Flight control system development platform based on graphic processing technology

Abstract

The invention relates to a flight control system development platform based on a graphic processing technology, and relates to the field of flight control system modeling, and the platform comprises the steps: carrying out the preprocessing of flight feature data of an aircraft, generating a state point matrix, and analyzing and determining the flight state, flight task and flight environment of the aircraft; according to the flight state, the flight task and the flight environment, constructing an active flight control system and an automatic flight control system which can be operated in a simulation manner; and performing performance evaluation on the active flight control system and the automatic flight control system, and performing iterative optimization on the flight control system according to a performance evaluation result until a predetermined flight requirement is met. The technical problems that a traditional development method usually needs a large amount of manual intervention and a complex manual modeling process, the development efficiency is low, and errors are prone to occurring can be solved; through automatic modeling processing, the required flight control system model can be quickly generated, and the modeling efficiency and modeling quality of the flight control system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of flight control system modeling, and particularly to a flight control system development platform based on graphics processing technology. Background Art

[0002] The flight control system is a crucial component in modern aircraft, responsible for controlling and stabilizing key parameters such as the attitude, speed, and heading of the aircraft during flight. With the continuous progress of aerospace technology, the design and development of flight control systems have become increasingly complex, especially in unmanned aircraft and modern aircraft, where the flight control system needs to adapt to different flight environments and mission requirements. Therefore, its development process requires high-precision and high-efficiency modeling and optimization.

[0003] Currently, traditional flight control system development methods usually rely on manual intervention and manual modeling processes, suffering from problems such as low development efficiency, high error risk, and difficulty in coping with complex requirements. Summary of the Invention

[0004] In view of the technical problems that traditional flight control system development methods usually require a large amount of manual intervention and complex manual modeling processes, resulting in low development efficiency and being prone to errors, the present invention provides a flight control system development platform based on graphics processing technology to solve these problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows. The present invention provides a flight control system development platform based on graphics processing technology, including: A flight characteristic data processing module, which is used to preprocess the flight characteristic data of the aircraft to generate a state point matrix, and analyze and determine the flight state, flight mission, and flight environment of the aircraft according to the state point matrix; a flight control system construction module, which is used to construct a flight control system that can be simulated and run according to the flight state, flight mission, and flight environment, wherein the flight control system includes an active flight control system and an automatic flight control system; a flight control system evaluation and optimization module, which is used to evaluate the performance of the active flight control system and the automatic flight control system, and perform iterative optimization on the active flight control system and the automatic flight control system according to the performance evaluation results until the predetermined flight requirements are met.

[0006] Optionally, the flight control system development platform based on graphics processing technology further includes: an aerodynamic data processing unit, configured to perform data cleaning, noise filtering, and formatting on aerodynamic data, and convert the original data into structured data that can be used for flight control system modeling; a state point selection unit, configured to select flight state points according to the type of aircraft and determine the state variables of the flight state points; a state point trimming unit, configured to ensure that the aircraft is in a balanced state at the flight state points; and a state point linearization unit, configured to perform linearization processing on the flight state points.

[0007] Optionally, the flight control system development platform based on graphics processing technology further includes: a flight control architecture management unit, configured to select an adapted architecture template from a flight control architecture library according to the flight state, where the flight control architecture library at least includes a PID control architecture, an optimal control architecture, and a robust control architecture; a flight control parameter configuration unit, configured to perform parameter configuration on predetermined control components according to the flight state, flight mission, and flight environment; and a flight control module selection unit, configured to match and obtain an adapted control module from a flight control module library according to the flight state, flight mission, and flight environment.

[0008] Optionally, the flight control system development platform based on graphics processing technology further includes: configuring the gain parameters, feedback coefficients, and filtering parameters of the control algorithm according to the flight state; configuring the sampling frequency, sampling range, and sampling accuracy of the sensor according to the flight state and flight environment; and setting the response time, control signal amplitude, and control accuracy of the actuator according to the flight mission.

[0009] Optionally, the flight control system development platform based on graphics processing technology further includes: configuring a flight control module library, where the flight control module library includes a plurality of control modules, and the control modules are marked with module-adapted flight conditions; inputting the flight state, flight mission, and flight environment into the flight control module library, respectively performing similarity comparison with a plurality of adapted flight conditions, and setting the control module corresponding to the adapted flight condition with the highest similarity as the adapted control module.

[0010] Optionally, the flight control system development platform based on graphics processing technology further includes: randomly selecting a first adapted flight condition from a plurality of adapted flight conditions of a plurality of control modules, where the first adapted flight condition includes a first adapted flight state, a first adapted flight mission, and a first adapted flight environment; respectively performing similarity comparison between the flight state, flight mission, and flight environment and the first adapted flight state, first adapted flight mission, and first adapted flight environment, outputting a first state similarity, a first mission similarity, and a first environment similarity, calculating a weighted first similarity, and sequentially analyzing and outputting the similarities of a plurality of adapted flight conditions.

[0011] Optionally, the flight control system development platform based on graphics processing technology further includes: an active flight control evaluation unit, configured to evaluate the response characteristics of the active flight control system in different flight states through the lateral-directional dimension and the longitudinal dimension within the simulation operation space, and output active response characteristics, where the active response characteristics include attitude control accuracy, transition response time, system stability coefficient, and system anti-interference coefficient; an automatic flight control evaluation unit, configured to evaluate the response characteristics of the automatic flight control system in different flight states through the lateral-directional dimension and the longitudinal dimension within the simulation operation space, and output automatic response characteristics, where the automatic response characteristics include path tracking accuracy, automatic navigation accuracy, and speed control error.

