Safe flight boundary analysis method for deformable long-endurance unmanned aerial vehicle

Abstract

The invention relates to the technical field of aircraft flight dynamics, and discloses a deformable long-endurance unmanned aerial vehicle safe flight boundary analysis method, which comprises the following steps of: accurately simulating the flight state of an unmanned aerial vehicle and dynamically adjusting the flight path by establishing a closed-loop nonlinear flight dynamics model and combining a simulation algorithm and a flight state vector. A radial grid and a spanwise grid are expanded along a unit vector of a flight state to generate a high-dimensional state envelope, and a safe flight boundary in an implicit function form is extracted by using polynomial fitting, so that a safe flight area of an aircraft can be comprehensively and accurately described. By evaluating the relationship between the flight state and the safety boundary in real time, it is ensured that the unmanned aerial vehicle is always kept in a safe flight area, the precision, stability and safety of flight control are effectively improved, various changes possibly occurring in the flight process can be dynamically dealt with especially when the method is applied to a complex environment, and the safety of the unmanned aerial vehicle is improved. And the autonomous flight capability and safety of the aircraft are improved.

Description

Technical Field

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

[0002] Deformable long-flight UAVs can autonomously change their shape and layout, achieve performance requirements such as wide speed range and long flight time, and have multi-task adaptability. They are an important development direction in the field of UAVs. However, in order to meet the performance requirements of long flight time, long-flight UAVs generally adopt a large aspect ratio wing design. The nonlinear aeroelastic effect caused by wing deformation makes the dynamic characteristics of the deformable long-flight UAV itself complex. At the same time, since the deformable long-flight UAV has added multiple control parameters such as deformation, the nonlinear unsteady aerodynamic effect caused by wing deformation is obvious, and there are uncertainties in the environment and model, which makes the design and analysis of nonlinear control laws difficult. The above factors threaten the flight safety of deformable long-flight UAVs. In the flight dynamics design stage of deformable long-flight UAVs, it is necessary to conduct in-depth analysis on the safe flight boundary 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 conditions into consideration and are not comprehensive for deformable long-flight UAVs. They 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 a deformable long-flight UAV. Based on the method of constructing an adaptive state grid for direct numerical simulation, the stability domain of a closed-loop nonlinear flight dynamics system is calculated, and the safe flight boundary is extracted, thus solving 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-flight UAV, comprising: Acquire flight parameters and control input of the UAV, establish a closed-loop nonlinear flight dynamics model according to the flight parameters and control input of the UAV, and acquire a simulated flight state vector of the UAV according to the closed-loop nonlinear flight dynamics model; Determining the flight state of the UAV based on the relationship between the 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; Based on the stable boundary points of the radial grid, the spanwise grid is expanded, and according to the radial grid and the spanwise grid, a high-dimensional state envelope of the flight state of the UAV is generated; 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.

[0006] Furthermore, obtaining the flight parameters and control input of the drone includes: The UAV flight parameters specifically include flight position, flight speed, flight angle and UAV angular velocity; The control input quantity specifically includes the control surface deflection angle and the throttle amount.

[0007] Furthermore, a closed-loop nonlinear flight dynamics model is established according to the flight parameters and control input of the UAV, and a simulated flight state vector of the UAV is obtained according to 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),t) is the nonlinear dynamic function.

[0008] Further, based on the relationship between the simulation algorithm and the flight state vector, determining the flight state of the drone includes: Based on the flight state vector, applying a simulation algorithm to simulate the flight dynamics of the UAV, the flight state vector including the flight position, flight speed, flight angle and angular rate of the UAV; Based on the simulation result of the flight state vector, simulating and updating the flight state of the UAV in real time, wherein the flight state includes the stability and trajectory of the current aircraft and the response of the aircraft to the control input; 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.

[0009] Furthermore, the stability determination criteria include: stable state, critically stable state, and unstable state; The stable state determination criteria are: ∣x(tf)−x target ∣≤ Among them, x(tf) is the flight state of the drone, xtarget is the target balance point, is the allowable 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 definition domain of the state space; The unstable state determination criteria are: x(t)∈ / D.

