A low-altitude vehicle attitude control system based on intelligent information data analysis

Abstract

The present invention relates to the technical field of attitude control of low-altitude aircraft. Specifically, it relates to an attitude control system for low-altitude aircraft based on intelligent information data analysis. It includes: a data acquisition and storage unit that acquires aircraft data and environmental parameter data; an intelligent information analysis unit that uses nonlinear dynamics technology and singular perturbation theory technology to analyze the real-time attitude of the aircraft, and uses singular perturbation analysis technology to determine whether the aircraft is in a safe state; a single aircraft control unit that uses a linear quadratic regulator combined with an advanced attitude control algorithm to generate an optimal control strategy; a multi-aircraft control unit that adopts a distributed control strategy to control the coordinated flight between aircraft; and a control and display terminal unit that provides an integrated human-machine interaction interface. The attitude control system for low-altitude aircraft based on intelligent information data analysis combines nonlinear dynamics and singular perturbation theory and adopts a distributed control architecture to achieve coordinated flight control of single and multiple aircraft.

Description

Technical Field

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

[0002] The attitude control system for low-altitude aircraft based on intelligent information data analysis aims to achieve real-time cooperative flight and high-precision attitude control of multiple aircraft. By integrating non-linear dynamics modeling, singular perturbation theory, and distributed control strategies, it controls the attitude and flight path of each aircraft to achieve optimal control of the overall stability, efficiency, and flight safety of the system.

[0003] Existing attitude control systems for low-altitude aircraft usually have difficulty in achieving high-precision real-time attitude estimation and path optimization in complex dynamic environments. Moreover, due to environmental disturbances, sensor noise, communication delays between multiple aircraft, and the risk of single-point failures, problems such as inaccurate attitude estimation, path planning conflicts, slow system response, and low overall flight efficiency will occur. Therefore, an attitude control system for low-altitude aircraft based on intelligent information data analysis is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide an attitude control system for low-altitude aircraft based on intelligent information data analysis to solve the problems of inaccurate attitude estimation, path planning conflicts, slow system response, and low overall flight efficiency caused by external environmental disturbances, sensor noise, communication delays between multiple aircraft, and the risk of single-point failures as mentioned in the above background art.

[0005] To achieve the above object, the present invention provides an attitude control system for low-altitude aircraft based on intelligent information data analysis, including:

[0006] A data acquisition and storage unit, which uses sensors to collect aircraft data and environmental parameter data in real time, and stores the aircraft data and environmental parameter data in a database using an embedded storage system;

[0007] It further includes an intelligent information analysis unit, which analyzes the real-time attitude of the aircraft based on the aircraft data and environmental parameter data using non-linear dynamics technology and singular perturbation theory technology, and uses 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;

[0008] It further 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 its attitude in real time based on the optimal control strategy;

[0009] It further includes a multi - vehicle control unit, which adopts a distributed control strategy to control the coordinated flight among the vehicles and feeds back the states of the vehicles to the intelligent information analysis unit and the control and display terminal unit;

[0010] It further includes a control and display terminal unit, which is used to provide an integrated human - machine interaction interface and display the vehicle data and environmental parameter data in real time through AR devices.

[0011] As a further improvement of this technical solution, the data acquisition and storage unit includes a data acquisition module and a data storage module;

[0012] Among them, the data acquisition module uses sensors to collect vehicle data and environmental parameter data in real time;

[0013] The vehicle data includes: roll angle , pitch angle , yaw angle , longitude , latitude , altitude , eastward speed , northward speed , vertical speed , X - axis acceleration , Y - axis acceleration , Z - axis acceleration , roll angular velocity , pitch angular velocity , yaw angular velocity ;

[0014] The environmental parameter data includes: horizontal wind speed , vertical wind speed , wind direction angle ;

[0015] The data storage module uses an embedded storage system to store the obtained vehicle data and environmental parameter data in the database in real time and transmits the vehicle data and environmental parameter data to the control and display terminal unit.

[0016] As a further improvement of this technical solution, the intelligent information analysis unit includes a dynamic attitude prediction module and a safety assessment and optimization module;

[0017] Among them, the dynamic attitude prediction module analyzes the real - time attitude of the vehicle based on the vehicle data and environmental parameter data, using non - linear dynamics technology and singular perturbation theory technology;

[0018] The safety assessment and optimization module determines whether the real-time attitude of the vehicle is in a safe state using singular perturbation analysis technology based on the real-time attitude of the vehicle from the dynamic attitude prediction module, and calculates the target attitude of the vehicle through an optimization algorithm.