[0012] Optionally, the flight control system development platform based on graphics processing technology further includes: the flight control system evaluation and optimization module further includes a flight control system optimization unit, configured to calculate the deviation of the active response characteristics and the automatic response characteristics according to the predetermined flight requirements, and respectively perform iterative optimization on the active flight control system and the automatic flight control system according to the deviation calculation results until the predetermined flight requirements are met.

[0013] The beneficial effects of the present invention are as follows: By preprocessing the flight characteristic data of the aircraft, a state point matrix is generated, and the flight state, flight mission, and flight environment of the aircraft are analyzed and determined according to the state point matrix; then, a flight control system that can be simulated and operated is constructed according to the flight state, flight mission, and flight environment, where the flight control system includes an active flight control system and an automatic flight control system; then, the performance of the active flight control system and the automatic flight control system is evaluated, and iterative optimization is performed on the active flight control system and the automatic flight control system according to the performance evaluation results until the predetermined flight requirements are met; that is to say, through automated modeling processing, manual intervention can be reduced, the required flight control system model can be quickly generated, efficient and automated development of the flight control system can be realized, and the modeling efficiency and modeling quality of the flight control system can be effectively improved. Description of the Drawings

[0014] Figure 1 It is a schematic structural diagram of the flight control system development platform based on graphics processing technology provided by the present invention; Figure 2 It is a schematic structural diagram of the flight control system construction module in the flight control system development platform based on graphics processing technology provided by the present invention.

[0015] In the drawings, the components represented by the reference numerals are described as follows: Flight characteristic data processing module 10, flight control system construction module 20, flight control system evaluation and optimization module 30, aerodynamic data processing unit 11, state point selection unit 12, state point trimming unit 13, state point linearization unit 14. Detailed implementation manners

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or description". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0019] Embodiment, as Figure 1 As shown, the embodiment of the present invention provides a flight control system development platform based on graphics processing technology, including: A flight characteristic data processing module 10, configured to preprocess the flight characteristic data of the aircraft, generate a state point matrix, and analyze and determine the flight state, flight mission, and flight environment of the aircraft according to the state point matrix.

[0020] Specifically, during the flight of an aircraft, the flight characteristic data involved include, but are not limited to, the attitude, speed, acceleration, aerodynamic characteristics, control inputs, external environment data (such as air pressure, temperature, wind speed, etc.) and the status information of the aircraft. These data play a crucial role in the modeling and design process of the flight control system. In particular, the performance of the aircraft's control system is closely related to the flight characteristics of the aircraft.

[0021] First, preprocess the flight characteristic data of the aircraft. The purpose of preprocessing the flight characteristic data is to convert the original data into a state point matrix that can be used for the modeling of the flight control system and ensure the accuracy and consistency of the data. The preprocessing steps include data cleaning and denoising, data normalization and standardization, etc. Generate the state point matrix. The state point matrix is the basis in the modeling of the flight control system. It contains the key parameters and flight characteristics of the aircraft in different flight states. These parameters include the attitude angle, linear velocity, acceleration, control input, etc. of the aircraft and are the core data in the design of the flight control system. A state point is a set that describes the flight characteristics of the aircraft in a specific flight state. The flight control system usually defines different state points based on data such as the attitude (such as pitch angle, roll angle, yaw angle), speed, and acceleration of the aircraft. The process of selecting state points needs to be reasonably set according to the flight mission and flight environment to ensure that the selected state points can accurately reflect the flight state of the aircraft. Then, organize the key parameters of the aircraft in different flight states into a matrix form, which is the state point matrix. Each row in the state point matrix represents the flight characteristics in a specific flight state, and the columns in the matrix correspond to different parameters of the aircraft. Through this matrix form, the flight characteristics of the aircraft in different states can be clearly reflected, providing the necessary data support for the modeling of the flight control system.

[0022] Next, based on the analysis of the state point matrix, the flight state, flight mission, and flight environment of the aircraft are determined. By analyzing the data in the state point matrix, developers can understand the flight characteristics of the aircraft in various states. For example, the changing trends of parameters such as the attitude angle, speed, and acceleration of the aircraft in different flight phases can reveal the stability, control accuracy, and response ability of the aircraft. Flight missions (such as takeoff, cruise, landing, etc.) usually require the aircraft to perform specific operations in different states. By analyzing the state point matrix, it can be determined whether the aircraft meets the requirements of the predetermined flight mission, thereby guiding the design of the flight control system. For example, a certain flight mission may require the aircraft to maintain a specific attitude and speed in a certain flight phase, and the state point matrix helps to identify and verify these flight requirements. The flight environment of the aircraft (such as temperature, air pressure, wind speed, etc.) has an important impact on flight performance. The state point matrix contains the environmental data of the aircraft. Developers can analyze the impact of the flight environment on the aircraft performance through these data and adjust the parameters of the flight control system according to different flight environment conditions to improve the adaptability and stability of the system.

