The Euler angle is used to represent the calculated attitude of the aircraft.

[0134] The velocity sub-model obtains the rotation matrix of the aircraft based on the Euler angle. The calculated acceleration of the aircraft is obtained based on the rotation matrix and operating parameters, and then the calculated velocity of the aircraft is obtained.

[0135] The position sub-model obtains the wind speed in the second test cycle based on the position of the aircraft in the first test cycle, and obtains the calculated position of the aircraft based on the wind speed and the calculated speed in the second test cycle.

[0136] Specifically, through the above content, this embodiment can, within each test cycle, simulate the flight control of the aircraft based on the control instructions and the navigation data of the previous test cycle through the flight control unit, navigation unit, sensor model, aircraft model and actuator model, thereby realizing the simulation of the flight control system. Since the sensor model, aircraft model and actuator model are models implemented based on software, the cost of performing simulation testing of the flight control system is reduced, and the running model does not need to spend a lot of time to prepare, thereby achieving the purpose of improving the efficiency of simulation testing.

[0137] In summary, this application has greatly improved the efficiency, robustness, safety, accuracy and stability of flight control system development by establishing a graphical modeling and simulation platform for aircraft control systems. It also provides practical guidance for the actual operation of aircraft. The platform has the following advantages: (1) Data processing: Users can quickly process the aerodynamic characteristic data required for aircraft model building by simply clicking a button on the interface, and quickly process it into the state point matrix required for the aircraft body model.

[0138] (2) Model construction: Users can use the flight control professional model library included in the product and the model construction interface function to quickly build a complete flight control system model that can be simulated and run, completely avoiding the complicated process of users manually building models.

[0139] (3) Quality evaluation: Users can use the product's quality evaluation function to quickly evaluate the active flight control and automatic flight control simulation results of the established flight control system model from the lateral and longitudinal dimensions, facilitating the rapid iteration of the control law of the flight control system model.

[0140] (4) Rapid update: The flight control system model design and quality assessment software products are based on the actual needs of users and closely combined with users' actual usage feedback. They are quickly iterated to continuously optimize product functions and improve product details.

[0141] Example 2 See also Figure 4 The present application provides a method for graphical modeling and simulation of an aircraft control system, comprising the following steps: Data processing: Acquire system-related data of the flight control system and preprocess it, including aerodynamic data processing, state point selection, state point balancing and state point linearization.

[0142] Model construction: Based on the flight control overall architecture model and the flight control professional model library, architecture management, configuration parameter settings and module selection are performed to generate a flight control system model.

[0143] Quality assessment: Evaluate and optimize the simulation results of the flight control system model, including active flight control evaluation and automatic flight control evaluation.

[0144] Simulation test: The flight control system model is simulated and tested in each test cycle.

[0145] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned flight control system model can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0147] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as a preferred embodiment as above, it is not intended to limit the present application. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present application. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the technical solution of the present application.