[0010] Further, based on the initial balance point, the radial grid is expanded along the unit vector direction of the flight state of the UAV, and the step length is dynamically adjusted according to the stability, including: Based on the initial balance 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; Among them, B is the grid point after the expansion of the drone, b is the current grid point, vi is the unit vector in the i-th direction, and α is the step size.

[0011] Further, dynamically adjusting the step size of the radial grid according to the stability analysis result includes: 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 length is increased according to the growth coefficient, wherein the growth coefficient ranges from 1.05 to 1.2; When the UAV is in the critical stable state, a reduction 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 reduced according to the reduction coefficient, wherein the reduction coefficient ranges from 0.1 to 0.5; When the UAV is in the unstable state, the step length is not adjusted.

[0012] Further, based on the stable boundary points of the radial grid, when the spanwise grid is expanded, it includes: G = g + β·vj; Among them, 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 jth spanwise direction, and β is the spanwise step size.

[0013] Furthermore, according to the high-latitude state envelope, based on polynomial fitting, extracting the safe flight boundary of the UAV in the form of an implicit function 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, 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.

[0014] Further, when determining whether the flight state of the UAV is a safe flight state according to the safe flight boundary, it includes: Obtaining a flight state vector of the UAV, and judging whether the flight state of the UAV is a safe flight state according to the 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.

[0015] Compared with the prior art, the beneficial effect of the present invention is that by establishing a closed-loop nonlinear flight dynamics model, the flight state of a deformable long-flight UAV is accurately modeled. By obtaining the flight parameters and control input of the UAV and establishing a closed-loop nonlinear flight dynamics model based on this information, the flight process of the UAV can be simulated and its flight state vector can be obtained. This process provides reliable data support for subsequent flight state analysis. Next, the actual flight state of the UAV is determined based on the relationship between the simulation algorithm and the flight state vector. This process enables the flight state of the UAV to be highly consistent with the actual flight situation, thereby better providing a basis for the analysis of the safe flight boundary. This analysis not only relies on a static model, but also dynamically reflects the changes in the flight process, ensuring that the aircraft can evaluate its flight state in real time at different flight stages. In addition, by expanding the radial grid along the unit vector direction of the flight state based on the initial equilibrium point, and dynamically adjusting the step size according to the stability of the flight state, a high-precision flight state envelope is constructed. This process ensures full coverage of the flight state space, and dynamically adjusts the step size according to the stability criterion, which can effectively balance the calculation accuracy and calculation efficiency. Finally, by extracting the safe flight boundary in implicit function form based on polynomial fitting, the method can accurately describe the safe area of ​​the aircraft. The safe flight boundary can effectively distinguish between safe and unsafe flight states, and judge in real time whether the current flight state is within the safe area, thus providing an important basis for flight control and ensuring that the aircraft is always in a safe and stable flight state. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of a method for analyzing safe flight boundaries of a deformable long-flight UAV provided by an embodiment of the present invention; Figure 2 A schematic diagram of radial and spanwise adaptive state grid expansion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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 described herein. On the contrary, these embodiments are provided in order 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, in the absence of conflict, the embodiments of the present invention and the features described 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 combination with the embodiments.

[0018] 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-flight UAV, including: Step S100, obtaining the flight parameters and control input of the UAV, establishing a closed-loop nonlinear flight dynamics model according to the flight parameters and control input of the UAV, and obtaining the simulated flight state vector of the UAV according to the closed-loop nonlinear flight dynamics model.

[0019] Specifically, when obtaining the flight parameters and control input of the drone, it includes: the flight parameters of the drone are specifically the flight position, flight speed, flight angle and angular rate of the drone. The control input is specifically the control surface deflection angle and throttle amount.

[0020] Specifically, a closed-loop nonlinear flight dynamics model is established according to the flight parameters and control input of the UAV, and the simulated flight state vector of the UAV is obtained according to the closed-loop nonlinear flight dynamics model, including: .

[0021] 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.