[0023] Furthermore, as Figure 2 shown, the flight characteristic data processing module 10 further includes: An aerodynamic data processing unit 11 for performing data cleaning, noise filtering, and formatting on aerodynamic data, and converting the original data into structured data that can be used for flight control system modeling; a state point selection unit 12 for selecting flight state points according to the aircraft type and determining the state variables of the flight state points; a state point trimming unit 13 for ensuring that the aircraft is in a balanced state at the flight state points; a state point linearization unit 14 for performing linearization processing on the flight state points.

[0024] Specifically, the pneumatic data processing unit is one of the core modules in the flight control system modeling process. Its main task is to convert the pneumatic data of the aircraft from the original unstructured data into structured data that can be used for subsequent modeling. Since the pneumatic data of the aircraft is usually disturbed by noise and other external factors, data preprocessing is an important link to ensure the accuracy of flight control system modeling. The main functions include data cleaning, noise filtering, and formatting. There may be missing values, outliers, or incomplete situations in the pneumatic data of the aircraft. The purpose of data cleaning is to remove these invalid data and retain reliable observed values. In this process, interpolation methods can be used to fill in the missing values, or outlier points can be removed to ensure the accuracy of the data. Since the aircraft sensors may be affected by environmental noise (such as airflow changes, electronic interference, etc.) when acquiring data, noise filtering becomes an important step in data processing. Common noise filtering techniques include low-pass filters, high-pass filters, and Kalman filters, etc. Through these techniques, high-frequency noise can be effectively removed and useful signals can be retained. The pneumatic data of the aircraft is usually stored in different formats (for example, time-series data, arrays, matrices, etc.). The task of formatting is to uniformly convert these data into a structured format for subsequent analysis and modeling. This step includes converting the original pneumatic data into a state point matrix that is easy to process to ensure that the data can be directly used in subsequent processing.

[0025] The main role of the state point selection unit is to select appropriate flight state points from the pneumatic data of the aircraft. These state points reflect the flight characteristics of the aircraft under different flight conditions. The selection of state points is based on factors such as the type of aircraft, mission requirements, and the dynamic model of the aircraft. The purpose is to ensure that the selected state points can comprehensively cover the working range of the aircraft and meet the requirements of flight control system modeling. The main functions include aircraft type selection, state point selection, and state variable determination. The type of aircraft (such as fixed-wing, rotary-wing, unmanned aircraft, etc.) directly affects the selection of its flight state points. Each type of aircraft has specific flight dynamic characteristics. Therefore, the selection of state points needs to be carried out according to the type of aircraft. For different types of aircraft, different flight state points may need to be selected and adjusted according to different requirements of flight missions. By analyzing the pneumatic data of the aircraft and the requirements of flight missions, state points related to the flight mission are selected. For example, during the takeoff, cruise, and landing of the aircraft, parameters such as the attitude, speed, and acceleration of the aircraft will change significantly. The state point selection unit ensures that the flight control system can be effectively controlled throughout the flight process by selecting these key state points. For each selected flight state point, the state variables related to this state point need to be determined. The state variables include the attitude, speed, acceleration, control input, etc. of the aircraft. These variables can completely describe the motion characteristics of the aircraft under a specific state and provide data support for flight control system modeling.

[0026] The function of the state point trimming unit is to ensure that the aircraft is in a balanced state at the selected flight state point, that is, at this state point, the flight characteristics of the aircraft are in a stable and controllable state. The balance of the aircraft directly affects the stability and responsiveness of the flight control system. Therefore, state point trimming is an important step in flight control system modeling. Its main functions include aircraft balance calculation, control moment calculation and optimization, and feedback regulation and optimization. For example, according to the aerodynamic data and dynamic model of the aircraft, calculate the balance conditions of the aircraft under specific flight states, which includes calculating the balance relationships of various forces such as lift, gravity, and traction of the aircraft. By ensuring the moment balance of the aircraft, it is guaranteed that the aircraft will not be in an unstable state under this state. The trimming calculation needs to consider various control components of the aircraft, such as control surfaces, engine thrust, etc. These control components generate control moments to maintain the balance of the aircraft. The state point trimming unit ensures that the aircraft can always maintain a balanced state during flight by calculating appropriate control inputs. In some complex flight missions, the aircraft may encounter external disturbances (such as wind speed changes, etc.). Therefore, the state point trimming unit also includes real-time monitoring and feedback regulation of the aircraft's balance state to cope with environmental changes during flight and ensure the balance of the aircraft.

[0027] The main task of the state point linearization unit is to transform the non-linear dynamic model of the aircraft at a specific state point into a linear model. The dynamics of the aircraft are usually highly non-linear. Therefore, in order to facilitate the design and analysis of the control system, it is necessary to linearize the dynamics of the aircraft. Its main functions include non-linear system linearization, analysis of the linearized control system, and adaptability analysis of the linear model. For example, the dynamic model of the aircraft usually includes non-linear equations, such as the non-linear relationships between the lift, drag, and control inputs of the aircraft and the flight state. The state point linearization unit linearizes these non-linear equations near a specific state point through Taylor series expansion or other linearization methods to obtain a simplified linear model. The linearized model can be used for the design and analysis of the flight control system, especially for the design of control laws. The linearized model can simplify the stability analysis, frequency response analysis, etc. of the control system, thereby providing a basis for the optimization of the flight control system. Through the linearized state point model, developers can analyze the adaptability and stability of the flight control system under different flight states, which helps to design a more adaptable flight control system and ensure the stable operation of the aircraft under different flight missions and environments.

