A safe flight boundary analysis method for deformable long-endurance UAVs

Abstract

The present invention relates to the field of aircraft flight dynamics technology, and discloses a method for analyzing the safe flight boundaries of a deformable long-endurance unmanned aerial vehicle (UAV), including: establishing a closed-loop nonlinear flight dynamics model, combining a simulation algorithm and a flight state vector, and being able to accurately simulate the flight state of the UAV and dynamically adjust the flight path. By expanding the radial grid and the spanwise grid along the unit vector of the flight state, a high-dimensional state envelope is generated, and the safe flight boundary in the form of an implicit function is extracted using polynomial fitting, which can comprehensively and accurately describe the safe flight area of the aircraft. The present invention ensures that the UAV always remains within the safe flight area by real-time evaluation of the relationship between the flight state and the safety boundary, effectively improving the accuracy, stability and safety of flight control, especially in applications in complex environments, and can dynamically respond to various changes that may occur during the flight process, thereby improving the autonomous flight capability and safety of the aircraft.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft flight dynamics, and in particular to a method for analyzing the safe flight boundaries of a deformable long-endurance unmanned aerial vehicle. Background Art

[0002] Deformable long-endurance UAVs (UAVs) are capable of autonomously changing their shape and layout to achieve performance requirements such as a wide speed range and long flight time, and possess multi-mission adaptability, making them a key development direction in the UAV field. However, to meet these performance requirements, UAVs typically employ high-aspect-ratio wings. The nonlinear aeroelastic effects caused by wing deformation complicate the dynamic characteristics of these UAVs. Furthermore, due to the addition of multiple control parameters, such as the deformation amount, the nonlinear unsteady aerodynamic effects caused by wing deformation are significant, and environmental and model uncertainties complicate the design and analysis of nonlinear control laws. These factors all threaten the flight safety of UAVs. During the flight dynamics design phase of UAVs, in-depth analysis of the safe flight boundary is essential to evaluate the effectiveness of the control law and improve flight safety.

[0003] The existing safe flight boundaries are mostly designed for the angle of attack boundary of aerodynamic stall and the overload boundary of structural strength, which take less flight state into consideration. They are not comprehensive for deformable long-endurance UAVs and cannot evaluate the analytical problems of nonlinear dynamics and nonlinear control caused by wing deformation. Summary of the Invention

[0004] In view of this, the present invention proposes a safe flight boundary analysis method for deformable long-endurance UAVs. Based on the method of constructing an adaptive state grid for direct numerical simulation, the stability domain of the closed-loop nonlinear flight dynamics system is calculated, and the safe flight boundary is extracted, which solves the problem of difficult nonlinear dynamics and nonlinear control analysis caused by wing deformation.

[0005] The present invention proposes a method for analyzing the safe flight boundaries of a deformable long-endurance UAV, comprising:

[0006] Acquiring flight parameters and control inputs of the UAV, establishing a closed-loop nonlinear flight dynamics model based on the flight parameters and control inputs of the UAV, and acquiring a simulated flight state vector of the UAV based on the closed-loop nonlinear flight dynamics model;

[0007] Determining the flight state of the UAV based on a relationship between a simulation algorithm and the flight state vector;

[0008] Based on the initial equilibrium point, a radial grid is expanded along the unit vector direction of the flight state of the UAV, and the step size is dynamically adjusted according to the stability;

[0009] Expanding a spanwise grid based on the stable boundary points of the radial grid, and generating a high-dimensional state envelope of the flight state of the UAV according to the radial grid and the spanwise grid;

[0010] According to the high-latitude state envelope, based on polynomial fitting, a safe flight boundary of the UAV in implicit function form is extracted, and according to the safe flight boundary, it is determined whether the flight state of the UAV is a safe flight state.

[0011] Furthermore, obtaining the flight parameters and control inputs of the drone includes:

[0012] The UAV flight parameters specifically include flight position, flight speed, flight angle and UAV angular rate;

[0013] The control input is specifically the control surface angle and the throttle amount.

