Method, device and medium for evaluating task capacity of aircraft including uncertainty propagation

Abstract

The invention relates to the technical field of aircraft task capability assessment based on uncertainty propagation, in particular to an aircraft task capability assessment method and device including uncertainty propagation and a medium, which can replace a flight capability prediction mode based on aircraft controlled flight path simulation, remarkably reduce the calculation amount of task capability assessment and improve the reliability of aircraft task capability assessment. And the real-time performance of evaluation is ensured. The invention provides a refined task capability assessment technology based on convex optimization and uncertainty propagation theories for aircraft task capability assessment demands under uncertainty, and establishes a dynamic graph point cloud model of an uncertainty information-flight capability-task capability mapping relation. Through an uncertainty factor carding and characterization method, based on optimized post-fault aircraft capability boundary prediction and on-line trajectory planning based on convex optimization, dynamic graph point cloud task capability evaluation containing uncertainty is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft mission capability evaluation based on uncertainty propagation, and particularly relates to a method, device, and medium for evaluating aircraft mission capability including uncertainty propagation. Background Art

[0002] With the increasingly complex confrontation environment, the mission requirements for the maneuverability, low-altitude penetration, large impact angle attack, obstacle avoidance, etc. of aircraft such as airplanes, missiles, and fighter jets are also becoming increasingly strong. During flight, an aircraft faces a large number of uncertain factors. In addition to random uncertainties such as mass property deviations, aerodynamic model deviations, atmospheric environment disturbances, and sensor measurement deviations, there are also uncertainties caused by failures of actuators, aircraft structures, engines, etc. during flight. The existence of these uncertain factors seriously affects the flight performance of the aircraft, and thus affects the aircraft's ability to perform related missions. Therefore, according to the confrontation situation and flight environment, it is a very important and indispensable task to evaluate the mission capability of the aircraft in real time, and then make intelligent decisions and plan the best subsequent flight missions based on the current capabilities. Traditional methods do not consider various uncertain factors during aircraft flight in the evaluation, and the ability evaluation results are not reliable, or simply applying safety factors leads to overly conservative evaluation results, lacking a refined flight ability evaluation model, which greatly limits the exploration and utilization of flight capabilities. With the increase in the nonlinearity of aircraft dynamics and the flight envelope, the computational amount and complexity of quantifying the uncertainty of aircraft performance have increased significantly, bringing great difficulties to the refined model of the flight ability - mission ability mapping containing uncertainties and the online evaluation of mission capabilities. Therefore, it is of great significance to study the method for evaluating the flight ability of aircraft containing uncertain factors. Summary of the Invention

[0003] The present invention provides a method, device, and medium for evaluating aircraft mission capability including uncertainty propagation, which can replace the flight ability prediction method based on the controlled flight trajectory simulation of the aircraft, significantly reduce the computational amount of mission capability evaluation, and ensure the real-time nature of the evaluation.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] A method for evaluating aircraft mission capability including uncertainty propagation, comprising:

[0006] Step 1: Classify, characterize, and model the uncertain factors affecting the aircraft mission capability;

[0007] Step 2: Establish the dynamic equation of the aircraft centroid motion;

[0008] Step 3: Establish an objective function corresponding to the lower the flight altitude, the larger the attack angle, and the more the remaining energy, the better;

[0009] Step 4: Linearize the dynamic equation and discretize the objective function to obtain a convex programming problem for trajectory planning;

[0010] Step 5: Establish an expression for predicting the aircraft's ability boundary;

[0011] Step 6: Based on the expression for predicting the aircraft's ability boundary, perform closed-loop guidance with rolling convex optimization to obtain an optimal flight trajectory;

[0012] Step 7: Repeat Steps 1 to 6 to obtain optimal flight trajectories under different faults and random uncertainty factors. Based on this, construct a dynamic graph point cloud model containing uncertainties, and then realize the online prediction of the aircraft's flight ability.