[0022] It can be seen that by obtaining the flight parameters and control input of the drone. Flight parameters include flight position, flight speed, flight angle and drone angular rate, which are key factors in describing the state of the drone. Control input includes control surface deflection angle and throttle amount, which directly affect the motion trajectory and flight attitude of the drone. By real-time monitoring of these parameters and input, the system can fully understand the current flight state and control input of the drone. Secondly, based on the flight parameters and control input, a closed-loop nonlinear flight dynamics model can be constructed. The flight dynamics model simulates the flight process by describing the motion law, mechanical response and the relationship between the drone and the control input. Closed-loop control means that the model not only considers the current state of the drone, but also feeds back the influence of the control input to ensure that the flight path and attitude can be dynamically adjusted according to real-time data, thereby improving flight stability and accuracy. In addition, 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 the current flight state information of the drone, including flight parameters, control input and its influence on the flight path and attitude. By combining flight parameters with control inputs, the state changes of the drone at future moments can be predicted more accurately, 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, gravity, etc.) usually 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, load changes, etc.) on the flight state. Through these parameters, the model can simulate the real flight state of the drone in a complex environment, thereby achieving accurate flight control and prediction.

[0023] It is understandable that by establishing a closed-loop nonlinear flight dynamics model, the flight state of the UAV can be accurately described and simulated, providing an accurate mathematical basis for flight control and safety analysis. Obtaining the flight parameters and control input of the UAV and incorporating them into the flight dynamics model makes the calculation of the flight state no longer rely on idealized assumptions, but based on actual flight data, so that the UAV can maintain stable flight in complex environments, thereby improving the accuracy and reliability of flight control. Secondly, by comprehensively considering the flight parameters and control input, the integrity of the aircraft state is ensured. Parameters such as flight position, speed, angle and angular rate constitute the basic state information of the UAV, while the control surface deflection angle and throttle amount as input variables directly affect the attitude and motion trajectory of the aircraft. Combining these data for dynamic modeling, the flight dynamics model can truly reflect the flight behavior of the UAV under different control inputs, thereby enhancing the adaptability and flexibility of the flight control system to the UAV response. In addition, by adopting a closed-loop control method, compared with traditional open-loop dynamics modeling, more emphasis is placed on the real-time interaction between the flight state and the control input. Through closed-loop feedback, the calculation of the flight state not only depends on the current input, but can also be adaptively adjusted according to the historical state, thereby improving the stability and anti-interference ability of the system. This is especially important for long-flight UAVs, enabling them to maintain stable flight under complex conditions such as wind field disturbances and load changes, thus enhancing the UAV's environmental adaptability. Ultimately, by obtaining the simulated flight state vector, the UAV's flight state can be quantified and predicted, thus providing a reliable basis for flight control, path optimization, and safety boundary analysis. Based on the calculation of the nonlinear dynamic function f(x(t), u(t), t), it is possible to accurately describe the state changes of the UAV under various flight conditions, enabling the flight control algorithm to be optimized and adjusted under different environments and mission requirements, thereby improving the UAV's flight performance and mission execution capabilities.

[0024] Step S200: Determine the flight state of the UAV based on the relationship between the simulation algorithm and the flight state vector.

[0025] Specifically, based on the relationship between the simulation algorithm and the flight state vector, the flight state of the UAV is determined, including: based on the flight state vector, applying the simulation algorithm to simulate the flight dynamics of the UAV, and the flight state vector includes the flight position, flight speed, flight angle and angular velocity of the UAV. Based on the simulation results of the flight state vector, the flight state of the UAV is simulated and updated in real time, where the flight state includes the stability and trajectory of the current aircraft and the response of the aircraft to the control input. According to the stability judgment criteria of the flight state, it is judged whether the current flight state is a stable state, wherein if the flight state meets the stability criteria, it is judged to be a safe flight state.

[0026] Specifically, the stability criteria include: stable state, critically stable state and unstable state.

[0027] The criteria for determining the stable state are: ∣x(tf)−x target ∣≤ Among them, x(tf) is the flight state of the drone, x target is the target balance point, is the allowable error threshold.

[0028] The criterion for determining the critical stable state 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 definition domain of the state space.

[0029] The unstable state determination criteria are: x(t)∈ / D.

