Low-altitude aircraft attitude control system based on intelligent information data analysis

Abstract

The invention relates to the technical field of low-altitude aircraft attitude control, in particular to a low-altitude aircraft attitude control system based on intelligent information data analysis. The method comprises the following steps: a data acquisition and storage unit acquires aircraft data and environmental parameter data; the intelligent information analysis unit analyzes the real-time attitude of the aircraft by using a nonlinear dynamics technology and a singular perturbation theory technology, and judges whether the aircraft is in a safe state or not by using a singular perturbation analysis technology; the single aircraft control unit generates an optimal control strategy by using a linear quadratic regulator in combination with an advanced attitude control algorithm; the multi-aircraft control unit adopts a distributed control strategy to control coordinated flight among the aircrafts; and the control display terminal unit is used for providing an integrated human-computer interaction interface. According to the low-altitude aircraft attitude control system based on intelligent information data analysis, nonlinear dynamics and singular perturbation theories are combined, and cooperative flight control of a single aircraft or multiple aircrafts is realized by adopting a distributed control architecture.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude aircraft attitude control, and in particular to a low-altitude aircraft attitude control system based on intelligent information data analysis. Background Art

[0002] The low-altitude aircraft attitude control system based on intelligent information data analysis is designed to achieve real-time coordinated flight and high-precision attitude control of multiple aircraft. By integrating nonlinear dynamic modeling, singular perturbation theory and distributed control strategy, the attitude and flight path of each aircraft are controlled to achieve optimal control of the overall stability, efficiency and flight safety of the system.

[0003] The existing low-altitude aircraft attitude control systems usually find it difficult to achieve high-precision real-time attitude estimation and path optimization in complex dynamic environments. In addition, due to environmental disturbances, sensor noise, communication delays between multiple aircraft and the risk of single-point failures, it can lead to inaccurate attitude estimation, path planning conflicts, slow system response and overall low flight efficiency. Therefore, a low-altitude aircraft attitude control system based on intelligent information data analysis is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a low-altitude aircraft attitude control system based on intelligent information data analysis to solve the problems raised in the above background technology, such as inaccurate attitude estimation, path planning conflicts, slow system response and low overall flight efficiency due to external environmental disturbances, sensor noise, communication delays between multiple aircraft and single point failure risks.

[0005] To achieve the above object, the present invention aims to provide a low-altitude aircraft attitude control system based on intelligent information data analysis, comprising: A data acquisition and storage unit, wherein the data acquisition and storage unit uses sensors to collect aircraft data and environmental parameter data in real time, and uses an embedded storage system to store the aircraft data and environmental parameter data in a database; It also includes an intelligent information analysis unit, which uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft based on the aircraft data and environmental parameter data, and uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state, and calculates the target attitude of the aircraft; It also includes a single aircraft control unit, which generates an optimal control strategy based on the target attitude of the aircraft using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the aircraft attitude in real time based on the optimal control strategy; It also includes a multi-aircraft control unit, which uses a distributed control strategy to control the coordinated flight of each aircraft and feeds back the status of each aircraft to the intelligent information analysis unit and the control display terminal unit; It also includes a control and display terminal unit, which is used to provide an integrated human-computer interaction interface and display aircraft data and environmental parameter data in real time through an AR device.

[0006] As a further improvement of the technical solution, the data acquisition storage unit includes a data acquisition module and a data storage module; Wherein, the data acquisition module uses sensors to collect aircraft data and environmental parameter data in real time; Aircraft data includes: roll angle , Pitch angle , yaw angle ,longitude ,latitude ,high , Eastward speed , Northbound speed , vertical speed , X-axis acceleration , Y-axis acceleration , Z-axis acceleration , Roll angular velocity , pitch angular velocity , yaw angular velocity ; Environmental parameter data include: horizontal wind speed , vertical wind speed , wind direction angle ; The data storage module stores the acquired aircraft data and environmental parameter data in a database in real time using an embedded storage system, and transmits the aircraft data and environmental parameter data to a control and display terminal unit.

[0007] As a further improvement of the technical solution, the intelligent information analysis unit includes a dynamic posture prediction module and a safety assessment optimization module; The dynamic attitude prediction module uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft based on the aircraft data and environmental parameter data; The safety assessment optimization module uses singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft from the dynamic attitude prediction module, and calculates the target attitude of the aircraft through an optimization algorithm.