[0014] Furthermore, establishing a closed-loop nonlinear flight dynamics model based on the UAV flight parameters and control inputs, and obtaining a simulated flight state vector of the UAV based on the closed-loop nonlinear flight dynamics model includes:

[0015] ;

[0016] in, is the flight state vector of the UAV, u(t) is the control input, p(t) is the deformation parameter vector, x(t) is the flight parameter, and f(x(t),u(t),t) is the nonlinear dynamic function.

[0017] Furthermore, based on the relationship between the simulation algorithm and the flight state vector, determining the flight state of the UAV includes:

[0018] Applying a simulation algorithm to simulate the flight dynamics of the UAV based on the flight state vector, wherein the flight state vector includes the flight position, flight speed, flight angle, and angular rate of the UAV;

[0019] Simulating and updating the flight state of the UAV in real time based on the simulation results of the flight state vector, wherein the flight state includes the stability and trajectory of the current aircraft and the response of the aircraft to control inputs;

[0020] According to the stability judgment criterion of the flight state, it is judged whether the current flight state is a stable state, wherein if the flight state meets the stability criterion, it is judged to be a safe flight state.

[0021] Furthermore, the stability determination criteria include:

[0022] stable state, critically stable state, and unstable state;

[0023] The criterion for determining the stable state is:

[0024] ∣x(tf)−x target ∣≤

[0025] Among them, x(tf) is the flight state of the UAV, x target is the target balance point, is the allowable error threshold;

[0026] The critical stable state determination criterion is:

[0027] T≤Tmax,x(t)∈D

[0028] Among them, T is the time point when the UAV flight enters the stable oscillation mode, Tmax is the maximum allowable oscillation time, and D is the domain of the state space;

[0029] The unstable state determination criteria are:

[0030] x(t)∈ / D.

[0031] Furthermore, based on the initial equilibrium point, the radial grid is expanded along the unit vector direction of the flight state of the UAV, and the step size is dynamically adjusted according to the stability, including:

[0032] Based on the initial equilibrium point, the flight state vector of the UAV is determined, the radial grid of the UAV is expanded along the unit vector direction of the flight state vector, and the step size of the radial grid is dynamically adjusted according to the stability analysis result:

[0033] B = b + α·vi;

[0034] Wherein, B is the grid point after the expansion of the UAV, b is the current grid point, vi is the unit vector in the i-th direction, and α is the step size.

[0035] Furthermore, dynamically adjusting the step size of the radial grid according to the stability analysis result includes:

[0036] When the UAV is in the stable state, a growth coefficient is determined according to a vector difference between the flight state vector of the UAV and a preset flight state vector, and the step size is increased according to the growth coefficient, wherein the growth coefficient ranges from 1.05 to 1.2;

[0037] When the UAV is in the critical stable state, a reduction coefficient is determined according to a vector difference between the UAV's flight state vector and a preset flight state vector, and the step size is reduced according to the reduction coefficient, wherein the reduction coefficient range is 0.1-0.5;

[0038] When the UAV is in the unstable state, the step length is not adjusted.

[0039] Furthermore, based on the stable boundary points of the radial grid, the spanwise grid is expanded, including:

[0040] G = g + β·vj;

[0041] Where G is the spanwise grid point after expansion, g is the stable boundary point of the radial grid, vj is the unit vector in the j-th spanwise direction, and β is the spanwise step size.

[0042] Furthermore, extracting the safe flight boundary of the UAV in the form of an implicit function based on the high-latitude state envelope and polynomial fitting includes:

[0043] The safe flight boundary of the UAV in the form of an implicit function is extracted based on the minimum multiplication fitting polynomial:

[0044] ;

[0045] Among them, F(x) is the implicit function of the safe flight boundary, ai is the coefficient of the i-th term, mi is the order of the i-th term, and xi is the i-th state variable.