[0028] These units work together to ensure that the data of the aircraft under different flight states can be effectively processed, modeled, and optimized, providing accurate support for the design and evaluation of the flight control system.

[0029] The flight control system construction module 20 is used to construct a flight control system that can be simulated and run according to the flight state, flight mission, and flight environment. Among them, the flight control system includes an active flight control system and an automatic flight control system.

[0030] Specifically, the flight control system construction module is the core component of the entire flight control system development platform. Its main task is to construct a flight control system that can be simulated and run based on the analysis results of the flight state, flight mission, and flight environment. This module reasonably organizes, integrates, and configures each functional component of the flight control system to ensure that the final flight control system can be effectively simulated and verified in the simulation environment. First, it is used to construct a flight control system that can be simulated and run according to the flight state, flight mission, and flight environment. Among them, the flight control system includes an active flight control system and an automatic flight control system. The active flight control system is responsible for controlling the motion state of the aircraft in real time to meet the requirements of specific flight missions. This system usually involves control law design and is mainly used for tasks such as aircraft attitude and trajectory control. The design of the active flight control system usually requires high dynamic response speed and stability. The automatic flight control system is used to automatically execute the predetermined flight mission during the flight, usually including functions such as heading control, speed control, and altitude control. This system can automatically adjust the state and parameters of the aircraft based on the requirements of the flight mission, reducing manual intervention. According to the input of the flight state, flight mission, and flight environment provided by the platform, the flight control system construction module will automatically select the appropriate control module. Through the analysis of the flight mission and flight state, the system will select different control strategies for different flight missions to ensure that the flight control system can operate effectively in each flight stage (such as takeoff, cruise, landing, etc.).

[0031] The flight control system construction module not only comprehensively considers data such as the flight state, flight mission, and flight environment, but also quickly constructs a flight control system that can be simulated and run by integrating different control modules. This system can effectively support the active flight control and automatic flight control of the aircraft, providing stable and accurate control capabilities for the execution of flight missions. At the same time, the modular and extensible design enables the system to flexibly adapt to different mission requirements and flight environments.

[0032] Furthermore, the flight control system construction module 20 further includes: The flight control architecture management unit is used to select an appropriate architecture template from the flight control architecture library according to the flight state. Among them, the flight control architecture library at least includes a PID control architecture, an optimal control architecture, and a robust control architecture.

[0033] Specifically, the core function of the flight control architecture management unit is to select an appropriate architecture template from a predetermined flight control architecture library according to the flight state. By selecting a suitable flight control architecture template, a flight control system suitable for the current flight state can be quickly constructed, ensuring stable and precise control of the aircraft under various flight tasks and environments. First, a flight control architecture library is constructed, and the flight control architecture library at least includes a PID control architecture, an optimal control architecture, and a robust control architecture. Among them, PID control is a classic feedback control method, which is widely used in the attitude control and trajectory tracking control of aircraft. PID control adjusts the output of the control system through three main parameters (proportional term, integral term, and derivative term), and is suitable for relatively simple and stable flight tasks, and requires the aircraft to respond quickly under the condition of less uncertainty. The optimal control architecture is dedicated to optimizing the system performance index (such as minimizing energy consumption, minimizing time, maximizing accuracy, etc.) under given constraints. The optimal control method is often used in scenarios with complex flight tasks, such as long-term cruising, complex trajectory tracking, etc., and is suitable for use when the performance of the aircraft needs to be globally optimized. The robust control architecture is used to handle the control problems of the aircraft in complex or uncertain environments, especially when the dynamic characteristics of the system are not fully known or the external disturbance is large. Robust control can ensure that the system still maintains good performance in the presence of uncertainties and external disturbances, and is suitable for flight tasks in the face of highly dynamic changing environments, emergencies, or uncertain conditions.

[0034] Next, an appropriate architecture template is selected from the flight control architecture library according to the flight state. The flight control architecture selection module automatically determines the most suitable control architecture type based on the flight state, flight task, and environmental conditions of the aircraft. For example, when the aircraft is performing complex tasks or in a high-dynamic environment, the system may automatically select the robust control architecture. In a normal flight state, the PID control architecture may be sufficient to meet the task requirements. Among them, the selected flight control architecture templates are not only a preset control framework within the platform, but they can also be flexibly configured according to the needs of specific flight tasks. For example, the PID control architecture can adjust the proportional, integral, and derivative parameters according to different control accuracy requirements. The optimal control architecture can dynamically adjust the optimization strategy based on real-time data; the robust control architecture can adjust the robustness parameters of the system to cope with more complex external environments.

[0035] The flight control architecture selection module realizes the efficient construction of the flight control system by selecting the most suitable architecture template from a pre-determined flight control architecture library. This module can automatically select a PID control architecture, an optimal control architecture, or a robust control architecture according to the flight state, flight mission, and flight environment conditions of the aircraft, so as to provide accurate control strategies for different flight requirements. Through intelligent architecture selection and adaptive optimization, the flight control architecture selection module can improve the control performance of the aircraft and ensure its stable operation under various complex tasks and environments.

[0036] The flight control parameter configuration unit is used to configure the parameters of the predetermined control components according to the flight state, flight mission, and flight environment.

[0037] Furthermore, the present application further includes the following steps: Configure the gain parameters, feedback coefficients, and filtering parameters of the control algorithm according to the flight state; configure the sampling frequency, sampling range, and sampling accuracy of the sensor according to the flight state and flight environment; set the response time, control signal amplitude, and control accuracy of the actuator according to the flight mission.