[0008] As a further improvement of the technical solution, the dynamic attitude prediction module uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft based on the aircraft data and environmental parameter data. The specific method steps are as follows: S2.1.1. Based on the aircraft data and environmental parameter data, a nonlinear dynamics model is constructed using nonlinear dynamics technology: Nonlinear dynamics models: ; in, For time; is the mass of a single spacecraft; is the acceleration due to gravity; is the eastward thrust; is the northward thrust; is the vertical thrust; It is eastward resistance; It is the northward resistance; is the vertical resistance force; S2.1.2. Using singular perturbation theory, the nonlinear dynamic model of the spacecraft is divided into fast dynamic equations and slow dynamic equations: Fast dynamic equation: ; Slow dynamic equation: ; in, is a fast variable; is a slow variable; is the time scale of fast dynamics relative to slow dynamics; Fast variable Equations based on time derivatives; Slow variable Equations based on time derivatives; S2.1.3. Based on the fast dynamic equation and the slow dynamic equation, the extended Kalman filter is used to calculate and estimate the real-time attitude of the aircraft: State prediction equation: ; in, For the time steps; For the time step Predicted fast variable status; is the time step; Fast variable Equations based on time derivatives; State update equation: ; in, For the time step Updated fast variable status; is the Kalman gain matrix; For the time step The observation vector of is the observation matrix; Real-time pose estimation equation: ; in, The real-time attitude of the vehicle.

[0009] As a further improvement of the technical solution, the safety assessment optimization module uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft in the dynamic attitude prediction module, and calculates the target attitude of the aircraft through the optimization algorithm. The specific method steps are as follows: S2.2.1, based on time Roll angle , Pitch angle and yaw angle Constructing aircraft safety assessment indicators: Aircraft safety assessment indicators : ; in, For time Roll angle; For time The pitch angle; For time The yaw angle; is the maximum safety threshold of the rolling angle; is the maximum safety threshold of the pitch angle; is the maximum safety threshold of the yaw angle; S2.2.2. Based on the real-time attitude of the aircraft As well as the fast dynamic equations, the safe state equations are constructed using the singular perturbation theory to determine whether the current posture is in a safe state: Safe state equations: ; in, It is the time derivative of the vehicle safety assessment index; is the system matrix; is the input matrix; is the external disturbance input; is the basic constant matrix of the spacecraft; Depends on the real-time attitude of the aircraft Matrix of Solve the safety state equations to obtain the aircraft safety assessment index ; like , the current attitude of the spacecraft is in a safe state; On the contrary, if the current attitude of the aircraft is not in a safe state, execute S2.2.3; S2.2.3. Based on the real-time attitude of the aircraft and external disturbance input , construct the optimization objective function: Constructing the vehicle state vector : ; Constructing the optimization objective function : ; in, for eastward speed of time; for northward velocity of time; for The vertical speed of time; for X-axis acceleration of time; is the Y-axis acceleration of time; for Z-axis acceleration of time; The upper limit of the optimized time interval; is the transpose operation; S2.2.4. Minimize the optimization objective function Get the target attitude of the spacecraft: ; in, is the target attitude of the vehicle.

[0010] As a further improvement of the technical solution, the single aircraft control unit generates an optimal control strategy based on the aircraft target attitude using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the aircraft attitude in real time based on the optimal control strategy. The specific method steps are as follows: S3.1. According to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations; S3.2. Based on the vehicle control objective function and constraint equations, use a linear quadratic regulator combined with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy; S3.3. Convert the optimal control strategy into control instructions and send them to the spacecraft to adjust its attitude.

[0011] As a further improvement of the technical solution, in S3.1, according to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations, the specific method is as follows: Vehicle control objective function : ; in, is the objective function for vehicle control; The constraint equations are: ; in, The minimum limit for external disturbance input; The maximum limit for external disturbance input; is the minimum limit of the vehicle state vector; is the maximum limit of the vehicle state vector; In S3.2, based on the vehicle control objective function and the constraint condition equation group, a linear quadratic regulator is used in combination with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy. The specific method is as follows: Construct the Riccati equation: ; Solving the Riccati equation yields , calculate the optimal control strategy To minimize the vehicle control objective function : ; in, is the optimal control strategy; is the solution of the Riccati equation.