[0046] Furthermore, determining whether the flight state of the UAV is a safe flight state based on the safe flight boundary includes:

[0047] Obtain a flight state vector of the UAV, and determine whether the flight state of the UAV is a safe flight state based on a relationship between the flight state vector and the safe flight boundary:

[0048] When the flight state of the UAV is within the safe flight boundary and is equal to 0, the flight state of the UAV is determined to be a safe flight state;

[0049] When the flight state of the UAV is not within the safe flight boundary and is greater than or less than 0, it is determined that the flight state of the UAV is not a safe flight state.

[0050] Compared to existing technologies, the present invention offers the advantage of accurately modeling the flight state of a deformable, long-endurance UAV by establishing a closed-loop nonlinear flight dynamics model. By acquiring the UAV's flight parameters and control inputs and building a closed-loop nonlinear flight dynamics model based on this information, the UAV's flight process can be simulated and its flight state vector obtained. This process provides reliable data support for subsequent flight state analysis. Next, the UAV's actual flight state is determined based on the relationship between the simulation algorithm and the flight state vector. This process ensures that the UAV's flight state closely matches actual flight conditions, thus providing a better basis for analyzing safe flight boundaries. This analysis not only relies on a static model but also dynamically reflects changes during flight, ensuring real-time assessment of the aircraft's flight state at different flight stages. Furthermore, by expanding the radial grid along the unit vector of the flight state based on the initial equilibrium point and dynamically adjusting the step size based on the stability of the flight state, a highly accurate flight state envelope is constructed. This process ensures comprehensive coverage of the flight state space, while dynamically adjusting the step size based on stability criteria, effectively balancing computational accuracy and efficiency. Finally, by extracting a safe flight boundary in the form of an implicit function based on polynomial fitting, the method accurately describes the aircraft's safe zone. This safe flight boundary effectively distinguishes between safe and unsafe flight states and determines in real time whether the current flight state is within the safe zone, providing an important basis for flight control and ensuring that the aircraft always maintains a safe and stable flight state. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0052] Figure 1 A flowchart of a method for analyzing safe flight boundaries of a deformable long-endurance UAV provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of radial and spanwise adaptive state grid expansion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0055] like Figure 1-Figure 2 As shown, in some embodiments of the present application, this embodiment provides a method for analyzing safe flight boundaries of a deformable long-endurance UAV, including:

[0056] Step S100: Obtain the flight parameters and control input of the UAV, establish a closed-loop nonlinear flight dynamics model based on the flight parameters and control input of the UAV, and obtain the simulated flight state vector of the UAV based on the closed-loop nonlinear flight dynamics model.

[0057] Specifically, obtaining the UAV flight parameters and control inputs includes: UAV flight parameters specifically include flight position, flight speed, flight angle, and UAV angular rate. Control inputs specifically include control surface deflection angle and throttle amount.

[0058] Specifically, a closed-loop nonlinear flight dynamics model is established based on the flight parameters and control input of the UAV, and the simulated flight state vector of the UAV is obtained based on the closed-loop nonlinear flight dynamics model, including:

[0059] .

[0060] in, is the flight state vector of the UAV, u(t) is the control input, p(t) is the deformation parameter vector, x(t) is the flight parameter, and f(x(t),u(t),t) is the nonlinear dynamic function.