[0013] Among them, in Step 2, based on the instantaneous equilibrium assumption, the dynamic equation of the aircraft's center-of-mass motion considering the horizon assumption and coordinated turn is obtained.

[0014] Among them, in Step 2, given any set of parameter values of uncertainty factors, the corresponding flight state trajectory is obtained by calling the controlled point-mass flight trajectory simulation model of the aircraft; the parameters of the uncertainty factors include: the occurrence time of servo failures, damaged lifting surfaces, and thrust drops, fault parameters, aerodynamic uncertainties, and thrust uncertainties.

[0015] Among them, the rate of change of the angle of attack, velocity inclination angle, and thrust is introduced as control variables to expand the dimension of the dynamic equation of the aircraft's center-of-mass motion. The angle of attack, velocity inclination angle, and thrust are all regarded as system state variables to obtain an expanded dynamic equation, and the expanded dynamic equation is used as the final dynamic equation of the aircraft's center-of-mass motion.

[0016] Among them, in Step 6, in each guidance cycle, the aircraft performs online trajectory planning based on convex optimization, obtains the optimal control command that satisfies the constraints under a certain type of mission in real time, and applies the optimal control command to the aircraft to update the current state of the aircraft and the battlefield environment, and then performs convex optimization trajectory planning again, and so on until it reaches the designated position or reaches the termination condition.

[0017] Among them, in Step 1, the uncertainty factors include aerodynamic coefficient perturbations, mass property deviations, random wind disturbances, atmospheric density deviations, sensor noise, servo failures, damaged lifting surfaces, and power drops.

[0018] Among them, in Step 3, considering the ability indicators of ultra-low altitude penetration, large dive angle attacks, and area avoidance, the objective function is established.

[0019] Among them, in the seventh step, the following steps are further included: Based on the constructed dynamic graph point cloud model, input the fault information and historical flight trajectories to predict a large number of flight states after the fault occurs, including the influence of random uncertainties.

[0020] The present invention also provides an electronic device, which includes a processor and a memory for storing executable instructions that can be executed by the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the method for evaluating the mission capabilities of an aircraft including uncertainty propagation according to the present invention.

[0021] The present invention also provides a computer-readable storage medium, which stores a computer program for executing the method for evaluating the mission capabilities of an aircraft including uncertainty propagation according to the present invention.

[0022] Beneficial effects:

[0023] 1. The method of the present invention is oriented to the requirement of evaluating the mission capabilities of an aircraft under uncertainty. Based on convex optimization and uncertainty propagation theory, a refined mission capability evaluation technology is proposed. By constructing a dynamic graph point cloud model for the mapping relationship between uncertainty information - flight capabilities - mission capabilities, through the uncertainty factor sorting and characterization method, the prediction of the aircraft's capability boundary after a fault based on optimization, and the online trajectory planning based on convex optimization, the dynamic graph point cloud mission capability evaluation including uncertainty is realized.

[0024] 2. The present invention is oriented to the requirement of evaluating the mission capabilities of an aircraft under conditions considering uncertainty. For many mission capabilities such as ultra-low altitude penetration, flight capability indicators corresponding to the mission capabilities are established respectively. Many uncertainty factors affecting the flight capability indicators of the aircraft during the flight are sorted out and characterized, and a mission capability mapping relationship model based on dynamic graph point cloud is established, forming a complete set of mission capability evaluation technologies based on the efficient uncertainty propagation theory.

[0025] 3. Compared with the traditional strategy considering the lower bandwidth of the guidance loop, the present invention can fully explore the flight capabilities of the aircraft under faults; the uncertainty propagation of the considered uncertainty factors is carried out based on the dynamic graph point cloud. Compared with the traditional method that does not directly consider uncertainty, the evaluation results are more reasonable, avoiding the conservatism of the safety factor method and being more conducive to the rationality of the aircraft mission planning decision.

[0026] 4. The methods and technologies proposed by the present invention can provide effective theoretical guidance and technical support for the evaluation of the mission capabilities of a fault aircraft under the influence of uncertainty.