[0012] As a further improvement of the technical solution, the multi-vessel control unit includes a distributed collaborative control module and a state feedback transmission module; The distributed cooperative control module uses a distributed control strategy to control the coordinated flight between the aircraft; The state feedback transmission module feeds back the state of each aircraft to the intelligent information analysis unit and the control display terminal unit.

[0013] As a further improvement of the technical solution, the distributed cooperative control module uses a distributed control strategy to achieve coordinated flight between various aircraft. The specific method steps are as follows: S4.1.1. Based on the distributed control strategy, design an independent but coordinated distributed control strategy for each aircraft: ; in, Indicates a spacecraft; Indicates a spacecraft; For the Spacecraft at time External disturbance input; For the Spacecraft at time The target attitude of the spacecraft; For the Spacecraft at time The spacecraft state vector of For the Spacecraft at time The spacecraft state vector of For the The aircraft and The cooperative gain matrix between the spacecraft; S4.1.2, based on the time of each aircraft External disturbance input , removing the influencing factors of environmental parameter data, the distributed control strategy of each spacecraft is obtained: ; ; in, For the Spacecraft at time Distributed control strategy; For the Spacecraft at time Environmental parameter data influencing factor vector; is the environmental impact coefficient matrix of the th aircraft.

[0014] As a further improvement of the technical solution, the control display terminal unit includes an augmented reality display module and a user interaction control module; The augmented reality display module is used to receive the aircraft data and environmental parameter data from the data storage module and the status of each aircraft from the status feedback transmission module, and display them in real time on the visual AR device; The user interaction control module is used to provide an interaction interface between the operator and the aircraft.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In this low-altitude aircraft attitude control system based on intelligent information data analysis, real-time attitude analysis and safety assessment based on nonlinear dynamics and singular perturbation theory can achieve high-precision attitude prediction, dynamic safety monitoring, and timely response to potential safety risks.

[0016] 2. In this low-altitude aircraft attitude control system based on intelligent information data analysis, efficient collaborative flight control among multiple aircraft is achieved through a distributed control architecture, ensuring the overall stability and flight efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Data acquisition and storage unit; 2. Intelligent information analysis unit; 3. Single-vessel control unit; 4. Multi-vessel control unit; 5. Control and display terminal unit; 11. Data acquisition module; 12. Data storage module; 21. Dynamic posture prediction module; 22. Safety assessment and optimization module; 41. Distributed collaborative control module; 42. State feedback transmission module; 51. Augmented reality display module; 52. User interaction control module. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0019] See also Figure 1 As shown, a low-altitude aircraft attitude control system based on intelligent information data analysis is provided, including: A data acquisition and storage unit 1, wherein the data acquisition and storage unit 1 uses sensors to collect aircraft data and environmental parameter data in real time, and uses an embedded storage system to store the aircraft data and environmental parameter data in a database; The data acquisition and storage unit 1 includes a data acquisition module 11 and a data storage module 12; Wherein, the data acquisition module 11 uses sensors to collect aircraft data and environmental parameter data in real time; Aircraft data includes: roll angle , Pitch angle , yaw angle ,longitude ,latitude ,high , Eastward speed , Northbound speed , vertical speed , X-axis acceleration , Y-axis acceleration , Z-axis acceleration , Roll angular velocity , pitch angular velocity , yaw angular velocity ; Environmental parameter data include: horizontal wind speed , vertical wind speed , wind direction angle ; The data storage module 12 stores the acquired aircraft data and environmental parameter data in a database in real time using an embedded storage system, and transmits the aircraft data and environmental parameter data to the control and display terminal unit 5 .