[0019] As a further improvement of this technical solution, the dynamic attitude prediction module analyzes the real-time attitude of the vehicle using nonlinear dynamics technology and singular perturbation theory technology based on vehicle data and environmental parameter data. The specific method steps are as follows:

[0020] S2.1.1. Based on vehicle data and environmental parameter data, construct a nonlinear dynamics model using nonlinear dynamics technology:

[0021] Nonlinear dynamics model:

[0022] ;

[0023] Among them, is time; is the mass of a single vehicle; is the acceleration due to gravity; is the eastward thrust; is the northward thrust; is the vertical thrust; is the eastward drag; is the northward drag; is the vertical drag force;

[0024] S2.1.2. Using singular perturbation theory, transform the nonlinear dynamics model of the vehicle into a fast dynamic equation and a slow dynamic equation:

[0025] Fast dynamic equation:

[0026] ;

[0027] Slow dynamic equation:

[0028] ;

[0029] Among them, is the fast variable; is the slow variable; is the time scale of the fast dynamics relative to the slow dynamics; is the equation of the fast variable based on the time derivative; is the equation of the slow variable based on the time derivative;

[0030] S2.1.3. Based on the fast dynamic equation and the slow dynamic equation, use the extended Kalman filter to calculate and estimate the real-time attitude of the vehicle:

[0031] State prediction equation:

[0032] ;

[0033] Wherein, is the th time step; is the fast variable state predicted at time step ; is the time step size; is the equation of the fast variable based on the time derivative;

[0034] State update equation:

[0035] ;

[0036] Wherein, is the fast variable state updated at time step ; is the Kalman gain matrix; is the observation vector at time step ; is the observation matrix;

[0037] Real-time attitude estimation equation:

[0038] ;

[0039] Wherein, is the real-time attitude of the vehicle.

[0040] As a further improvement of the technical solution, the safety assessment and optimization module uses the singular perturbation analysis technique to determine whether the real-time attitude of the vehicle is in a safe state based on the real-time attitude of the vehicle of the dynamic attitude prediction module, and calculates the target attitude of the vehicle through an optimization algorithm. The specific method steps are as follows:

[0041] S2.2.1. Construct a safety assessment index for the vehicle based on the roll angle , pitch angle and yaw angle at time :

[0042] Safety assessment index of the vehicle :

[0043] ;

[0044] Wherein, is the roll angle at time ; is the pitch angle at time ; is the time of the yaw angle; is the maximum safety threshold of the roll angle; is the maximum safety threshold of the pitch angle; is the maximum safety threshold of the yaw angle;

[0045] S2.2.2. Based on the real-time attitude of the vehicle and the fast dynamic equation, use the singular perturbation theory to construct a safety state equation set to judge whether the current attitude is in a safe state:

[0046] Safety state equation set:

[0047] ;

[0048] wherein, is the time derivative of the vehicle safety evaluation index; is the system matrix; is the input matrix; is the external disturbance input; is the vehicle basic constant matrix; is a matrix that depends on the real-time attitude of the vehicle ;

[0049] Solve the safety state equation set to obtain the vehicle safety evaluation index ;

[0050] If , then the current attitude of the vehicle is in a safe state;

[0051] On the contrary, if the current attitude of the vehicle is not in a safe state, execute S2.2.3;

[0052] S2.2.3. Based on the real-time attitude of the vehicle and the external disturbance input , construct an optimization objective function:

[0053] Construct the vehicle state vector :

[0054] ;

[0055] Construct the optimization objective function :

[0056] ;

[0057] wherein, is the eastward speed at time; is the northward speed at time; is the vertical velocity of time; is the X-axis acceleration of time; is the Y-axis acceleration of time; is the Z-axis acceleration of time; is the upper limit of the optimized time interval; is the transpose operation;

[0058] S2.2.4. Minimize the optimization objective function to obtain the target attitude of the vehicle:

[0059] ;

[0060] wherein, is the target attitude of the vehicle.