[0061] As can be seen, by acquiring the drone's flight parameters and control inputs, flight parameters include flight position, flight speed, flight angle, and angular rate, which are key factors in describing the drone's state. Control inputs include control surface deflection and throttle, which directly affect the drone's trajectory and flight attitude. By monitoring these parameters and inputs in real time, the system can fully understand the drone's current flight state and control inputs. Secondly, based on the flight parameters and control inputs, a closed-loop nonlinear flight dynamics model can be constructed. A flight dynamics model simulates the flight process by describing the drone's motion patterns, mechanical responses, and their relationship to control inputs. Closed-loop control means that the model not only considers the drone's current state but also provides feedback on the impact of control inputs, ensuring that the flight path and attitude can be dynamically adjusted based on real-time data, thereby improving flight stability and accuracy. Furthermore, based on the closed-loop nonlinear flight dynamics model, the simulated flight state vector (x(t)) of the drone can be further calculated and obtained. This vector contains information about the drone's current flight state, including flight parameters, control inputs, and their impact on the flight path and attitude. By combining flight parameters with control inputs, the UAV's future state changes can be more accurately predicted, providing a basis for subsequent flight control and planning. Finally, the nonlinear dynamic function f(x(t), u(t), t) in the model represents the complex relationship between flight state and control input. This function is nonlinear because the various mechanical factors in the flight system (such as aerodynamic effects and gravity) often have nonlinear characteristics. The deformation parameter vector p(t) further reflects the impact of changes in the external environment or the system itself (such as climate change and load changes) on the flight state. Using these parameters, the model can simulate the UAV's real-world flight state in complex environments, thereby achieving precise flight control and prediction.

[0062] It can be understood that establishing a closed-loop nonlinear flight dynamics model enables the accurate description and simulation of the UAV's flight state, providing a precise mathematical foundation for flight control and safety analysis. Obtaining the UAV's flight parameters and control inputs and incorporating them into the flight dynamics model eliminates the reliance on idealized assumptions when calculating flight state, relying instead on actual flight data. This allows the UAV to maintain stable flight in complex environments, thereby improving the accuracy and reliability of flight control. Secondly, by comprehensively considering flight parameters and control inputs, the integrity of the aircraft's state is ensured. Parameters such as flight position, speed, angle, and angular rate constitute the fundamental state information of the UAV, while control surface deflection and throttle as input variables directly influence the aircraft's attitude and trajectory. Combining these data for dynamic modeling enables the flight dynamics model to realistically reflect the UAV's flight behavior under varying control inputs, thereby enhancing the adaptability and flexibility of the flight control system's response to the UAV. Furthermore, the adoption of a closed-loop control approach places greater emphasis on the real-time interaction between flight state and control inputs than traditional open-loop dynamics modeling. Through closed-loop feedback, flight state calculations rely not only on current inputs but also on adaptive adjustments based on historical states, thereby improving system stability and interference resistance. This is particularly important for long-flight drones, enabling them to maintain stable flight under complex conditions such as wind field disturbances and load variations, thereby enhancing the drone's environmental adaptability. Ultimately, by obtaining the simulated flight state vector, the drone's flight state can be quantified and predicted, providing a reliable basis for flight control, path optimization, and safety boundary analysis. Calculation based on the nonlinear dynamic function f(x(t), u(t), t) accurately describes the drone's state changes under various flight conditions, enabling the flight control algorithm to be optimized and adjusted in different environments and mission requirements, thereby improving the drone's flight performance and mission execution capabilities.

[0063] Step S200: Determine the flight state of the UAV based on the relationship between the simulation algorithm and the flight state vector.

[0064] Specifically, determining the drone's flight state based on the relationship between the simulation algorithm and the flight state vector involves: applying a simulation algorithm to simulate the drone's flight dynamics based on the flight state vector, where the flight state vector includes the drone's flight position, flight speed, flight angle, and angular rate. Based on the simulation results of the flight state vector, the drone's flight state is simulated and updated in real time, where the flight state includes the current stability and trajectory of the aircraft, as well as the aircraft's response to control inputs. Based on the flight state stability criteria, a determination is made as to whether the current flight state is stable. If the flight state meets the stability criteria, it is determined to be a safe flight state.

[0065] Specifically, the stability criteria include:

[0066] stable state, critically stable state and unstable state.

[0067] The criteria for determining the stable state are:

[0068] ∣x(tf)−x target ∣≤

[0069] Among them, x(tf) is the flight state of the UAV, x target is the target balance point, is the allowed error threshold.