[0020] It also includes an intelligent information analysis unit 2, which uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft based on the aircraft data and environmental parameter data, and uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state, and calculates the target attitude of the aircraft; Nonlinear dynamics technology is a mathematical method used to describe and analyze the dynamic behavior of a vehicle in a complex environment. It can capture the nonlinear characteristics of the system, such as changes in attitude angles and the effects of external disturbances. Singular perturbation theory is a method used to simplify and decompose systems with multi-time scale characteristics. By dividing the system into fast dynamic and slow dynamic parts, it is easier to deal with the analysis and control of complex systems more efficiently. In this embodiment, the nonlinear dynamics technology and the singular perturbation theory technology are combined and applied to the real-time attitude analysis of the aircraft, which can accurately capture the complex dynamic behavior of the aircraft and the changes under different time scales, improve the accuracy and calculation efficiency of attitude prediction, and ensure that the aircraft is always in a safe state and can quickly adjust to the target attitude; The intelligent information analysis unit 2 includes a dynamic posture prediction module 21 and a safety assessment optimization module 22; The dynamic attitude prediction module 21 analyzes the real-time attitude of the aircraft based on the aircraft data and the environmental parameter data using nonlinear dynamics technology and singular perturbation theory technology; The safety assessment optimization module 22 uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft obtained by the dynamic attitude prediction module 21, and calculates the target attitude of the aircraft through an optimization algorithm.

[0021] The dynamic attitude prediction module 21 analyzes the real-time attitude of the aircraft based on the aircraft data and the environmental parameter data using nonlinear dynamics technology and singular perturbation theory technology. The specific method steps are as follows: S2.1.1. Based on the aircraft data and environmental parameter data, a nonlinear dynamics model is constructed using nonlinear dynamics technology: Nonlinear dynamics models: ; in, For time; is the mass of a single spacecraft; is the acceleration due to gravity; is the eastward thrust; is the northward thrust; is the vertical thrust; It is eastward resistance; It is the northward resistance; is the vertical resistance force; S2.1.2. Using singular perturbation theory, the nonlinear dynamic model of the spacecraft is divided into fast dynamic equations and slow dynamic equations: Fast dynamic equation: ; Slow dynamic equation: ; in, is a fast variable; is a slow variable; is the time scale of fast dynamics relative to slow dynamics; Fast variable Equations based on time derivatives; Slow variable Equations based on time derivatives; S2.1.3. Based on the fast dynamic equation and the slow dynamic equation, the extended Kalman filter is used to calculate and estimate the real-time attitude of the aircraft: State prediction equation: ; in, For the time steps; For the time step Predicted fast variable status; is the time step; Fast variable Equations based on time derivatives; State update equation: ; in, For the time step Updated fast variable status; is the Kalman gain matrix; For the time step The observation vector of is the observation matrix; Real-time pose estimation equation: ; in, The real-time attitude of the vehicle.

[0022] The safety assessment optimization module 22 uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft obtained by the dynamic attitude prediction module 21, and calculates the target attitude of the aircraft through an optimization algorithm. The specific method steps are as follows: S2.2.1, based on time Roll angle , Pitch angle and yaw angle Constructing aircraft safety assessment indicators: Aircraft safety assessment indicators : ; in, For time Roll angle; For time The pitch angle; For time The yaw angle; is the maximum safety threshold of the rolling angle; is the maximum safety threshold of the pitch angle; is the maximum safety threshold of the yaw angle; S2.2.2. Based on the real-time attitude of the aircraft As well as the fast dynamic equations, the safe state equations are constructed using the singular perturbation theory to determine whether the current posture is in a safe state: Safe state equations: ; in, It is the time derivative of the vehicle safety assessment index; is the system matrix; is the input matrix; is the external disturbance input; is the basic constant matrix of the spacecraft; Depends on the real-time attitude of the aircraft Matrix of Solve the safety state equations to obtain the aircraft safety assessment index ; like , the current attitude of the spacecraft is in a safe state; On the contrary, if the current attitude of the aircraft is not in a safe state, execute S2.2.3; S2.2.3. Based on the real-time attitude of the aircraft and external disturbance input , construct the optimization objective function: Constructing the vehicle state vector : ; Constructing the optimization objective function : ; in, for eastward speed of time; for northward velocity of time; for The vertical speed of time; for X-axis acceleration of time; for Y-axis acceleration of time; for Z-axis acceleration of time; The upper limit of the optimized time interval; is the transpose operation; S2.2.4. Minimize the optimization objective function Get the target attitude of the spacecraft: ; in, is the target attitude of the vehicle.