[0027] 5. The device of the present invention is used to implement the method of the present invention. Facing the demand for the evaluation of the mission capabilities of aircraft under uncertainty, a refined mission capability evaluation technology is proposed based on convex optimization and uncertainty propagation theory. By constructing a dynamic graph point cloud model of the mapping relationship between uncertainty information - flight capabilities - mission capabilities, through the methods of sorting out and characterizing uncertainty factors, predicting the aircraft capability boundary after failure based on optimization, and online trajectory planning based on convex optimization, the dynamic graph point cloud mission capability evaluation including uncertainty is realized.

[0028] 6. The medium of the present invention is used to implement the method of the present invention. Facing the demand for the evaluation of the mission capabilities of aircraft under uncertainty, a refined mission capability evaluation technology is proposed based on convex optimization and uncertainty propagation theory. By constructing a dynamic graph point cloud model of the mapping relationship between uncertainty information - flight capabilities - mission capabilities, through the methods of sorting out and characterizing uncertainty factors, predicting the aircraft capability boundary after failure based on optimization, and online trajectory planning based on convex optimization, the dynamic graph point cloud mission capability evaluation including uncertainty is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of the method for evaluating the mission capabilities of an aircraft including uncertainty propagation according to the present invention.

[0030] Figure 2 It is a schematic diagram of the key contents involved in the evaluation of the mission capabilities of an aircraft according to the present invention and their associations.

[0031] Figure 3 It is a schematic flow chart of the closed-loop guidance based on receding horizon control and convex optimization according to the present invention.

[0032] Figure 4 It is a schematic flow chart of the construction of the sample library for network training according to the present invention.

[0033] Figure 5 It is a schematic diagram of the dynamic graph - point cloud - convolutional deep network structure for flight trajectory prediction according to the present invention.

[0034] Figure 6 It is a schematic diagram of the online prediction of flight capabilities based on dynamic graph point cloud according to the present invention.

[0035] Figure 7 It is a schematic diagram of the mission capability evaluation result when a failure occurs at x = 1000m in the embodiment of the present invention.

[0036] Figure 8 It is a schematic diagram of the mission capability evaluation result when a failure occurs at x = 2000m in the embodiment of the present invention.

[0037] Figure 9 It is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] The present invention proposes a method for evaluating the mission capabilities of an aircraft including uncertainty propagation, and the specific implementation process is as follows Figure 1 . In the method of the present invention, first, research on modeling and characterization is carried out for various uncertainty factors that may affect the flight capabilities and mission capabilities of the aircraft, laying a foundation for subsequent mission capability evaluation based on uncertainty propagation; then, an aircraft flight motion model and mission capability quantification indexes for evaluating flight capabilities and mission capabilities are established to measure the capabilities of the aircraft to complete tasks such as ultra-low altitude penetration, large-angle-of-descent attack, arrival time control, area avoidance, circumferential flight reconnaissance, terminal correction, and overflight of mountain obstacles; secondly, a trajectory online planning and optimal guidance algorithm based on convex optimization under faults is constructed to explore the capability boundaries of the aircraft and generate the best guidance commands for executing a certain type of mission under faults. Since the mission capability evaluation of the aircraft must be carried out on the set guidance law, on the basis of the above convex optimization trajectory optimization, a receding horizon control is introduced to form a closed-loop guidance, and then the capability evaluation is carried out; finally, based on the established uncertainty information model, rolling convex optimization closed-loop guidance, and mission capability quantification index model, a dynamic graph point cloud model is constructed to achieve efficient and accurate uncertainty propagation during the capability evaluation process and achieve the purpose of real-time mission capability evaluation.

[0039] Based on the motion model of the aircraft, the guidance and control method, and the relevant theories of uncertainty propagation, and facing the demand for evaluating the mission capabilities of the aircraft under uncertain conditions, the present invention gradually implements the mission capability evaluation of the aircraft mainly from the following four aspects, and the correlation of each part is as follows Figure 2 .