[0070] The criterion for determining the critical stable state is:

[0071] T≤Tmax,x(t)∈D

[0072] Among them, T is the time point when the UAV flight enters the stable oscillation mode, Tmax is the maximum allowable oscillation time, and D is the domain of the state space.

[0073] The criteria for determining an unstable state are:

[0074] x(t)∈ / D.

[0075] It can be understood that the application of simulation algorithms in flight state analysis uses the flight state vector (including parameters such as flight position, velocity, angle, and angular rate) as input, and uses simulation algorithms to simulate flight dynamics, enabling real-time calculation of the aircraft's state at different time points. This approach ensures that the flight control system tracks the flight state in real time, thereby more accurately reflecting the aircraft's motion and behavior, especially during flight in complex environments. Next, real-time flight state updates are achieved through simulation algorithms. At each time step, the aircraft's state is continuously updated based on the simulation results of the current flight state vector. This includes information on the aircraft's stability, trajectory, and response to control inputs. In this way, the flight state is not limited to static model calculations, but is continuously adjusted and fed back based on actual flight conditions, ensuring that the aircraft's dynamic behavior in a real-time environment can be effectively simulated and controlled. To ensure flight safety, the next core technology is the stability determination criterion. The stability of the flight state determines whether the aircraft is in a safe state. This criterion categorizes flight states into three types: stable, critically stable, and unstable. By determining these three states, the aircraft's current flight safety can be clearly assessed, allowing timely implementation of appropriate flight recovery or control measures. This process provides a systematic stability assessment for the aircraft during flight, helping to avoid entering unsafe states. The stability determination criteria specifically encompass three types of states: stable, critically stable, and unstable. The stable state determination criterion uses the difference between the target equilibrium point and the allowable error threshold to determine whether the aircraft is stable near the target position. The critically stable state is determined based on the oscillation time and the domain of the state space. If the flight state fails to stabilize within a predetermined time, the aircraft is considered to have entered a critical state. Finally, if the flight state exceeds the defined safe region (i.e., the state is not within the definable state space), it is considered to be unstable. These criteria enable precise assessment of the aircraft's flight state and the implementation of appropriate control measures based on the respective states, ensuring flight safety and stability.

[0076] Step S300: Based on the initial equilibrium point, the radial grid is expanded along the unit vector direction of the UAV's flight state, and the step size is dynamically adjusted according to the stability.

[0077] Specifically, based on the initial balance point, the radial grid is expanded along the unit vector direction of the UAV's flight state, and the step size is dynamically adjusted according to the stability, including: based on the initial balance point, determining the UAV's flight state vector, expanding the UAV's radial grid along the unit vector direction of the flight state vector, and dynamically adjusting the step size of the radial grid according to the stability analysis result:

[0078] B=b+α·vi.

[0079] Among them, B is the grid point after the drone is expanded, b is the current grid point, vi is the unit vector in the i-th direction, and α is the step size.

[0080] Specifically, based on the stability analysis results, the radial grid step size is dynamically adjusted, including:

[0081] When the UAV is in a stable state, the growth coefficient is determined according to the vector difference between the UAV's flight state vector and the preset flight state vector, and the step size is increased according to the growth coefficient, wherein the growth coefficient range is 1.05-1.2.

[0082] When the UAV is in a critical stable state, the reduction coefficient is determined according to the vector difference between the UAV's flight state vector and the preset flight state vector, and the step size is reduced according to the reduction coefficient, wherein the reduction coefficient range is 0.1-0.5.

[0083] When the drone is in an unstable state, the step size is not adjusted.