[0023] It also includes a single aircraft control unit 3, which generates an optimal control strategy based on the target attitude of the aircraft using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the aircraft attitude in real time based on the optimal control strategy; The linear quadratic regulator is an optimization control method used to design a control strategy to minimize the performance index of the system. By solving the Riccati equation, a feedback gain matrix is ​​determined to generate the optimal control input. In this embodiment, the linear quadratic regulator is combined with an advanced attitude control algorithm to calculate the optimal control instructions in real time according to the current state and target attitude of the aircraft, providing a smooth and efficient control response, ensuring that the aircraft can adjust its attitude stably and accurately in a complex dynamic environment; The single aircraft control unit 3 generates an optimal control strategy based on the aircraft target attitude using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the aircraft attitude in real time based on the optimal control strategy. The specific method steps are as follows: S3.1. According to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations; S3.2. Based on the vehicle control objective function and constraint equations, use a linear quadratic regulator combined with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy; S3.3. Convert the optimal control strategy into control instructions and send them to the spacecraft to adjust its attitude.

[0024] In S3.1, according to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations, the specific method is as follows: Vehicle control objective function : ; in, is the objective function for vehicle control; The constraint equations are: ; in, The minimum limit for external disturbance input; The maximum limit for external disturbance input; is the minimum limit of the vehicle state vector; is the maximum limit of the vehicle state vector; In S3.2, based on the vehicle control objective function and the constraint condition equation group, a linear quadratic regulator is used in combination with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy. The specific method is as follows: Construct the Riccati equation: ; Solving the Riccati equation yields , calculate the optimal control strategy To minimize the vehicle control objective function : ; in, is the optimal control strategy; is the solution of the Riccati equation.

[0025] It also includes a multi-aircraft control unit 4, which uses a distributed control strategy to control the coordinated flight of each aircraft, and feeds back the status of each aircraft to the intelligent information analysis unit 2 and the control display terminal unit 5; Distributed control strategy is a control method that distributes control tasks to each spacecraft for independent execution, realizes the coordination and cooperative flight of the overall system through local information exchange and limited communication, and achieves the global flight goal; In this embodiment, the distributed control strategy is applied to the coordinated flight of multiple aircraft to promote efficient information sharing and distributed processing, and optimize the overall flight path and task allocation; The multi-vessel control unit 4 includes a distributed collaborative control module 41 and a state feedback transmission module 42; The distributed cooperative control module 41 uses a distributed control strategy to control the coordinated flight between the aircraft; The state feedback transmission module 42 feeds back the state of each aircraft to the intelligent information analysis unit 2 and the control display terminal unit 5 .

[0026] The distributed cooperative control module 41 uses a distributed control strategy to achieve coordinated flight between various aircraft. The specific method steps are as follows: S4.1.1. Based on the distributed control strategy, design an independent but coordinated distributed control strategy for each aircraft: ; in, Indicates a spacecraft; Indicates a spacecraft; For the Spacecraft at time External disturbance input; For the Spacecraft at time The target attitude of the spacecraft; For the Spacecraft at time The spacecraft state vector of For the Spacecraft at time The spacecraft state vector of For the The aircraft and The cooperative gain matrix between the spacecraft; S4.1.2, based on the time of each aircraft External disturbance input , removing the influencing factors of environmental parameter data, the distributed control strategy of each spacecraft is obtained: ; ; in, For the Spacecraft at time Distributed control strategy; For the Spacecraft at time Environmental parameter data influencing factor vector; For the The environmental impact coefficient matrix of each aircraft.

[0027] It also includes a control display terminal unit 5, which is used to provide an integrated human-computer interaction interface and display aircraft data and environmental parameter data in real time through an AR device; The control and display terminal unit 5 includes an augmented reality display module 51 and a user interaction control module 52; The augmented reality display module 51 is used to receive the aircraft data and environmental parameter data from the data storage module 12 and the status of each aircraft from the status feedback transmission module 42, and display them in real time on the visual AR device; The user interaction control module 52 is used to provide an interaction interface between the operator and the aircraft.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A low-altitude aircraft attitude control system based on intelligent information data analysis, characterized in that: include: A data acquisition and storage unit (1), wherein the data acquisition and storage unit (1) uses sensors to collect aircraft data and environmental parameter data in real time, and uses an embedded storage system to store the aircraft data and environmental parameter data in a database; An intelligent information analysis unit (2), the intelligent information analysis unit (2) uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft based on the aircraft data and environmental parameter data, and uses the singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state, and calculates the target attitude of the aircraft; A single aircraft control unit (3), wherein the single aircraft control unit (3) generates an optimal control strategy based on the target attitude of the aircraft using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the attitude of the aircraft in real time based on the optimal control strategy; A multi-vehicle control unit (4), wherein the multi-vehicle control unit (4) adopts a distributed control strategy to control the coordinated flight of each vehicle, and feeds back the status of each vehicle to the intelligent information analysis unit (2) and the control display terminal unit (5); A control display terminal unit (5) is used to provide an integrated human-machine interaction interface and to display aircraft data and environmental parameter data in real time through an AR device.

2. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 1 is characterized by: The data acquisition and storage unit (1) comprises a data acquisition module (11) and a data storage module (12); The data acquisition module (11) uses sensors to collect aircraft data and environmental parameter data in real time; Aircraft data includes: roll angle , Pitch angle , yaw angle ,longitude ,latitude ,high , Eastward speed , North speed , vertical speed , X-axis acceleration , Y-axis acceleration , Z-axis acceleration , Roll angular velocity , pitch angular velocity , yaw angular velocity ; Environmental parameter data include: horizontal wind speed , vertical wind speed , wind direction angle ; The data storage module (12) stores the acquired aircraft data and environmental parameter data in a database in real time using an embedded storage system, and transmits the aircraft data and environmental parameter data to a control and display terminal unit (5).

3. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 2 is characterized by: The intelligent information analysis unit (2) comprises a dynamic posture prediction module (21) and a safety assessment optimization module (22); The dynamic attitude prediction module (21) analyzes the real-time attitude of the aircraft based on the aircraft data and the environmental parameter data using nonlinear dynamics technology and singular perturbation theory technology; The safety assessment optimization module (22) uses a singular perturbation analysis technique to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft obtained by the dynamic attitude prediction module (21), and calculates the target attitude of the aircraft through an optimization algorithm.

4. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 3 is characterized by: The dynamic attitude prediction module (21) analyzes the real-time attitude of the aircraft based on the aircraft data and the environmental parameter data using nonlinear dynamics technology and singular perturbation theory technology. The specific method steps are as follows: S2.1.

1. Based on the aircraft data and environmental parameter data, a nonlinear dynamics model is constructed using nonlinear dynamics technology: Nonlinear dynamics models: ； in, For time; is the mass of a single spacecraft; is the acceleration due to gravity; is the eastward thrust; is the northward thrust; is the vertical thrust; It is eastward resistance; It is the northward resistance; is the vertical resistance force; S2.1.

2. Using singular perturbation theory, the nonlinear dynamic model of the spacecraft is divided into fast dynamic equations and slow dynamic equations: Fast dynamic equation: ； Slow dynamic equation: ； in, is a fast variable; is a slow variable; is the time scale of fast dynamics relative to slow dynamics; Fast variable Equations based on time derivatives; Slow variable Equations based on time derivatives; S2.1.

3. Based on the fast dynamic equation and the slow dynamic equation, the extended Kalman filter is used to calculate and estimate the real-time attitude of the aircraft: State prediction equation: ； in, For the time steps; For the time step Predicted fast variable status; is the time step; Fast variable Equations based on time derivatives; State update equation: ； in, For the time step Updated fast variable status; is the Kalman gain matrix; For the time step The observation vector of is the observation matrix; Real-time pose estimation equation: ； in, The real-time attitude of the vehicle.

5. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 4 is characterized in that: The safety assessment optimization module (22) uses singular perturbation analysis technology to determine whether the real-time attitude of the aircraft is in a safe state based on the real-time attitude of the aircraft obtained by the dynamic attitude prediction module (21), and calculates the target attitude of the aircraft through an optimization algorithm. The specific method steps are as follows: S2.2.1, based on time Roll angle , Pitch angle and yaw angle Constructing aircraft safety assessment indicators: Aircraft safety assessment indicators : ； in, For time Roll angle; For time The pitch angle; For time The yaw angle; is the maximum safety threshold of the rolling angle; is the maximum safety threshold of the pitch angle; is the maximum safety threshold of the yaw angle; S2.2.