[0061] As a further improvement of the present technical solution, the single vehicle control unit generates an optimal control strategy based on the target attitude of the vehicle by using a linear quadratic regulator in combination with an advanced attitude control algorithm, and the vehicle adjusts the attitude of the vehicle in real time based on the optimal control strategy. The specific method steps are as follows:

[0062] S3.1. According to the vehicle state vector and the target attitude of the vehicle , construct the vehicle control objective function and the constraint condition equations;

[0063] S3.2. Based on the vehicle control objective function and the constraint condition equations, use a linear quadratic regulator in combination with an advanced attitude control algorithm to minimize the vehicle control objective function and solve the optimal control strategy;

[0064] S3.3. Convert the optimal control strategy into a control command and send it to the vehicle to adjust the attitude.

[0065] As a further improvement of the present technical solution, in the S3.1, according to the vehicle state vector and the target attitude of the vehicle , construct the vehicle control objective function and the constraint condition equations. The specific method is as follows:

[0066] The vehicle control objective function :

[0067] ;

[0068] wherein, is the vehicle control objective function;

[0069] The constraint condition equations:

[0070] ;

[0071] wherein, is the minimum limit of the external disturbance input; is the maximum limit of the external disturbance input; is the minimum limit of the vehicle state vector; is the maximum limit of the vehicle state vector;

[0072] In the above S3.2, based on the vehicle control objective function and the constraint condition equations, using a linear quadratic regulator combined with an advanced attitude control algorithm, the vehicle control objective function is minimized to solve the optimal control strategy. The specific method is as follows:

[0073] Construct the Riccati equation:

[0074] ;

[0075] Solve the Riccati equation to obtain , and calculate the optimal control strategy to minimize the vehicle control objective function :

[0076] ;

[0077] wherein, is the optimal control strategy; is the solution of the Riccati equation.

[0078] As a further improvement of this technical solution, the multi-vehicle control unit includes a distributed cooperative control module and a state feedback transmission module;

[0079] wherein, the distributed cooperative control module uses a distributed control strategy to control the coordinated flight between vehicles;

[0080] The state feedback transmission module feeds back the states of each vehicle to the intelligent information analysis unit and the control display terminal unit.

[0081] As a further improvement of this technical solution, the distributed cooperative control module uses a distributed control strategy to achieve the coordinated flight between vehicles. The specific method steps are as follows:

[0082] S4.1.1. Based on the distributed control strategy, design an independent but coordinated distributed control strategy for each vehicle:

[0083] ;

[0084] wherein, denotes the th vehicle; denotes the th vehicle; is the external disturbance input of the th vehicle at time ; is the target attitude of the th vehicle at time ; is the state vector of the th vehicle at time ; is the state vector of the th vehicle at time ; is the cooperative gain matrix between the th vehicle and the th vehicle;

[0085] S4.1.2. Based on the external disturbance input of each vehicle at time , remove the influence factors of environmental parameter data to obtain the distributed control strategy of each vehicle:

[0086] ;

[0087] ;

[0088] where is the distributed control strategy of the th vehicle at time ; is the influence factor vector of environmental parameter data of the th vehicle at time ; is the environmental influence coefficient matrix of the th vehicle.

[0089] As a further improvement of the present technical solution, the control and display terminal unit includes an augmented reality display module and a user interaction control module;

[0090] wherein, the augmented reality display module is used to receive the vehicle data, environmental parameter data and the states of each vehicle from the state feedback transmission module of the data storage module, and display them on the visual AR device in real time;

[0091] The user interaction control module is used to provide an interaction interface between the operator and the vehicle.

[0092] Compared with the prior art, the beneficial effects of the present invention:

[0093] 1. In the attitude control system of a low-altitude vehicle based on intelligent information data analysis, real-time attitude analysis and safety assessment based on nonlinear dynamics and singular perturbation theory are carried out to achieve high-precision attitude prediction, dynamic safety monitoring, and timely response to potential safety risks.