[0030] It can be understood that based on the application of simulation algorithms in flight state analysis, by taking the flight state vector (including parameters such as flight position, speed, angle and angular rate) as input and simulating flight dynamics using simulation algorithms, the state of the aircraft at different time points can be calculated in real time. This method ensures that the flight control system tracks the flight state in real time, thereby more accurately reflecting the movement and behavior of the aircraft, especially during flight in complex environments. Next, the real-time update of the flight state is achieved through the simulation algorithm. At each time step, the state of the aircraft is continuously updated based on the simulation results of the current flight state vector. This includes the stability, trajectory and response of the aircraft to control inputs. In this way, the flight state does not just stay in the static model calculation, but is continuously adjusted and fed back according to the actual flight situation to ensure that the dynamic behavior of the aircraft in a real-time environment can be effectively simulated and controlled. In order to ensure flight safety, the next core technology is the stability judgment criterion. According to the stability of the flight state, it is judged whether the aircraft is in a safe state. This criterion divides the flight state into three types: stable state, critical stable state and unstable state. Through the judgment of these three states, the current flight safety of the aircraft can be clearly evaluated, and corresponding flight recovery or control measures can be taken in time. This process provides a systematic stability assessment for the aircraft during flight, which helps to avoid entering an unsafe state. The stability judgment criteria specifically include three types of states: stable state, critical stable state and unstable state. The judgment criterion for the stable state is to judge whether the aircraft is stable near the target position based on the difference between the target balance point and the allowable error threshold. The critical stable state is judged based on the definition domain of the oscillation time and the state space. If the flight state fails to stabilize within the predetermined time, it is considered that the aircraft enters a critical state. Finally, if the flight state exceeds the defined safety area (that is, the state is not in the definable state space), it is judged as an unstable state. Through these criteria, the flight state of the aircraft can be accurately judged, and appropriate control measures can be taken according to different states to ensure safety and stability during flight.

[0031] Step S300: Based on the initial balance 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.

[0032] Specifically, based on the initial balance 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 balance point, determining the flight state vector of the UAV, expanding the radial grid of the UAV 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: B=b+α·vi.

[0033] 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.

[0034] Specifically, according to the stability analysis results, the step size of the radial grid is dynamically adjusted, including: 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 ranges from 1.05 to 1.2.

[0035] 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 ranges from 0.1 to 0.5.

[0036] When the drone is in an unstable state, the step size is not adjusted.

[0037] It can be understood that by determining the flight state vector based on the initial equilibrium point, a reference point can be provided for the flight state of the UAV, and the flight state grid can be constructed on this basis. Extending the grid along the unit vector direction of the flight state vector can effectively cover the flight state space of the UAV, ensuring that all possible flight states can be considered during the calculation process. Secondly, in the process of expanding the radial grid, the dynamic adjustment of the step size is an important step to ensure the calculation accuracy and efficiency. Specifically, the adjustment of the step size depends on the stability of the current flight state of the UAV. When the UAV is in a stable state, the difference between the flight state vector and the preset flight state vector can be used as the basis for the growth coefficient. Based on this difference, the growth coefficient (usually between 1.05 and 1.2) is used to increase the step size, thereby speeding up the calculation process. This method can improve the simulation efficiency while ensuring the calculation accuracy. When the aircraft enters a critical stable state, the step size needs to be reduced to maintain the stability of the system. At this time, the reduction coefficient (usually between 0.1 and 0.5) is determined based on the difference between the flight state vector and the preset flight state vector. According to this coefficient, the step size will be reduced accordingly, thereby increasing the accuracy of the calculation and avoiding error accumulation or instability caused by too large a step size. When the aircraft is in an unstable state, since the state of the aircraft has exceeded the stable range, the calculation process will stop adjusting the step size to avoid deviations in the numerical calculation results in the unstable state. The purpose at this time is to ensure the stability of the calculation process and avoid further error amplification or unpredictable dynamic behavior. In this way, the adjustment of the step size not only ensures the calculation efficiency, but also maintains the accuracy and stability of the results under different flight conditions.

[0038] Step S400: based on the stable boundary points of the radial grid, the spanwise grid is expanded, and according to the radial grid and the spanwise grid, a high-dimensional state envelope of the flight state of the UAV is generated.