2. Based on the real-time attitude of the aircraft As well as the fast dynamic equations, the safe state equations are constructed using the singular perturbation theory to determine whether the current posture is in a safe state: Safe state equations: ； in, It is the time derivative of the vehicle safety assessment index; is the system matrix; is the input matrix; is the external disturbance input; is the basic constant matrix of the spacecraft; Depends on the real-time attitude of the aircraft Matrix of Solve the safety state equations to obtain the aircraft safety assessment index ; like , the current attitude of the spacecraft is in a safe state; On the contrary, if the current attitude of the aircraft is not in a safe state, execute S2.2.3; S2.2.

3. Based on the real-time attitude of the aircraft and external disturbance input , construct the optimization objective function: Constructing the vehicle state vector : ； Constructing the optimization objective function : ； in, for eastward speed of time; for northward velocity of time; for The vertical speed of time; for X-axis acceleration of time; for Y-axis acceleration of time; for Z-axis acceleration of time; The upper limit of the optimized time interval; is the transpose operation; S2.2.

4. Minimize the optimization objective function Get the target attitude of the spacecraft: ； in, is the target attitude of the vehicle.

6. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 5 is characterized by: The single aircraft control unit (3) generates an optimal control strategy based on the aircraft target attitude using a linear quadratic regulator combined with an advanced attitude control algorithm, and the aircraft adjusts the aircraft attitude in real time based on the optimal control strategy. The specific method steps are as follows: S3.

1. According to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations; S3.

2. Based on the vehicle control objective function and constraint equations, use a linear quadratic regulator combined with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy; S3.

3. Convert the optimal control strategy into control instructions and send them to the spacecraft to adjust its attitude.

7. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 6 is characterized by: In S3.1, according to the aircraft state vector and the target attitude of the spacecraft , construct the vehicle control objective function and constraint equations, the specific method is as follows: Vehicle control objective function : ； in, is the objective function for vehicle control; The constraint equations are: ; in, The minimum limit for external disturbance input; The maximum limit for external disturbance input; is the minimum limit of the vehicle state vector; is the maximum limit of the vehicle state vector; In S3.2, based on the vehicle control objective function and the constraint condition equation group, a linear quadratic regulator is used in combination with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy. The specific method is as follows: Construct the Riccati equation: ； Solving the Riccati equation yields , calculate the optimal control strategy To minimize the vehicle control objective function : ； in, is the optimal control strategy; is the solution of the Riccati equation.

8. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 7 is characterized by: The multi-vessel control unit (4) comprises a distributed collaborative control module (41) and a state feedback transmission module (42); The distributed cooperative control module (41) uses a distributed control strategy to control the coordinated flight between the various aircraft; The state feedback transmission module (42) feeds back the state of each aircraft to the intelligent information analysis unit (2) and the control display terminal unit (5).

9. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 8 is characterized by: The distributed cooperative control module (41) uses a distributed control strategy to achieve coordinated cooperative flight between various aircraft. The specific method steps are as follows: S4.1.

1. Based on the distributed control strategy, design an independent but coordinated distributed control strategy for each aircraft: ； in, Indicates A spacecraft; Indicates a spacecraft; For the Spacecraft at time External disturbance input; For the Spacecraft at time The target attitude of the spacecraft; For the Spacecraft at time The spacecraft state vector of For the Spacecraft at time The spacecraft state vector of For the The aircraft and The cooperative gain matrix between the spacecraft; S4.1.2, based on the time of each aircraft External disturbance input , removing the influencing factors of environmental parameter data, the distributed control strategy of each spacecraft is obtained: ； ； in, For the Spacecraft at time Distributed control strategy; For the Spacecraft at time Environmental parameter data influencing factor vector; For the The environmental impact coefficient matrix of each aircraft.

10. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 9 is characterized in that: The control display terminal unit (5) comprises an augmented reality display module (51) and a user interaction control module (52); The augmented reality display module (51) is used to receive the aircraft data and environmental parameter data from the data storage module (12) and the status of each aircraft from the status feedback transmission module (42), and display them in real time on the visual AR device; The user interaction control module (52) is used to provide an interaction interface between an operator and the aircraft.

Images