[0084] It can be understood that determining the flight state vector based on the initial equilibrium point provides a reference point for the UAV's flight state, upon which the flight state grid can be constructed. Expanding the grid along the unit vector of the flight state vector effectively covers the UAV's flight state space, ensuring that all possible flight states are considered during the calculation process. Secondly, during the radial grid expansion process, dynamic adjustment of the step size is a crucial step in ensuring computational accuracy and efficiency. Specifically, step size adjustment depends on the stability of the UAV's current flight state. When the UAV is in a stable state, the difference between the flight state vector and the preset flight state vector serves as the basis for the growth factor. Based on this difference, the growth factor (typically between 1.05 and 1.2) is used to increase the step size, thereby accelerating the calculation process. This approach improves simulation efficiency while ensuring computational accuracy. When the UAV enters a critically stable state, the step size needs to be reduced to maintain system stability. In this case, the reduction factor (typically between 0.1 and 0.5) is determined based on the difference between the flight state vector and the preset flight state vector. Based on this coefficient, the step size is reduced accordingly, thereby increasing calculation accuracy and avoiding error accumulation or instability caused by excessive step sizes. However, when the aircraft is unstable, the calculation process stops adjusting the step size because the aircraft's state has exceeded the stable range. This is to prevent deviations in the numerical results under unstable conditions. The purpose of this is to ensure the stability of the calculation process and avoid further error amplification or unpredictable dynamic behavior. In this way, step size adjustment not only ensures calculation efficiency, but also maintains the accuracy and stability of the results under different flight conditions.

[0085] Step S400: Expand the spanwise grid based on the stable boundary points of the radial grid, and generate a high-dimensional state envelope of the flight state of the UAV based on the radial grid and the spanwise grid.

[0086] Specifically, based on the stable boundary points of the radial grid, the spanwise grid is expanded to include:

[0087] G=g+β·vj.

[0088] Where G is the spanwise grid point after expansion, g is the stable boundary point of the radial grid, vj is the unit vector in the j-th spanwise direction, and β is the spanwise step size.

[0089] As can be understood, the radial grid's stable boundary points provide stable boundaries for the vehicle's state space, defining the stable range of the vehicle's states. Further extending the grid along the spanwise direction allows for a wider coverage of the flight state space, ensuring that all possible flight states are considered and enabling a comprehensive analysis of the vehicle's safe flight region. During the spanwise grid expansion process, the calculation method utilizes the spanwise step size and spanwise unit vectors. Specifically, the spanwise grid points G are calculated by combining the radial grid's stable boundary points g and the spanwise unit vector vj using the formula G = g + β⋅vj. This means that the spanwise grid expansion is performed along a specific direction, better capturing flight state variations and providing a comprehensive analysis of vehicle behavior. Finally, spanwise grid expansion not only refines the flight state boundaries but also provides a higher-dimensional state space for subsequent safe flight boundary analysis. This approach allows for a precise delineation of the vehicle's safe flight region, ensuring that the vehicle remains in a controllable and safe flight state. The technical principles and implementation strategies of this process enhance the stability analysis capabilities of vehicles in complex environments, particularly for long-duration missions.

[0090] Step S500: Extract the safe flight boundary of the UAV in the form of an implicit function based on the high-latitude state envelope and polynomial fitting, and determine whether the flight state of the UAV is a safe flight state based on the safe flight boundary.

[0091] Specifically, according to the high-latitude state envelope and based on polynomial fitting, the safe flight boundary of the UAV in implicit function form is extracted, including:

[0092] Extract the safe flight boundary of the UAV in implicit function form based on the minimum multiplication fitting polynomial:

[0093] .

[0094] Among them, F(x) is the implicit function of the safe flight boundary, ai is the coefficient of the i-th term, mi is the order of the i-th term, and xi is the i-th state variable.

[0095] Specifically, determining whether the UAV's flight state is in a safe flight state based on the safe flight boundary includes: obtaining the UAV's flight state vector, and judging whether the UAV's flight state is in a safe flight state based on the relationship between the flight state vector and the safe flight boundary: when the UAV's flight state is within the safe flight boundary and is equal to 0, the UAV's flight state is determined to be in a safe flight state. When the UAV's flight state is not within the safe flight boundary and is greater than or less than 0, the UAV's flight state is determined to be not in a safe flight state.