Claims

1. A graphical modeling and simulation platform for aircraft control systems, characterized in that: include: A data processing module, used for acquiring system-related data of the flight control system and preprocessing the data, wherein the preprocessing includes aerodynamic data processing, state point selection, state point balancing and state point linearization; Model building module, which manages the architecture, sets configuration parameters and selects modules based on the overall architecture model of flight control and the professional model library of flight control, and generates a flight control system model. A quality assessment module, used to evaluate and optimize the simulation results of the flight control system model; the evaluation includes active flight control evaluation and automatic flight control evaluation; A simulation test module, used for performing simulation test on the flight control system model in each test cycle; Wherein, the data processing module includes a pneumatic data processing unit, a state point selection unit, a state point balancing unit, a state point linearization unit and a system data acquisition unit; The aerodynamic data processing unit is used to process and analyze the aerodynamic characteristic data of the aircraft; The state point selection unit is used to select a state point from a flight simulation or a flight experiment; The state point balancing unit is used to dig out and determine the control input of the aircraft so that the aircraft remains stable in the set flight state; The state point linearization unit is used to linearize the nonlinear motion equation of the aircraft near the state point; The system data acquisition unit includes an image processing subunit, a CFD model subunit and a system data extraction subunit; The image processing subunit is used to obtain video image data of the aircraft in different flight states and process the video image data, including: The video image data is obtained, specifically: Acquire a three-dimensional real scene image and perform smoothing processing: establish a corresponding image template on the three-dimensional real scene image, determine the pixels on the image template and calculate the average value of the pixels, and replace the original pixel value with the average value of the pixels; Perform image segmentation on the three-dimensional real scene image: determine a segmentation threshold value according to the three-dimensional real scene image and determine the average gray value of the three-dimensional real scene image; iteratively perform the segmentation step until the difference between the sum of the grayscale means of the two types of images to be segmented and the average grayscale value is within a preset range; the segmentation step includes: segmenting the three-dimensional real scene image into two types of images according to the determined segmentation threshold value, determining the grayscale means of the two types of images to be segmented, and the sum of their grayscale means; updating the current segmentation threshold value, and performing the segmentation step; segmenting the three-dimensional real scene image using the current segmentation threshold value; Performing image stitching on the segmented three-dimensional real scene images: obtaining the image to be stitched from the segmented three-dimensional real scene images, extracting feature points of the image to be stitched for matching; registering the image to be stitched by the feature points, and constructing a transformation matrix between the images to be stitched to complete the stitching of the three-dimensional real scene images; Fusing the stitched images, and using the fused images as the video image data; The video image data is processed, specifically: Acquire a grayscale image sequence of the video image data; Based on a preset clarity threshold, images with a clarity greater than the clarity threshold are selected from the grayscale image sequence as clear images to obtain a first type of clear image sequence; Determine the grayscale mean of the difference image of the difference image of two consecutive frames of clear images in the first type of clear image sequence, and select two frames of clear images whose grayscale means of the difference images are greater than the first image threshold; If the two selected clear image frames are adjacent in the grayscale image sequence, the two frames of images are retained in the first type of clear image sequence; if the two selected clear image frames are not adjacent in the grayscale image sequence, a blurred image between the two frames of images is obtained, and blurred images between all non-adjacent two frames of clear images are obtained to form a second type of blurred image sequence; forming a key image sequence according to the second type of blurred image sequence and the first type of clear image sequence; Inputting the key image sequence into the network model and outputting simulated flow field data of the aircraft; The CFD model subunit constructs a CFD model based on the simulated flow field data, and performs simulation calculations to obtain CFD simulation results; The system data extraction subunit extracts the system related data from the CFD simulation results; The aerodynamic characteristic data include lift, drag, pitch moment and roll moment of the aircraft; and the state points include attitude, speed and acceleration of the aircraft.

2. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The pneumatic data processing unit includes a data interpolation subunit, a pneumatic model fitting subunit, a control torque calculation subunit and a pneumatic nonlinear processing subunit; The data interpolation subunit is used to perform interpolation processing on the aerodynamic characteristic data under different flight states by using an interpolation method; The aerodynamic model fitting subunit is used to fit the aerodynamic model according to the measured aerodynamic measurement data to obtain more accurate aerodynamic characteristic data; wherein the fitting method used includes a polynomial fitting method and a piecewise linear fitting method; The control torque calculation subunit is used to calculate the control torque generated by the movement of the aircraft according to the aerodynamic model; The aerodynamic nonlinear processing subunit is used to process the aerodynamic nonlinear behavior of the aircraft under different flight conditions.

3. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The state point selection unit includes a flight state space division subunit and a key state point dynamic selection subunit; The flight state space division subunit is used to divide the flight space of the aircraft to form flight state subspaces with different working states according to the mission requirements and flight environment of the aircraft; The key state point dynamic selection subunit is used to dynamically select key state points in the flight state subspace; The key state points include the maximum lift point, the best cruising point and the boundary state point.

4. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The state point balancing unit includes a control input balancing subunit, a stability analysis subunit and a boundary condition processing subunit; The control input balancing subunit is used to adjust the initial input of each control surface according to the aerodynamic model of the aircraft to keep the aircraft in a balanced state; The stability analysis subunit is used to analyze the balance and stability of the aircraft in a specific state or in the absence of external disturbances; The boundary condition processing subunit is used to process and balance the flight state of the aircraft under boundary conditions to verify the impact of extreme conditions on the aircraft.

5. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The state point linearization unit includes a linearized dynamic equation subunit, a Jacobian matrix calculation subunit, a linearized control design subunit and an error analysis subunit; The linearized dynamic equation subunit is used to perform Taylor expansion on the nonlinear dynamic equation of the aircraft near the selected state point to obtain an approximate linear system model; The Jacobian matrix calculation subunit is used to calculate the Jacobian matrix in the state equation to obtain a linearized system dynamic matrix; wherein the system dynamic matrix is ​​used to describe the small disturbance response of the aircraft under a specific state; The linearization control design subunit is used to design a controller through the linear system model after linearization; The error analysis subunit is used to evaluate the applicability of the linear system model and analyze the impact of the linearization error on the control performance of the controller.

6. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The model building module includes an architecture management unit, a parameter setting unit and a module selection unit; The architecture management unit is used to select the system control architecture and divide the flight control system model into multiple functional modules; it is also used to design interfaces between different functional modules and define the data transmission method and control signal interaction method between functional modules; The parameter setting unit is used to adjust the gain parameters of the controller and configure the dynamic parameters of the aircraft; it is also used to set environmental parameters, filter parameters and signal processing parameters; The module selection unit is used to select functional modules according to actual needs, and connect and integrate the selected functional modules through appropriate interfaces to form a complete flight control system model; Wherein, the functional modules include a sensor module, a controller module and an actuator module.

7. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The quality assessment module includes an active flight control assessment unit, an automatic flight control assessment unit and an assessment optimization unit; The active flight control evaluation unit is used to evaluate the performance of the flight control system model in the active control mode, specifically: real-time monitoring of the flight performance of the aircraft in the flight control system model, and feedback and adjustment of the control of the aircraft; The automatic flight control evaluation unit is used to evaluate the performance of the flight control system model in the automatic control mode, specifically: evaluating the tracking of the autonomous flight path, the completion of the autonomous flight mission, and the fault detection during autonomous flight; The evaluation and optimization unit is used to analyze the evaluation results of the flight control system model to generate optimization suggestions, and optimize the control strategy and system parameters based on the evaluation results.

8. The aircraft control system graphical modeling and simulation platform according to claim 1, characterized in that: The simulation test module includes a flight control unit, an actuator model and a navigation unit; The flight control unit sends the actuator data of the second test cycle to the actuator model based on the control instruction and the navigation data of the aircraft in the first test cycle output by the navigation unit; the navigation data of the aircraft in the first test cycle includes: at least one of the first position, the first attitude and the first speed of the aircraft; the actuator data is used to represent the kinetic energy parameters required to be output for executing the control instruction; the second test cycle is the next test cycle of the first test cycle; The actuator model determines the operating parameters of the power element of the aircraft based on the actuator data of the second test cycle of the flight control unit, and sends the operating parameters to the aircraft model; the actuator model is obtained based on fitting the real actuator data and the real power parameters; The navigation unit determines the navigation data of the aircraft in the second test cycle based on the sensor readings, and the navigation data of the aircraft in the second test cycle includes: at least one of the second position, second attitude and second speed of the aircraft; the navigation unit is used to simulate the guidance of the aircraft in any test cycle of the aircraft.

9. The aircraft control system graphical modeling and simulation platform according to claim 8, characterized in that: The simulation test module also includes an aircraft model and a sensor model; The aircraft model acquires simulated state data of the aircraft based on the operating parameters, and sends the simulated state data to the sensor model; the simulated state data includes at least one of the calculated position, calculated attitude, calculated velocity, calculated acceleration and calculated angular velocity of the aircraft; The sensor model determines a sensor reading of at least one sensor based on the simulated state data and sends the sensor reading to the navigation unit.

10. The aircraft control system graphical modeling and simulation platform according to claim 9, characterized in that: The aircraft model comprises: The attitude sub-model obtains the calculated angular velocity of the aircraft based on the spiral torque and the moment of inertia of the aircraft and calculates the Euler angle of the aircraft based on the calculated angular velocity of the aircraft, wherein the Euler angle is used to represent the calculated attitude of the aircraft; The velocity sub-model obtains the rotation matrix of the aircraft based on the Euler angles; obtains the calculated acceleration of the aircraft based on the rotation matrix and the operating parameters, and then obtains the calculated velocity of the aircraft based on the acceleration; The position sub-model obtains the wind speed in the second test cycle based on the position of the aircraft in the first test cycle; and obtains the calculated position of the aircraft based on the wind speed in the second test cycle and the calculated speed.

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