[0038] Specifically, the function of the flight control parameter configuration unit is to automatically adjust and configure the relevant parameters of the control algorithm, the sampling parameters of the sensor, and the response characteristics of the actuator according to the flight state, flight environment, and flight mission requirements, with the aim of ensuring that the flight control system can achieve the optimal control effect and provide stable and accurate flight control performance under different flight environments and task conditions.

[0039] First, configure the gain parameters, feedback coefficients, and filtering parameters of the control algorithm according to the flight state, that is, according to the flight state (such as state variables such as the attitude, speed, and acceleration of the aircraft), automatically configure the gain parameters in the control algorithm. These gain parameters control the reaction intensity of the flight control system to the input signal and directly affect the dynamic response of the aircraft. For example, in some states, higher gains may be required to achieve fast response, while in other states, lower gains may be required to avoid over-response. The control system of the aircraft usually includes a feedback loop, and the feedback coefficient determines the response strength of the system to the deviation. According to the flight state and mission requirements, the system will automatically adjust the feedback coefficient to optimize the control stability of the aircraft. For example, during high-speed cruise or flight stabilization, the system may select a lower feedback coefficient to reduce over-adjustment of the control system. During takeoff or sharp turns, a higher feedback coefficient may be required to ensure sufficient control force. The filter is used to smooth the sensor data, remove noise and interference. According to the flight environment (such as external factors such as wind speed, temperature, and air pressure), the system will automatically select the appropriate filter type and filtering parameters to reduce the influence of data noise on the control system, thereby improving the control accuracy and stability.

[0040] Next, configure the sampling frequency, sampling range, and sampling accuracy of the sensor according to the flight state and flight environment. Among them, the sampling frequency of the sensor determines the real-time performance of the flight control system to obtain data. According to the flight state of the aircraft (such as flight speed, acceleration, etc.) and flight environment (such as high-dynamic or stable flight conditions), the system will automatically adjust the sampling frequency of the sensor to balance real-time performance and the load of data processing. During high-speed flight, a higher sampling frequency may be required to obtain more accurate flight data. While during low-speed and stable flight, a lower sampling frequency is sufficient. The range of the sensor determines the upper and lower limits of the measured data. According to the mission requirements and flight state of the aircraft (such as flight altitude, speed, etc.), the system will automatically adjust the sampling range of the sensor. For example, during high-altitude flight, a larger range may be required to adapt to the altitude change of the aircraft. During low-altitude flight, a more precise range may be required to improve the measurement accuracy of the sensor. The control system of the aircraft has relatively high requirements for the accuracy of sensor data. Therefore, it is necessary to configure the sampling accuracy of the sensor according to the flight mission. For example, during the execution of a precise landing mission, a higher sampling accuracy is required to ensure accurate position information. While in the normal flight state, a lower accuracy can be used to save computing resources.

[0041] Then, set the response time, control signal amplitude, and control accuracy of the actuator according to the flight mission. Among them, the response time of the actuator determines the time required for the flight control system to issue a control command to the actual execution of the control action. According to the flight state and flight mission (such as sharp turns or high-speed flight), the system will automatically configure the response time of the actuator. Some fast-dynamic tasks may require a faster actuator response, while relatively stable tasks can tolerate a longer response time. The amplitude of the control signal determines the working intensity of the aircraft control components (for example, the range of the rudder surface deflection angle, the thrust of the thruster, etc.). According to the needs of the flight mission and different flight states, the system will automatically configure the control signal amplitude of the actuator. In high-dynamic tasks, a larger amplitude of the control signal may be required to cope with the rapid changes of the aircraft. During smooth cruising, the amplitude may be smaller to save energy and ensure system stability. The control accuracy determines the control accuracy that the actuator can achieve. According to the accuracy requirements of the flight mission, the system will automatically adjust the control accuracy of the actuator. For example, during precise landing, low-altitude flight, or high-speed maneuvering, the control accuracy requirements of the actuator are relatively high; during normal cruising or larger flight missions, a lower control accuracy can be accepted.

[0042] The flight control parameter configuration unit automatically configures the relevant parameters of the control algorithm, sensors, and actuators according to the requirements of the flight state, flight environment, and flight mission, ensuring that the flight control system can achieve the best control effect under various complex flight conditions, thereby improving the stability, response speed, and accuracy of the aircraft. Through this configuration process, the flight control system can more intelligently adapt to various mission requirements and environmental changes, and achieve the efficient execution of flight missions.

[0043] The flight control module selection unit is used to match and obtain an adapted control module from the flight control module library according to the flight state, flight mission, and flight environment.

[0044] Furthermore, the present application further includes the following steps: Configure the flight control module library, where the flight control module library includes a number of control modules, and the control modules are marked with module-adapted flight conditions. Input the flight state, flight mission, and flight environment into the flight control module library, and perform similarity comparison with a number of adapted flight conditions respectively. Set the control module corresponding to the adapted flight condition with the highest similarity as the adapted control module.

[0045] Specifically, the main function of the flight control module selection unit is to intelligently select and adapt the most suitable flight control module through the analysis of the flight state, flight mission, and flight environment. This module automatically screens out the control module that best matches the current flight conditions from the flight control module library to ensure that the flight control system can provide the optimal control strategy under different flight missions and environments.