[0096] It can be understood that by fitting a polynomial using the least squares method, an implicit function describing the safe flight boundary can be extracted from the aircraft's state space. This implicit function F(x) describes whether the flight state is within the safe range. The polynomial coefficients ai and order mi represent the weight and influence of each state variable, respectively, while the state variables xi represent the key state parameters of the aircraft (such as position, velocity, and angle). In this process, the safe flight boundary, expressed as an implicit function through the polynomial, accurately describes the safe region within the aircraft's state space. This implicit function effectively distinguishes between aircraft states within and outside the safe boundary, forming the basis for determining whether the flight state is safe. Fitting the polynomial using the least squares method ensures high accuracy of the safe flight boundary model, thereby ensuring more reliable flight state determination. Next, based on the relationship between the safe flight boundary and the flight state vector, the safety of the UAV's flight state can be determined. Specifically, the current flight state vector is obtained and substituted into the implicit function of the safe flight boundary for calculation. If the implicit function evaluates to 0 and the flight state is within the safe boundary, the aircraft is determined to be in a safe flight state. If the implicit function's value is greater than or less than 0 and the flight state exceeds the safety boundary, the aircraft is deemed to be in an unsafe flight state. Finally, by comparing the flight state in high-dimensional state space with the safety boundary in real time, the aircraft is effectively ensured to remain in a safe state throughout the flight. Using the implicit function's safety boundary, the aircraft's state can be dynamically monitored during flight, allowing for timely identification and response to unsafe flight states, thereby improving flight safety.

[0097] In the above-described embodiment, a closed-loop nonlinear flight dynamics model is established to accurately model the flight state of a deformable, long-endurance UAV. By obtaining the UAV's flight parameters and control inputs and building a closed-loop nonlinear flight dynamics model based on this information, the UAV's flight process can be simulated and its flight state vector obtained. This process provides reliable data support for subsequent flight state analysis. Next, the UAV's actual flight state is determined based on the relationship between the simulation algorithm and the flight state vector. This process ensures that the UAV's flight state closely matches actual flight conditions, thus providing a better basis for analyzing safe flight boundaries. This analysis not only relies on a static model but also dynamically reflects changes during flight, ensuring real-time assessment of the aircraft's flight state during different flight phases. Furthermore, by expanding the radial grid along the unit vector of the flight state based on the initial equilibrium point and dynamically adjusting the step size based on the stability of the flight state, a highly accurate flight state envelope is constructed. This process ensures comprehensive coverage of the flight state space. Dynamically adjusting the step size based on the stability criterion effectively balances computational accuracy and efficiency. Finally, by extracting the safe flight boundary in the form of an implicit function based on polynomial fitting, the method accurately describes the aircraft's safety zone. The safe flight boundary can effectively distinguish between safe and unsafe flight states, and determine in real time whether the current flight state is within the safe area, thereby providing an important basis for flight control and ensuring that the aircraft is always in a safe and stable flight state.