[0039] Specifically, based on the stable boundary points of the radial grid, the spanwise grid is expanded to include: G=g+β·vj.

[0040] Among them, 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 jth spanwise direction, and β is the spanwise step size.

[0041] It can be understood that the stable boundary points of the radial grid provide stable boundaries for the state space of the aircraft, and these points determine the stable range of the aircraft state. By further expanding the grid along the span direction, a wider range of flight state space can be covered, ensuring that all possible flight states can be taken into account, so as to comprehensively analyze the safe flight area of ​​the aircraft. In the process of expanding the span-wise grid, the calculation method uses the span-wise step size and the unit vector in the span-wise direction. Specifically, by using the formula G=g+β⋅vj, the stable boundary point g of the radial grid and the span-wise unit vector vj are combined to calculate the new span-wise grid point G. This means that the expansion of the span-wise grid is carried out along a specific direction, which can better capture the changes in the flight state and provide a full-scale analysis of the aircraft behavior. Finally, the expansion of the span-wise grid not only makes the boundary of the flight state more accurate, but also provides a higher-dimensional state space for the subsequent safe flight boundary analysis. In this way, the safe flight area of ​​the aircraft can be accurately depicted, ensuring that the aircraft is always in a controllable and safe flight state. The technical principles and implementation strategies of this process enhance the stability analysis capabilities of aircraft in complex environments, especially in long-duration flight missions.

[0042] Step S500: extract the safe flight boundary of the UAV in the form of an implicit function according to the high-latitude state envelope based on polynomial fitting, and determine whether the flight state of the UAV is a safe flight state according to the safe flight boundary.

[0043] 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: Extract the safe flight boundary of the drone in implicit function form based on the minimum multiplication fitting polynomial: .

[0044] 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.

[0045] Specifically, according to the safe flight boundary, determining whether the flight state of the drone is a safe flight state includes: obtaining the flight state vector of the drone, and judging whether the flight state of the drone is a safe flight state according to the relationship between the flight state vector and the safe flight boundary: when the flight state of the drone is within the safe flight boundary and is equal to 0, the flight state of the drone is judged to be a safe flight state. When the flight state of the drone is not within the safe flight boundary and is greater than or less than 0, the flight state of the drone is judged not to be a safe flight state.

[0046] It can be understood that by fitting the polynomial by the least square method, an implicit function describing the safe flight boundary can be extracted from the state space of the aircraft. This implicit function F(x) describes whether the flight state is within the safe range, where the coefficient ai and the order mi of the polynomial represent the weight and influence of each state variable respectively, and the state variable xi is the key state parameters of the aircraft (such as position, speed, angle, etc.). In this process, the safe flight boundary in the form of an implicit function is expressed by a polynomial, which can accurately describe the safe area of ​​the aircraft in the state space. This implicit function can effectively distinguish the state of the aircraft inside and outside the safe boundary, and becomes the basis for judging whether the flight state is safe. By fitting the polynomial by the least square method, the high accuracy of the safe boundary model is ensured, thereby ensuring that the judgment of the flight state is more reliable. Next, according to the relationship between the safe flight boundary and the flight state vector, it can be judged whether the flight state of the drone is safe. Specifically, by obtaining the current flight state vector, it is substituted into the implicit function of the safe flight boundary for calculation. If the value of the implicit function is 0 and the flight state is within the safe boundary, the aircraft is judged to be in a safe flight state. If the value of the implicit function is greater than 0 or less than 0 and the flight state exceeds the safety boundary, the aircraft is judged to be in an unsafe flight state. Finally, by comparing the flight state in the high-dimensional state space with the safety boundary in real time, it is effectively ensured that the aircraft is always in a safe state during the entire flight process. Using the safety boundary in the form of an implicit function, the state of the aircraft can be dynamically monitored during the flight, and unsafe flight states can be judged and responded to in a timely manner, thereby improving flight safety.