[0046] First, configure the flight control module library, where the flight control module library includes a number of control modules, and the control modules are marked with module-adapted flight conditions. The flight control module library contains a variety of flight control modules, and each module is optimized for different flight conditions. The types of flight control modules can include PID control modules, optimal control modules, robust control modules, fuzzy control modules, etc. Each module is designed to adapt to a specific flight mission or environment. Each control module is marked with the adapted flight conditions, such as flight state (e.g., static, accelerating, cruising, etc.), flight mission (e.g., precise landing, cruising flight, sharp turn, etc.), and flight environment (e.g., high altitude, low altitude, wind speed, temperature, etc.).

[0047] Next, input the flight state, flight mission, and flight environment into the flight control module library, and perform similarity comparisons with several adapted flight conditions respectively. According to the input flight state, flight mission, and flight environment, the system compares this information with the adapted flight conditions in the flight control module library. By calculating the similarity of the flight conditions, the flight control module most similar to the current flight state, mission, and environment is found; through the similarity comparison, the system will automatically select a most suitable control module according to the highest similarity value. For example, if the current flight mission of the aircraft is precise landing and the environmental condition is low-altitude flight, the system may select a PID control module suitable for low-altitude high-precision control; if the flight mission is high-speed dynamic flight, an optimal control or robust control module may be selected to ensure the stability of the system in a changing environment. Finally, set the control module corresponding to the adapted flight condition with the highest similarity as the adapted control module. Once the most suitable control module is selected, the system will integrate this module into the flight control system to ensure that this control module can work in coordination with other system components (such as sensors, actuators, control algorithms, etc.).

[0048] By automatically selecting the control module in the flight control module library that best matches the current flight state, flight mission, and flight environment, the efficient adaptation of the flight control system is achieved. This module intelligently screens out the best control module through the similarity comparison method and performs real-time optimization according to the requirements of the aircraft to ensure the smooth execution of the flight mission. Through this process, the flight control system can provide efficient, stable, and precise control strategies under various flight conditions.

[0049] Furthermore, the present application further includes the following steps: Randomly select a first adapted flight condition from among the several adapted flight conditions of several control modules, where the first adapted flight condition includes a first adapted flight state, a first adapted flight mission, and a first adapted flight environment; perform similarity comparisons between the flight state, flight mission, and flight environment and the first adapted flight state, first adapted flight mission, and first adapted flight environment respectively, output a first state similarity, a first mission similarity, and a first environment similarity, calculate the first similarity by weighted calculation, and sequentially analyze and output the similarities of several adapted flight conditions.

[0050] Specifically, first, randomly select any one of the several adapted flight conditions of several control modules as the first adapted flight condition. Among them, the first adapted flight condition includes the first adapted flight state, the first adapted flight mission, and the first adapted flight environment. Then, compare the flight state, flight mission, and flight environment with the first adapted flight state, the first adapted flight mission, and the first adapted flight environment respectively. Common calculation methods can be Euclidean distance, cosine similarity, etc., and output the first state similarity, the first mission similarity, and the first environment similarity. Then, perform weighted calculation on the first state similarity, the first mission similarity, and the first environment similarity. This weighted calculation can be adjusted according to the priority of the mission and the importance of the flight conditions. For example, if the flight mission is more important than the flight state or environment in the current situation, a higher weight can be assigned to the similarity of the flight mission. Set the result of the weighted calculation as the first similarity.

[0051] Then continue to analyze and calculate the similarities of other adapted flight conditions in the flight control module library in turn. Each adapted flight condition will go through similarity comparison with the current flight state, mission, and environment, and calculate the corresponding similarity value. This process not only improves the adaptability of the flight control system but also ensures that the flight control system can automatically and intelligently select the optimal control strategy under various complex flight conditions.

[0052] The flight control system evaluation and optimization module 30 is used to evaluate the performance of the active flight control system and the automatic flight control system, and perform iterative optimization on the active flight control system and the automatic flight control system according to the performance evaluation results until the predetermined flight requirements are met.

[0053] Furthermore, the flight control system evaluation and optimization module 30 further includes: The active flight control evaluation unit is used to evaluate the response characteristics of the active flight control system in different flight states through the lateral-directional dimension and the longitudinal dimension in the simulation operation space, and output the active response characteristics. Among them, the active response characteristics include attitude control accuracy, transition response time, system stability coefficient, and system anti-interference coefficient. The automatic flight control evaluation unit is used to evaluate the response characteristics of the automatic flight control system in different flight states through the lateral-directional dimension and the longitudinal dimension in the simulation operation space, and output the automatic response characteristics. Among them, the automatic response characteristics include path tracking accuracy, automatic navigation accuracy, and speed control error.