[0098] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the safe flight boundaries of a deformable long-endurance UAV, characterized by: include: Acquiring flight parameters and control inputs of the UAV, establishing a closed-loop nonlinear flight dynamics model based on the flight parameters and control inputs of the UAV, and acquiring a simulated flight state vector of the UAV based on the closed-loop nonlinear flight dynamics model; Determining the flight state of the UAV based on a relationship between a simulation algorithm and the flight state vector; Based on the initial equilibrium point, a radial grid is expanded along the unit vector direction of the flight state of the UAV, and the step size is dynamically adjusted according to the stability; Expanding a spanwise grid based on the stable boundary points of the radial grid, and generating a high-dimensional state envelope of the flight state of the UAV according to the radial grid and the spanwise grid; Determining the flight state of the UAV based on the relationship between the simulation algorithm and the flight state vector includes: Applying a simulation algorithm to simulate the flight dynamics of the UAV based on the flight state vector, wherein the flight state vector includes the flight position, flight speed, flight angle, and angular rate of the UAV; Simulating and updating the flight state of the UAV in real time based on the simulation results of the flight state vector, wherein the flight state includes the stability and trajectory of the current aircraft and the response of the aircraft to control inputs; determining whether the current flight state is a stable state according to a stability determination criterion of the flight state, wherein if the flight state satisfies the stability criterion, the flight state is determined to be a safe flight state; The stability judgment criteria include: stable state, critically stable state, and unstable state; The criterion for determining the stable state is: ∣x(tf)-x target ∣≤∈ Among them, x(tf) is the flight state of the UAV, x target is the target equilibrium point, ∈ is the allowed error threshold; The critical stable state determination criterion is: T≤Tmax,x(t)∈D Among them, T is the time point when the UAV flight enters the stable oscillation mode, Tmax is the maximum allowable oscillation time, and D is the domain of the state space; The unstable state determination criteria are: Extracting a safe flight boundary of the UAV in an implicit function form based on polynomial fitting according to the high-latitude state envelope, and determining whether the flight state of the UAV is a safe flight state according to the safe flight boundary; Among them, when the UAV is in the stable state, the growth coefficient is determined according to the vector difference between the flight state vector of the UAV and the preset flight state vector, and the step size is increased according to the growth coefficient, wherein the growth coefficient range is 1.05-1.2; when the UAV is in the critical stable state, the reduction coefficient is determined according to the vector difference between the flight state vector of the UAV and the preset flight state vector, and the step size is reduced according to the reduction coefficient, wherein the reduction coefficient range is 0.1-0.5; when the UAV is in the unstable state, the step size is not adjusted.

2. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 1, characterized in that: When obtaining drone flight parameters and control input, including: The UAV flight parameters specifically include flight position, flight speed, flight angle and UAV angular rate; The control input is specifically the control surface angle and the throttle amount.

3. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 2, wherein: Establishing a closed-loop nonlinear flight dynamics model based on the UAV flight parameters and control inputs, and obtaining a simulated flight state vector of the UAV based on the closed-loop nonlinear flight dynamics model, including: in, is the flight state vector of the UAV, u(t) is the control input, p(t) is the deformation parameter vector, x(t) is the flight parameter, and f(x(t),u(t),p(t)) is the nonlinear dynamic function.

4. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 1, wherein: Based on the initial equilibrium point, the radial grid is expanded along the unit vector direction of the flight state of the UAV, and the step size is dynamically adjusted according to the stability, including: Based on the initial equilibrium point, the flight state vector of the UAV is determined, the radial grid of the UAV is expanded along the unit vector direction of the flight state vector, and the step size of the radial grid is dynamically adjusted according to the stability analysis result: B=b+α·vi; Wherein, B is the grid point after the expansion of the UAV, b is the current grid point, vi is the unit vector in the i-th direction, and α is the step size.

5. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 4, characterized in that: Based on the stable boundary points of the radial grid, the spanwise grid is expanded, including: G=g+β·vj; Where G is the spanwise grid point after expansion, g is the stable boundary point of the radial grid, vj is the unit vector in the j-th spanwise direction, and β is the spanwise step size.

6. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 1, characterized in that: Extracting the safe flight boundary of the UAV in an implicit function form based on the high-latitude state envelope and polynomial fitting includes: The safe flight boundary of the UAV in the form of an implicit function is extracted based on the minimum multiplication fitting polynomial: Among them, F(x) is the implicit function of the safe flight boundary, a i is the coefficient of the i-th term, m i is the order of the i-th term, x i is the i-th state variable.

7. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 6, wherein: Determining whether the flight state of the UAV is a safe flight state based on the safe flight boundary includes: Obtain a flight state vector of the UAV, and determine whether the flight state of the UAV is a safe flight state based on a relationship between the flight state vector and the safe flight boundary: When the flight state of the UAV is within the safe flight boundary and is equal to 0, the flight state of the UAV is determined to be a safe flight state; When the flight state of the UAV is not within the safe flight boundary and is greater than or less than 0, it is determined that the flight state of the UAV is not a safe flight state.

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