[0040] 1. Sorting out and characterizing uncertainty factors: To analyze the impact of uncertainty on the mission capabilities of the aircraft, it is first necessary to reasonably model and characterize the uncertainty, which specifically includes:

[0041] · Tracing the sources and classifying the uncertainty factors that affect the mission capabilities of the aircraft, with a focus on uncertainties such as aerodynamic coefficient perturbations, mass property deviations, environmental disturbances (random wind, atmospheric density), sensor measurement noise, and actuator failures;

[0042] · Characterizing random uncertainty factors based on parameter estimation;

[0043] · Analyzing failure types such as actuator / lifting surface / thrust degradation and uncertainty modeling

[0044] 2. Establishing the flight dynamics and kinematics models considering uncertainty factors: The mission capabilities depend on the flight capability indexes of the aircraft. Therefore, to finely analyze the impact of uncertainty on the mission capabilities of the aircraft, it is necessary to establish the aircraft motion equation and flight trajectory simulation model, which specifically includes:

[0045] · Establish the three - degree - of - freedom dynamics and kinematics models for the particle motion of the aircraft;

[0046] · Build the simulation model of the three - degree - of - freedom controlled particle flight trajectory of the aircraft considering uncertainties;

[0047] · Build the online trajectory planning model based on optimal control.

[0048] 3. Construction of the task - ability mapping neural network surrogate model containing uncertainties: To ensure the timeliness of aircraft task - ability assessment, it is necessary to build a task - ability assessment model considering uncertainties to replace the large number of flight - trajectory simulations based on traditional Monte Carlo simulations. Specifically, it includes:

[0049] · Establish the flight - ability indexes of the aircraft for multiple flight tasks such as ultra - low - altitude penetration;

[0050] · Uncertainty factors based on the aircraft flight - trajectory simulation model or online trajectory planning model—

[0051] —Generate the input - output samples of flight - ability indexes;

[0052] · Construct the dynamic graph point - cloud model of uncertainty factors (input) — flight - ability indexes (output);

[0053] · Construct the task - ability evaluation probability model based on multi - source uncertainty propagation.

[0054] 4. Online task - ability assessment: Carry out simulation verification on the proposed method for typical task scenarios. Comprehensively consider various deviation uncertainties, environmental disturbances (random wind, atmospheric density), and servo failures, conduct uncertainty propagation based on the dynamic graph point - cloud model, calculate the changes in the aircraft state and flight - ability indexes after a failure occurs, and evaluate the task - completion ability of the failed aircraft under the influence of uncertainties.

[0055] The method of the present invention specifically includes the following operation steps:

[0056] Step 1: Model the uncertainties of random parameters and servo failures. Classify and characterize various uncertainty factors affecting the task ability of the aircraft, such as the perturbation of aerodynamic coefficients (C x / C y / C z ), mass - property deviation (m), random - wind interference (U 0 / W 0 ), atmospheric - density deviation (ρ), sensor noise (ε), servo failure (δ), damaged lifting surface (C L ), power drop (P), etc., according to expert opinions, engineering experience, sample data, product - factory - specified parameter values, etc., as shown in Equation (1).

[0057]

[0058] wherein, δ′ / C L ′ / P′ respectively represent the actual rudder deflection angle / lift coefficient / thrust.

[0059] Step 2: Establish a three-degree-of-freedom controlled particle trajectory simulation model under uncertainty.

[0060] Based on the instantaneous equilibrium hypothesis, considering the kinematic and dynamic equations of the center of mass motion of the aircraft under the horizon hypothesis and coordinated turn are as shown in Equation (2).

[0061]

[0062] wherein, V is the aircraft speed, θ is the speed inclination angle, ψ is the speed deviation angle, (x, y, z) represents the position of the aircraft relative to the coordinate origin, D, L, P are the aerodynamic drag, lift, and thrust of the aircraft, α is the total angle of attack; γ V is the speed tilt angle.