[0094] 2. In the attitude control system of a low-altitude vehicle based on intelligent information data analysis, through a distributed control architecture, efficient cooperative flight control among multiple vehicles is realized to ensure the overall stability and flight efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 is the overall flow block diagram of the present invention;

[0096] The meanings of the reference numerals in the figure are as follows:

[0097] 1. Data acquisition and storage unit; 2. Intelligent information analysis unit; 3. Single vehicle control unit; 4. Multi-vehicle control unit; 5. Control display terminal unit; 11. Data acquisition module; 12. Data storage module; 21. Dynamic attitude prediction module; 22. Safety assessment and optimization module; 41. Distributed cooperative control module; 42. State feedback transmission module; 51. Augmented reality display module; 52. User interaction control module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0098] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. 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. Embodiment

[0099] Please refer to Figure 1 as shown, a low-altitude vehicle attitude control system based on intelligent information data analysis is provided, including:

[0100] A data acquisition and storage unit 1, which uses sensors to collect vehicle data and environmental parameter data in real time, and stores the vehicle data and environmental parameter data in a database using an embedded storage system;

[0101] The data acquisition and storage unit 1 includes a data acquisition module 11 and a data storage module 12;

[0102] Among them, the data acquisition module 11 uses sensors to collect vehicle data and environmental parameter data in real time;

[0103] The vehicle data includes: roll angle , pitch angle , yaw angle , longitude , latitude , altitude , eastward velocity , northward velocity , vertical velocity , X-axis acceleration , Y-axis acceleration , Z-axis acceleration , roll angular velocity , pitch angular velocity , yaw angular velocity ;

[0104] The environmental parameter data includes: horizontal wind speed , vertical wind speed , wind direction angle ;

[0105] The data storage module 12 stores the acquired vehicle data and environmental parameter data in the database in real time using the embedded storage system, and transmits the vehicle data and environmental parameter data to the control and display terminal unit 5.

[0106] It further includes an intelligent information analysis unit 2. The intelligent information analysis unit 2 analyzes the real-time attitude of the vehicle based on the vehicle data and environmental parameter data, uses the nonlinear dynamics technology and the singular perturbation theory technology to analyze the real-time attitude of the vehicle, and uses the singular perturbation analysis technology to determine whether the real-time attitude of the vehicle is in a safe state, and calculates the target attitude of the vehicle;

[0107] The nonlinear dynamics technology is a mathematical method used to describe and analyze the dynamic behavior of a vehicle in a complex environment, and can capture the nonlinear characteristics existing in the system, such as the change of attitude angle and the influence of external disturbances;

[0108] The singular perturbation theory technology is a method used to simplify and decompose a system with multi-time scale characteristics. By dividing the system into fast dynamic and slow dynamic parts, it is convenient to more efficiently process the analysis and control of complex systems;

[0109] In this embodiment, the combination of the nonlinear dynamics technology and the singular perturbation theory technology is applied to the real-time attitude analysis of the vehicle, which can accurately capture the complex dynamic behavior of the vehicle and the changes at different time scales at the same time, improve the accuracy and calculation efficiency of attitude prediction, and ensure that the vehicle is always in a safe state and can quickly adjust to the target attitude;

[0110] The intelligent information analysis unit 2 includes a dynamic attitude prediction module 21 and a safety assessment and optimization module 22;

[0111] Among them, the dynamic attitude prediction module 21 analyzes the real-time attitude of the vehicle based on the vehicle data and environmental parameter data, using nonlinear dynamics technology and singular perturbation theory technology;

[0112] The safety assessment and optimization module 22 determines whether the real-time attitude of the vehicle is in a safe state based on the real-time attitude of the vehicle from the dynamic attitude prediction module 21, using singular perturbation analysis technology, and calculates the target attitude of the vehicle through an optimization algorithm.

[0113] The dynamic attitude prediction module 21 analyzes the real-time attitude of the vehicle based on the vehicle data and environmental parameter data, using nonlinear dynamics technology and singular perturbation theory technology. The specific method steps are as follows:

[0114] S2.1.1. Based on the vehicle data and environmental parameter data, use nonlinear dynamics technology to construct a nonlinear dynamics model:

[0115] Nonlinear dynamics model:

[0116] ;

[0117] Among them, is time; is the mass of a single vehicle; is the gravitational acceleration; is the eastward thrust; is the northward thrust; is the vertical thrust; is the eastward drag; is the northward drag; is the vertical drag force;

[0118] S2.1.2. Using the singular perturbation theory, transform the nonlinear dynamics model of the vehicle into a fast dynamic equation and a slow dynamic equation:

[0119] Fast dynamic equation:

[0120] ;

[0121] Slow dynamic equation:

[0122] ;

[0123] Among them, is the fast variable; is the slow variable; is the time scale of the fast dynamics relative to the slow dynamics; is the equation of the fast variable based on the time derivative; is the equation of the slow variable based on the time derivative;

[0124] S2.1.3. Calculate the estimated real-time attitude of the vehicle using an extended Kalman filter based on the fast dynamic equation and the slow dynamic equation:

[0125] State prediction equation:

[0126] ;

[0127] where, is the th time step; is the fast variable state predicted at time step ; is the time step size; is the equation of the fast variable based on the time derivative;

[0128] State update equation:

[0129] ;

[0130] where, is the fast variable state updated at time step ; is the Kalman gain matrix; is the observation vector at time step ; is the observation matrix;

[0131] Real-time attitude estimation equation:

[0132] ;

[0133] where, is the real-time attitude of the vehicle.

[0134] The safety assessment and optimization module 22 determines whether the real-time attitude of the vehicle is in a safe state using singular perturbation analysis technology based on the real-time attitude of the vehicle from the dynamic attitude prediction module 21, and calculates the target attitude of the vehicle through an optimization algorithm. The specific method steps are as follows:

[0135] S2.2.1. Construct a safety assessment index for the vehicle based on the roll angle , pitch angle , and yaw angle at time :

[0136] Safety assessment index of the vehicle :

[0137] ;

[0138] where, is the roll angle at time ; is the pitch angle at time ; is the yaw angle at time ; is the maximum safety threshold of the roll angle; is the maximum safety threshold of the pitch angle; is the maximum safety threshold of the yaw angle;

[0139] S2.2.2. Based on the real-time attitude of the vehicle and the fast dynamic equation, use the singular perturbation theory to construct a safety state equation set to determine whether the current attitude is in a safe state:

[0140] Safety state equation set:

[0141] ;

[0142] where is the time derivative of the vehicle safety evaluation index; is the system matrix; is the input matrix; is the external disturbance input; is the vehicle basic constant matrix; is the matrix dependent on the real-time attitude of the vehicle ;

[0143] Solve the safety state equation set to obtain the vehicle safety evaluation index ;

[0144] If , then the current attitude of the vehicle is in a safe state;

[0145] Conversely, if the current attitude of the vehicle is not in a safe state, execute S2.2.3;

[0146] S2.2.3. Based on the real-time attitude of the vehicle and the external disturbance input , construct an optimization objective function:

[0147] Construct the vehicle state vector :

[0148] ;

[0149] Construct the optimization objective function :

[0150] ;

[0151] where is the eastward velocity at time is the northward velocity at time is the vertical velocity at time is the X-axis acceleration at time is the Y-axis acceleration at time is the Z-axis acceleration at time is the upper limit of the optimized time interval; is the transpose operation;

[0152] S2.2.4. Minimize the optimization objective function to obtain the target attitude of the vehicle:

[0153] ;

[0154] wherein, is the target attitude of the vehicle.

[0155] It further includes a single vehicle control unit 3. The single vehicle control unit 3 generates an optimal control strategy based on the target attitude of the vehicle, using a linear quadratic regulator combined with an advanced attitude control algorithm, and the vehicle adjusts its attitude in real time based on the optimal control strategy;

[0156] The linear quadratic regulator is an optimal 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;

[0157] In this embodiment, the linear quadratic regulator is combined with an advanced attitude control algorithm to calculate the optimal control command in real time according to the current state and target attitude of the vehicle, providing a smooth and efficient control response to ensure that the vehicle adjusts its attitude stably and accurately in a complex dynamic environment;

[0158] The single vehicle control unit 3 generates an optimal control strategy based on the target attitude of the vehicle, using a linear quadratic regulator combined with an advanced attitude control algorithm, and the vehicle adjusts its attitude in real time based on the optimal control strategy. The specific method steps are as follows:

[0159] S3.1. According to the vehicle state vector and the target attitude of the vehicle , construct the vehicle control objective function and the constraint condition equations;

[0160] S3.2. Minimize the control objective function of the vehicle and solve for the optimal control strategy by using a linear quadratic regulator combined with an advanced attitude control algorithm based on the vehicle control objective function and the system of constraint equations;

[0161] S3.3. Convert the optimal control strategy into a control command and send it to the vehicle to adjust its attitude.