[0054] Specifically, the active flight control evaluation unit is a module used to evaluate the performance of the active flight control part in the flight control system. Its evaluation is carried out by simulating the aircraft under different flight states in the simulation operation space, focusing on the response characteristics of the aircraft, especially in the lateral-directional dimension (including controls such as heading, roll, pitch, etc.) and the longitudinal dimension (such as altitude, speed, etc.). In the lateral-directional evaluation dimension, the evaluation of attitude control accuracy, transition response time, system stability coefficient, and system anti-interference coefficient is carried out respectively. Attitude control accuracy refers to evaluating the control accuracy of the aircraft in the lateral-directional dimension (such as heading, roll, and pitch), including the response of the aircraft to the pilot's commands and whether the system can accurately maintain the target attitude. The transition response time refers to the time required for the aircraft to transition from one flight state to the target state. This index reflects the dynamic performance of the flight control system. An overly long transition response time may affect the timely completion of the flight mission; the system stability coefficient reflects the stability of the flight control system. Under different flight states, whether the aircraft is prone to oscillation or instability. The higher the stability coefficient, the more stable the system. The system anti-interference coefficient evaluates whether the system can maintain stable flight under external disturbances (such as airflows, wind speed changes, control disturbances, etc.). The larger the system anti-interference coefficient, the stronger the anti-interference ability of the system. Similarly, in the longitudinal dimension (such as flight altitude, speed control, etc.), the response ability and error correction ability of the system are evaluated. This dimension mainly involves the stability and response ability of the aircraft in terms of flight altitude, ascent and descent, etc. Output the active response characteristics, where the active response characteristics include attitude control accuracy, transition response time, system stability coefficient, and system anti-interference coefficient.

[0055] The automatic flight control evaluation unit is mainly used to evaluate the automatic flight control part in the flight control system. In the simulation environment, the automatic flight control system will automatically execute tasks according to the flight mission without relying on manual control by the pilot. The focus of the evaluation is its performance of automatic flight control under different flight states (such as cruising, climbing, descending, etc.). For example, evaluate whether the automatic flight control system can accurately track the predetermined flight path in the lateral-directional dimension. When the accuracy is poor, the aircraft may deviate from the target path, affecting the completion of the flight mission. Evaluate the accuracy of the aircraft to automatically adjust the flight path according to the navigation system (such as GPS, inertial navigation, etc.), and whether it can accurately complete the automatic navigation task. Evaluate the control accuracy of the automatic flight control system for the speed of the aircraft during flight, especially its ability to maintain the target speed and respond to speed changes. The longitudinal dimension evaluation includes the control performance of the aircraft for longitudinal indicators such as flight altitude and acceleration during flight, and evaluate the performance of the automatic flight control system in these dimensions to ensure the longitudinal flight accuracy and mission achievement in the flight mission. Output the automatic response characteristics, where the automatic response characteristics include path tracking accuracy, automatic navigation accuracy, and speed control error.

[0056] Through the functions of the above two evaluation units, the active and automatic control parts of the flight control system can be evaluated in detail under different flight states, covering the main control performance indicators during the flight of the aircraft, including response accuracy, stability, anti-interference ability, and mission completion.

[0057] Furthermore, the flight control system evaluation and optimization module 30 further includes: A flight control system optimization unit, configured to calculate the deviation of the active response characteristics and the automatic response characteristics according to the predetermined flight requirements, and respectively perform iterative optimization on the active flight control system and the automatic flight control system according to the deviation calculation result until the predetermined flight requirements are met.

[0058] Specifically, first, according to the predetermined flight requirements (such as flight attitude, transition response time, system stability, and anti-interference ability, etc.), calculate the deviation between the active response characteristics (such as attitude control accuracy, transition response time, system stability coefficient, and anti-interference coefficient) shown by the active flight control system during actual operation and the expected target. This deviation calculation can be achieved through an error function (such as mean square error, weighted error, etc.), and the error function calculates the difference between the active response characteristics and the predetermined requirements. Then, based on the deviation calculation result, analyze the influence of each parameter (such as gain coefficient, feedback coefficient, etc.) in the flight control system on the flight response characteristics. For the calculated deviation, use an optimization algorithm (such as genetic algorithm, particle swarm optimization, gradient descent, etc.) to adjust the control parameters of the active flight control system, and update the system model according to the optimization result. Then, after each optimization, re-perform the simulation evaluation to check whether the optimized flight control system can reduce the deviation and gradually meet the predetermined flight requirements. Through multiple iterative processes, continuously optimize the response characteristics of the flight control system until the specified control accuracy, transition time, system stability, and anti-interference ability are achieved. Finally, output the optimized active flight control system to ensure that the specified response accuracy and stability requirements can be met during the flight.

[0059] On the other hand, according to the predetermined flight requirements (such as path tracking accuracy, automatic navigation accuracy, speed control error, etc.), calculate the deviation between the automatic response characteristics (path tracking accuracy, automatic navigation accuracy, speed control error) obtained by the automatic flight control system in the simulation and the target values. Using the same method, quantify and evaluate each response characteristic through an error function. Then, based on the deviation calculation results, analyze the influence of control parameters (such as control gain, sampling frequency, sampling accuracy, etc.) on the response of the automatic flight control system. Adjust the system parameters through an optimization algorithm so that the automatic flight control system can better meet the flight task requirements such as path accuracy, navigation accuracy, and speed control. Then, during the optimization process, gradually reduce the deviation through repeated simulation evaluations until the automatic flight control system can accurately complete the predetermined automatic flight task and meet the flight accuracy and stability requirements. Finally, output the automatically flight control system after iterative optimization to ensure that the system has a high degree of automation and accuracy under different flight tasks.

[0060] Through this method of deviation calculation and iterative optimization, the development efficiency and accuracy of the flight control system can be greatly improved, and the errors and complexities in the traditional manual adjustment process can be reduced; the optimization process can automatically adjust the flight control system parameters in various flight states and flight tasks, and finally enable the flight control system to achieve higher automation, response accuracy, and system stability, so as to meet the high-standard flight requirements.