[0047] In the above embodiment, by establishing a closed-loop nonlinear flight dynamics model, the flight state of the deformable long-flight UAV is accurately modeled. By obtaining the flight parameters and control input of the UAV and establishing a closed-loop nonlinear flight dynamics model based on this information, the flight process of the UAV can be simulated and its flight state vector can be obtained. This process provides reliable data support for subsequent flight state analysis. Next, the actual flight state of the UAV is determined based on the relationship between the simulation algorithm and the flight state vector. This process enables the flight state of the UAV to be highly consistent with the actual flight situation, thereby better providing a basis for the analysis of the safe flight boundary. This analysis not only relies on a static model, but also dynamically reflects the changes in the flight process, ensuring that the aircraft can evaluate its flight state in real time at different flight stages. In addition, by expanding the radial grid along the unit vector direction of the flight state based on the initial equilibrium point, and dynamically adjusting the step size according to the stability of the flight state, a high-precision flight state envelope is constructed. This process ensures full coverage of the flight state space, and dynamically adjusts the step size according to the stability criterion, which can effectively balance the calculation accuracy and calculation efficiency. Finally, by extracting the safe flight boundary in the form of an implicit function based on polynomial fitting, the method can accurately describe the safety area of ​​the aircraft. 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.

[0048] 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 a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0049] 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.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant 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 within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the safe flight boundary of a deformable long-flight UAV, characterized in that: include: Acquire flight parameters and control input of the UAV, establish a closed-loop nonlinear flight dynamics model according to the flight parameters and control input of the UAV, and acquire a simulated flight state vector of the UAV according to the closed-loop nonlinear flight dynamics model; Determining the flight state of the UAV based on the relationship between the 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; Based on the stable boundary points of the radial grid, the spanwise grid is expanded, and according to the radial grid and the spanwise grid, a high-dimensional state envelope of the flight state of the UAV is generated; 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.

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

3. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 2, characterized in that: Establishing a closed-loop nonlinear flight dynamics model according to the UAV flight parameters and the control input, and obtaining a simulated flight state vector of the UAV according to 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),t) is the nonlinear dynamic function.

4. The method for analyzing safe flight boundaries of a deformable long-flight UAV as claimed in claim 3, characterized in that: Based on the relationship between the simulation algorithm and the flight state vector, determining the flight state of the UAV includes: Based on the flight state vector, applying a simulation algorithm to simulate the flight dynamics of the UAV, the flight state vector including the flight position, flight speed, flight angle and angular rate of the UAV; Based on the simulation result of the flight state vector, simulating and updating the flight state of the UAV in real time, wherein the flight state includes the stability and trajectory of the current aircraft and the response of the aircraft to the control input; 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.

5. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 4, characterized in that: The stability criteria include: stable state, critically stable state, and unstable state; The stable state determination criteria are: ∣x(tf)−x target ∣≤ Among them, x(tf) is the flight state of the drone, x target is the target balance point, is the allowable 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 definition domain of the state space; The unstable state determination criteria are: x(t)∈ / D.

6. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 4, characterized in that: Based on the initial balance 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 balance 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; Among them, B is the grid point after the expansion of the drone, b is the current grid point, vi is the unit vector in the i-th direction, and α is the step size.

7. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 6, characterized in that: According to the stability analysis result, dynamically adjusting the step size of the radial grid includes: 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 length is increased according to the growth coefficient, wherein the growth coefficient ranges from 1.05 to 1.2; When the UAV is in the critical stable state, a reduction 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 reduced according to the reduction coefficient, wherein the reduction coefficient ranges from 0.1 to 0.5; When the UAV is in the unstable state, the step length is not adjusted.

8. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 7, characterized in that: Based on the stable boundary points of the radial grid, the spanwise grid is expanded, including: G = g + β·vj; Among them, 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 jth spanwise direction, and β is the spanwise step size.

9. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 1, characterized in that: According to the high-latitude state envelope, based on polynomial fitting, extracting the safe flight boundary of the UAV in an implicit function form 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, 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.

10. The method for analyzing safe flight boundaries of a deformable long-flight UAV according to claim 9, characterized in that: Determining whether the flight state of the UAV is a safe flight state according to the safe flight boundary includes: Obtaining a flight state vector of the UAV, and judging whether the flight state of the UAV is a safe flight state according to the 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.

Images