[0162] In S3.1, based on the vehicle state vector and the target attitude of the vehicle , construct the vehicle control objective function and the system of constraint equations. The specific method is as follows:

[0163] The vehicle control objective function :

[0164] ;

[0165] where is the vehicle control objective function;

[0166] The system of constraint equations:

[0167] ;

[0168] where is the minimum limit of the external disturbance input; is the maximum limit of the external disturbance input; is the minimum limit of the vehicle state vector; is the maximum limit of the vehicle state vector;

[0169] In S3.2, based on the vehicle control objective function and the system of constraint equations, use a linear quadratic regulator combined with an advanced attitude control algorithm to minimize the vehicle control objective function and solve for the optimal control strategy. The specific method is as follows:

[0170] Construct the Riccati equation:

[0171] ;

[0172] Solve the Riccati equation to obtain , and calculate the optimal control strategy to minimize the vehicle control objective function :

[0173] ;

[0174] where is the optimal control strategy; is the solution of the Riccati equation.

[0175] It further includes a multi - vehicle control unit 4. The multi - vehicle control unit 4 adopts a distributed control strategy to control the coordinated flight among the vehicles, and feeds back the states of each vehicle to the intelligent information analysis unit 2 and the control and display terminal unit 5;

[0176] The distributed control strategy is a control method that distributes control tasks to each vehicle for independent execution. Through local information exchange and limited communication, it realizes the coordination and collaborative flight of the overall system and achieves the global flight goal;

[0177] In this embodiment, the distributed control strategy is applied to the coordinated flight of multi - vehicles to promote the efficient sharing and distributed processing of information, and optimize the overall flight path and task allocation;

[0178] The multi - vehicle control unit 4 includes a distributed collaborative control module 41 and a state feedback transmission module 42;

[0179] Among them, the distributed collaborative control module 41 uses the distributed control strategy to control the coordinated flight among the vehicles;

[0180] The state feedback transmission module 42 feeds back the states of each vehicle to the intelligent information analysis unit 2 and the control and display terminal unit 5.

[0181] The distributed collaborative control module 41 uses the distributed control strategy to achieve the coordinated flight among the vehicles. The specific method steps are as follows:

[0182] S4.1.1. Based on the distributed control strategy, design an independent but coordinated distributed control strategy for each vehicle:

[0183] ;

[0184] Among them, represents the th vehicle; represents the th vehicle; is the external disturbance input of the th vehicle at time ; is the target attitude of the th vehicle at time ; is the state vector of the th vehicle at time ; is the state vector of the th vehicle at time ; is the The collaborative gain matrix between the th vehicle and the

[0185] S4.1.2. Based on the external disturbance input of each vehicle at time , the influence factors of environmental parameter data are removed to obtain the distributed control strategy of each vehicle: ;

[0186] ;

[0187] ;

[0188] wherein, is the distributed control strategy of the th vehicle at time ; is the influence factor vector of environmental parameter data of the th vehicle at time ; is the environmental influence coefficient matrix of the th vehicle.

[0189] It further includes a control display terminal unit 5, and the control display terminal unit 5 is used to provide an integrated human-machine interaction interface, and display vehicle data and environmental parameter data in real time through an AR device;

[0190] The control display terminal unit 5 includes an augmented reality display module 51 and a user interaction control module 52;

[0191] wherein, the augmented reality display module 51 is used to receive vehicle data, environmental parameter data from the data storage module 12 and the states of each vehicle from the status feedback transmission module 42, and display them on the visual AR device in real time;

[0192] The user interaction control module 52 is used to provide an interaction interface between the operator and the vehicle.

[0193] 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. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

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; Based on the aircraft data and environmental parameter data, the real-time attitude of the aircraft is analyzed 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 spacecraft; 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 , 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 a control and display terminal unit (5).

3. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 1 is characterized in that: 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 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.

5. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 1 is characterized in that: 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.

6. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 5 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.

7. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 1 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).

8. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 7 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 coordination gain matrix between the vehicles; 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.

9. The low-altitude aircraft attitude control system based on intelligent information data analysis according to claim 1 is characterized by: 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.

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