[0061] The flight control system development platform based on graphics processing technology provided by the embodiments of the present invention has at least the following technical effects: By preprocessing the flight characteristic data of the aircraft, generate a state point matrix, and analyze and determine the flight state, flight task, and flight environment of the aircraft according to the state point matrix; then construct a flight control system that can be simulated and run according to the flight state, flight task, and flight environment, where the flight control system includes an active flight control system and an automatic flight control system; then evaluate the performance of the active flight control system and the automatic flight control system, and perform iterative optimization on the active flight control system and the automatic flight control system according to the performance evaluation results until the predetermined flight requirements are met; that is to say, through automated modeling processing, manual intervention can be reduced, and the required flight control system model can be quickly generated, realizing efficient and automated development of the flight control system, and effectively improving the modeling efficiency and modeling quality of the flight control system.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept.

[0063] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A flight control system development platform based on graphics processing technology, characterized by: The flight control system development platform includes: A flight characteristic data processing module is used to pre-process the flight characteristic data of the aircraft, generate a state point matrix, and analyze and determine the flight state, flight mission and flight environment of the aircraft based on the state point matrix; A flight control system construction module, used to construct a flight control system capable of simulation operation according to the flight state, flight mission and flight environment, wherein the flight control system includes an active flight control system and an automatic flight control system; The flight control system evaluation and optimization module is used to perform performance evaluation on the active flight control system and the automatic flight control system, and iteratively optimize the active flight control system and the automatic flight control system according to the performance evaluation results until the predetermined flight requirements are met.

2. The flight control system development platform based on graphics processing technology according to claim 1, characterized in that: The flight characteristics data processing module also includes: The pneumatic data processing unit is used to clean, filter out noise and format the pneumatic data, and convert the raw data into structured data that can be used for flight control system modeling; A state point selection unit, used to select a flight state point according to the type of aircraft and determine a state variable of the flight state point; A state point balancing unit, used to ensure that the aircraft is in a balanced state at the flight state point; The state point linearization unit is used to perform linearization processing on the flight state point.

3. The flight control system development platform based on graphics processing technology according to claim 1, characterized in that: The flight control system building module also includes: A flight control architecture management unit, configured to select an adaptation architecture template in a flight control architecture library according to the flight state, wherein the flight control architecture library includes at least a PID control architecture, an optimal control architecture, and a robust control architecture; A flight control parameter configuration unit, used to configure parameters of predetermined control components according to the flight state, flight mission and flight environment; The flight control module selection unit is used to match and obtain an adaptation control module in the flight control module library according to the flight state, flight mission and flight environment.

4. The flight control system development platform based on graphics processing technology according to claim 3, characterized in that: Parameter configuration of predetermined control components is performed according to the flight state, flight mission and flight environment, including: Configure the gain parameters, feedback coefficients and filtering parameters of the control algorithm according to the flight state; Configure the sampling frequency, sampling range and sampling accuracy of the sensor according to the flight state and flight environment; The response time, control signal amplitude and control accuracy of the actuator are set according to the flight mission.

5. The flight control system development platform based on graphics processing technology according to claim 3, characterized in that: According to the flight state, flight mission and flight environment, matching and acquiring the adaptation control module in the flight control module library includes: Configuring a flight control module library, wherein the flight control module library includes a plurality of control modules, and the control module is identified with a module adaptation flight condition; The flight status, flight mission and flight environment are input into the flight control module library, and are respectively compared with a number of adaptive flight conditions for similarity, and the control module corresponding to the adaptive flight condition with the highest similarity is set as the adaptive control module.

6. The flight control system development platform based on graphics processing technology according to claim 5, characterized in that: Similar comparisons were made with several adaptive flight conditions, including: Randomly selecting a first adaptive flight condition from a plurality of adaptive flight conditions of a plurality of control modules, wherein the first adaptive flight condition includes a first adaptive flight state, a first adaptive flight mission, and a first adaptive flight environment; The flight state, flight mission, and flight environment are respectively compared with the first adapted flight state, the first adapted flight mission, and the first adapted flight environment, and the first state similarity, the first mission similarity, and the first environment similarity are output. The first similarity is obtained by weighted calculation, and several similarities of several adapted flight conditions are analyzed and output in turn.

7. The flight control system development platform based on graphics processing technology according to claim 1, characterized in that: The flight control system evaluation and optimization module also includes: An active flight control evaluation unit is used to evaluate the response characteristics of the active flight control system under different flight states in the simulation operation space through the lateral and longitudinal dimensions, and output active response characteristics, wherein the active response characteristics include attitude control accuracy, transition response time, system stability coefficient, and system anti-interference coefficient; The automatic flight control evaluation unit is used to evaluate the response characteristics of the automatic flight control system under different flight conditions in the simulation operation space through the lateral heading dimension and the longitudinal dimension, and output the automatic response characteristics, wherein the automatic response characteristics include path tracking accuracy, automatic navigation accuracy and speed control error.

8. The flight control system development platform based on graphics processing technology according to claim 7, characterized in that: The flight control system evaluation and optimization module also includes a flight control system optimization unit, which is used to calculate the deviation of the active response characteristics and the automatic response characteristics according to the predetermined flight requirements, and iteratively optimize the active flight control system and the automatic flight control system according to the deviation calculation results until the predetermined flight requirements are met.

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