[0063] Given any set of parameter values of uncertainty factors, including: the occurrence time R of servo failure / damaged lift surface / thrust reduction F , the fault parameters of servo failure / damaged lift surface / thrust reduction (for servo failure, the fault type is also included), aerodynamic uncertainty ΔC L / ΔC D , thrust uncertainty ΔT, etc., and the corresponding flight state trajectories (x(t), y(t), z(t), V(t), m(t), θ(t), ψ(t)) are obtained by calling the controlled particle flight trajectory simulation model of the aircraft.

[0064] Step 3: Establish optimal control modeling and mission capability indicators.

[0065] Introduce the change rates of the angle of attack, speed tilt angle, and thrust as control variables, expand the dimension of the original dynamic equation shown in Equation (2), and regard the angle of attack, speed tilt angle, and thrust as system state variables. The specific dynamic equation expression is as follows:

[0066]

[0067] wherein, the independent variable is x, the state variable is X = [y, z, V, θ, ψ, m, α, γ V , p], and the control variable is U = [u 1 , u 2 , u 3 .

[0068] Considering the ability indexes of tasks such as ultra-low altitude penetration, large angle-of-attack attack, and area avoidance, the requirements are that the lower the flight altitude, the larger the attack angle-of-fall, and the more remaining energy, the better. The corresponding objective functions are established as equations (4), (5), and (6) respectively.

[0069]

[0070] Where d is the safe height from the ground, and h(x) is the terrain height; c 1 , c 2 , c 3 is the weight coefficient.

[0071] Step 4: Linearize the dynamic equation and discretize the optimal control problem to obtain a convex programming problem for trajectory planning for efficient solution.

[0072] Taking the large angle-of-attack attack as an example, with the number of discrete points being n, the optimal control problem is converted into a parameter optimization problem as equation (7) to realize the conversion of the non-linear optimal control problem into a convex programming problem.

[0073]

[0074] Where X 0 and X f represent the initial and final states of the aircraft respectively; Q s is the heat flux density, q is the dynamic pressure, k h is a constant coefficient, q min , q max , Q smax are pre-given values, ρ is the air density; δ is the trust region boundary specified artificially.

[0075] Step 5: Predict the ability boundary of the aircraft in case of failures such as the servo.

[0076] The flight ability of the aircraft is reflected in the solution of the range of the angle of attack. Taking the servo failure as an example, an optimization problem for the maximum available angle of attack as shown in equation (8) is established, and the maximum available angle of attack α max_fault and the minimum available angle of attack α min_fault under the failure condition are solved, that is: the ability boundary of the aircraft.

[0077]

[0078] Where δ i_min , δ i_max represent the minimum and maximum rudder deflections of the i-th rudder under normal conditions; Cl, Cm, Cn represent the aerodynamic moment coefficients; δ i_fault is the jamming angle of the i-th rudder, and K i_fault is the rudder effectiveness degradation coefficient of the i-th rudder.

[0079] Step 5: Based on the ability boundary prediction result in Step 4, perform closed-loop guidance of rolling convex optimization to obtain an optimal flight trajectory.

[0080] In each guidance cycle, the aircraft conducts online trajectory planning based on convex optimization, obtains optimal control commands that meet the constraints under a certain type of mission in real time, applies the optimal control commands to the aircraft, updates the current state of the aircraft and the adversarial environment, and conducts trajectory planning of convex optimization again. This cycle continues until the aircraft reaches the designated position or meets the termination conditions. The overall process is as Figure 3 shown.

[0081] Step 6: Repeat Steps 1 - 5 to obtain optimal flight trajectories under a large number of different faults and random uncertainty factors. Based on this, construct a dynamic graph point cloud model containing uncertainties, and further realize the online prediction of the flight ability of the aircraft.

[0082] Taking the servo failure as an example, the general process of constructing the supervised sample database is as Figure 4 shown. Using the fault mode and its parameters, and uncertainty variables as inputs, extract the deep features contained therein through a multi-layer perceptron, fuse them with the deep features of the historical flight trajectory extracted by the dynamic graph point cloud convolution structure, and quickly predict the flight trajectory under the action of uncertainties after the fault. Convert the point cloud data structure into a graph structure through the K-Nearest Neighbor algorithm (KNN), introduce graph learning theory, and enable the network to effectively learn the spatial correlation information between discrete points in the historical trajectory. The graph-point cloud-convolution deep network structure for future flight trajectory prediction is as Figure 5 shown.

[0083] Step 7: Based on the constructed dynamic graph point cloud model, input the fault information (fault mode, fault time) and the historical flight trajectory, and then a large number of flight states including the influence of random uncertainties after the fault can be quickly predicted.

[0084] Since each flight ability index d * , θ * , h L * , m * , w * , z * , t min , t max , h H * is a function of the flight state. According to the results of quantifying the uncertainty of the flight state (mean and variance), the uncertainty of each flight ability index can be conveniently calculated, that is: the probability that the aircraft can successfully execute various tasks. Figure 6It shows the flight ability prediction process based on the dynamic graph point cloud model, conducts fast uncertainty propagation considering the influence of random uncertainty based on the dynamic graph point cloud, avoids the simulation process of a large number of high-precision ballistic calculations multiple times, obtains the flight ability in the sense of probability and the reliability of the aircraft to successfully execute various tasks, and completes the real-time evaluation of the refined task ability under the influence of uncertainty.

[0085] To verify the effectiveness of the method proposed by the present invention, the above-mentioned aircraft dynamics model is adopted, and it is considered that the aircraft has one of the faults of control surface failure, lift surface damage, and power decline at a certain time. Under the influence of various random uncertainty factors such as environmental interference, aerodynamic coefficient perturbation, and mass deviation, taking the large-angle-of-attack mission as an example, the feasibility of the aircraft to execute the mission is evaluated. The uncertainty quantification results and the mission ability evaluation results are shown in Table 1 and Figure 7 and Figure 8 respectively, where MD represents the number of samples that both the direct MCS and the graph point cloud judge to be able to successfully execute the large-angle-of-attack mission among 10,000 groups of samples; represents the number of samples that the direct MCS judges to be unable to execute the large-angle-of-attack mission but the dynamic graph point cloud judges to be able to successfully execute; represents the number of samples that the graph point cloud judges to be unable to execute but the direct MCS judges to be able to successfully execute; represents the number of samples that both judge to be unable to successfully execute.

[0086] Table 1 The first two-order statistical moments of the terminal deviation and the mission ability evaluation results

[0087]

[0088] It can be seen from the results that the later the fault occurs (1000m → 2000m), the lower the mission reliability (0.894 → 0.724; 0.611 → 0.511). The earlier the fault occurs, the greater the adjustment space left for the aircraft, and the wider the solution space provided for convex optimization. Therefore, it is easier to obtain the optimal solution, which proves the superiority of convex optimization in exploring the flight potential of the aircraft. Due to the influence of uncertainty, the flight states of the aircraft all fluctuate, and the deviations of the terminal position and the terminal angle of attack also show uncertainty. Therefore, the event of being able to execute the large-angle-of-attack mission needs to be evaluated from a probability perspective, rather than "can" or "cannot" under the traditional method. For different degrees of the same fault mode, compared with the direct Monte Carlo simulation (MCS) method, the mission reliability obtained by the mission ability evaluation based on the dynamic graph point cloud has a correct rate greater than 95% (95.43%, 97.73%, 97.68%, and 96.88% respectively), indicating the effectiveness of the proposed method.

[0089] Based on this calculation example, it can be proved that the aircraft mission capability evaluation technology based on uncertainty propagation disclosed in this patent is effective.

[0090] An embodiment of the present application also provides an electronic device. Figure 9 The structure of the electronic device provided by the embodiment of the present invention is shown. For example, the electronic device 90 may include a processor 91, a memory 92, and a transmission device 93. The processor 91 is used to execute the aircraft mission capability evaluation method including uncertainty propagation mentioned in the above embodiment. The processor and the memory may be connected through a bus or other means. Taking the connection through the bus as an example. The transmission device can be connected to the processor and the memory in a wired or wireless manner. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions and modules corresponding to the aircraft mission capability evaluation method including uncertainty propagation in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, that is, to implement the aircraft mission capability evaluation method including uncertainty propagation in the above method embodiment. The memory may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely provided relative to the processor, and these remote memories may be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof. The one or more modules are stored in the memory and, when executed by the processor, execute the aircraft mission capability evaluation method including uncertainty propagation in the embodiment.

[0091] On the other hand, the present application also provides a computer-readable storage medium. The computer-readable storage medium may be the computer-readable storage medium included in the device in the above embodiment; it may also exist separately and not be assembled into the device. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the art. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the aircraft mission capability evaluation method described in the present application including uncertainty propagation.

[0092] The above are the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating aircraft mission capability including uncertainty propagation, characterized in that: include: Step 1: Classify, characterize and model the uncertainty factors that affect the mission capability of the aircraft; Step 2: Establish the dynamic equation of the center of mass motion of the aircraft; Step 3: Establish an objective function corresponding to the lower the flight altitude, the larger the attack angle, and the more residual energy, the better; Step 4: Linearize the dynamic equation and discretize the objective function to obtain the convex programming problem of trajectory planning; Step 5: Establish an expression that predicts the aircraft capability boundary; Step 6: Based on the expression predicted by the aircraft capability boundary, a rolling convex optimization closed-loop guidance is performed to obtain an optimal flight trajectory; Step 7: Repeat steps 1 to 6 to obtain the optimal flight trajectory under different faults and random uncertainty factors. Based on this, a dynamic graph point cloud model containing uncertainty is constructed to achieve online prediction of the aircraft's flight capability.

2. The method according to claim 1, characterized in that In the step 2, based on the instantaneous equilibrium assumption, the dynamic equation of the motion of the center of mass of the aircraft under the assumption of a flat ground and coordinated turning is obtained.

3. The method according to claim 2, characterized in that In the step 2, given any set of parameter values ​​of uncertainty factors, the corresponding flight state trajectory is obtained by calling the controlled particle flight trajectory simulation model of the aircraft; the parameters of the uncertainty factors include: the occurrence time of servo failure, lift surface damage and thrust reduction, fault parameters, aerodynamic uncertainty and thrust uncertainty.

4. The method according to claim 2 or 3, characterized in that The angle of attack, velocity inclination angle and rate of change of thrust are introduced as control quantities, and the dynamic equation of the motion of the center of mass of the aircraft is expanded. The angle of attack, velocity inclination angle and thrust are all used as system state quantities to obtain the expanded dynamic equation, and the expanded dynamic equation is used as the final dynamic equation of the motion of the center of mass of the aircraft.

5. The method according to any one of claims 1 to 3, characterized in that: In step six, in each guidance cycle, the aircraft performs online trajectory planning based on convex optimization, obtains the optimal control instructions that meet the constraints under a certain type of task in real time, and applies the optimal control instructions to the aircraft, updates the current state of the aircraft and the battlefield environment, and performs convex optimization trajectory planning again, and repeats this cycle until it reaches the specified position or reaches the termination condition.

6. The method according to any one of claims 1 to 3, characterized in that: In the step 1, the uncertain factors include aerodynamic coefficient disturbance, mass characteristic deviation, random wind interference, atmospheric density deviation, sensor noise, steering gear failure, lifting surface damage and power reduction.

7. The method according to any one of claims 1 to 3, characterized in that: In the step three, the objective function is established by considering the capability indicators of ultra-low altitude penetration, large angle attack and area avoidance.

8. The method according to any one of claims 1 to 3, characterized in that: The step seven also includes the following steps: based on the constructed dynamic graph point cloud model, inputting fault information and historical flight trajectories, predicting a large number of flight states including random uncertainty effects after the fault occurs.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the aircraft mission capability assessment method including uncertainty propagation as described in any one of claims 1-8 above.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the aircraft mission capability assessment method including uncertainty propagation as described in any one of claims 1 to 8.

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