Tailless flying wing unmanned aerial vehicle fault-tolerant control method and system for single aileron soft-stuck failure

Abstract

This invention proposes a fault-tolerant control method and system for tailless flying-wing UAVs experiencing a single aileron soft-lock failure, belonging to the field of UAV flight control and fault-tolerant control technology. Addressing the asymmetric dead moment, heading divergence, and conventional redundant reconfiguration failure problems caused by a single aileron soft-lock failure in tailless flying-wing UAVs, this invention establishes a healthy-state dynamic model and a soft-lock nonlinear decoupling model, decomposing the faulty control surface deflection angle into a constant bias and a small dynamic component; employing a position response dual-verification mechanism to diagnose the fault and extract bias estimates; obtaining feedforward trim commands for the healthy-side control surface through algebraic inverse solution to counteract the asymmetric dead moment; injecting high-frequency sinusoidal jitter signals into the healthy-side control surface to induce sideslip angles and utilizing the wind vane effect to generate yaw moments for heading correction; and generating integrated control commands including saturation limiting. This invention eliminates the need for additional redundant hardware, effectively improving the survivability and flight safety of tailless flying-wing UAVs under soft-lock failure conditions.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control and fault-tolerant control technology, and in particular to a fault-tolerant control method and system for a tailless flying wing UAV with a single aileron soft jamming fault. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Tailless flying wing UAVs, lacking a vertical tail structure, possess significant advantages such as high aerodynamic efficiency and small radar cross-section, making them an important direction for modern UAV development and widely used in reconnaissance, patrol, and other aviation operations. However, this layout also has inherent problems such as a limited number of control surfaces and strong lateral aerodynamic coupling characteristics, posing severe challenges to flight control stability and safety. One such challenge is the "soft jamming" fault of a single-sided elevon caused by actuator mechanical jamming. This type of fault manifests as the faulty control surface maintaining a large offset angle with a very small range of motion, generating an asymmetric dead moment, causing the UAV's flight state to diverge, resulting in severe roll and sideslip. Because tailless flying wing UAVs lack rudders and the actuators that directly generate yaw moments, traditional control strategies cannot effectively correct heading deviations, and improper handling of such faults can easily lead to loss of control and crash.

[0004] Current fault-tolerant control schemes for UAV actuator failures have significant technical shortcomings when facing such extreme failures. On the one hand, the mainstream fault-tolerant control method relies on the redundancy reconstruction of control allocation, using redundant control surfaces to redistribute forces and moments. However, in the case of a single aileron jamming, the control allocation matrix cannot find a feasible solution, and the redundancy reconstruction completely fails. On the other hand, existing flight control logic classifies lateral aerodynamic coupling phenomena as instability and suppresses them. However, when a tailless flying wing UAV loses its rudder, suppressing lateral motion directly cuts off the path for generating yaw moment, ultimately causing the heading to continue diverging. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a fault-tolerant control method and system for tailless flying-wing UAVs experiencing a single aileron soft-lock fault. By establishing a nonlinear decoupling model for the soft-lock fault, the faulty control surface deflection angle is decomposed into a constant bias and a small dynamic component. This constant interference source is then separated by substituting these components into the pitch moment equation. A dual-verification mechanism for position response is used to diagnose the fault and extract the bias estimate. Feedforward trim commands for the healthy side control surface are obtained based on algebraic inverse kinematics to counteract the asymmetric dead moment. Simultaneously, a high-frequency sinusoidal jitter signal is injected to induce sideslip angles, and a yaw moment is generated based on the wind vane effect to correct the heading. The jitter intensity is dynamically adjusted in real-time based on the remaining travel distance. This invention eliminates the need for redundant hardware, effectively solving the technical challenges of conventional redundant reconfiguration failure and the inability to correct heading without a rudder under a single-side elevator soft-lock fault in tailless flying-wing UAVs, thus improving fault survivability.

[0006] On the one hand, a fault-tolerant control method for a tailless flying-wing UAV with a single aileron soft-lock fault is provided, including: Establish a conventional virtual control surface dynamics model of the UAV under healthy conditions, and determine the pitch moment equation, roll moment equation, and yaw moment equation; A soft jamming nonlinear decoupling model is established, which decomposes the actual physical deflection angle of the faulty control surface into the jamming center position offset and the residual small dynamic component, and substitutes them into the pitching moment equation to separate the constant interference source. Calculate the absolute position error and response residual of the faulty control surface, diagnose the fault when the joint boundary crossing condition is met, and extract the estimated value of the fault offset. Based on the estimated fault bias, the soft-jamming nonlinear decoupling model is used to perform an algebraic inverse solution to obtain the feedforward trim command for the healthy side control surface, so as to counteract the constant interference source. A high-frequency sinusoidal jitter signal is injected into the healthy side control surface to induce sideslip angle and generate yaw moment using the wind vane effect for course correction. At the same time, the remaining physical travel of the healthy side control surface is calculated in real time, and the injection intensity of the high-frequency sinusoidal jitter signal is adjusted according to the remaining physical travel. By combining the feedforward trim command, the longitudinal pitch stabilization command, and the adjusted high-frequency sinusoidal jitter signal, a comprehensive control command for the healthy side control surface is generated.

[0007] Furthermore, the establishment of the soft-jamming nonlinear decoupling model decomposes the actual physical deflection angle of the faulty control surface into the jamming center position offset and the residual small dynamic component, specifically: Establish independent physical elliptic deflection angles for the left and right wings. , With virtual total elevator deflection Virtual total aileron deflection Control allocation mapping relationship: ; The actual physical deflection angle of the faulty control surface is decomposed using the following formula: (t) = + Δ (t); in Δ is the offset of the center position of the aileron jamming. (t) represents the residual small dynamic component. This is the virtual total elevator deflection angle. Virtual total aileron deflection.

[0008] Furthermore, the absolute position error is calculated by the difference between the commanded deflection angle and the actual feedback deflection angle of the faulty control surface, and the response residual is calculated by the difference between the expected roll rate and the actual roll rate; the joint boundary crossing condition is: the absolute position error is greater than the dynamic pressure adaptive position threshold, and the response residual is greater than the maneuver adaptive response threshold. The dynamic pressure adaptive position threshold is: ,in, For location tolerance parameters, To address the tolerance parameter, V represents the airspeed. This is the dynamic pressure adaptive position threshold coefficient; The specific threshold for the adaptive maneuver response is: ,in, For the desired roll rate, This represents the threshold coefficient for the adaptive response of the maneuver.

[0009] Furthermore, when extracting the estimated value of the fault offset, a first-order low-pass filter is used to filter the actual feedback offset angle of the faulty control surface in order to filter out sensor measurement noise and mechanical vibration interference.

[0010] Furthermore, by performing an algebraic inverse solution using the aforementioned soft-lock nonlinear decoupling model, the feedforward trim command for the healthy side control surface is obtained, specifically: Establish the algebraic balancing equation: ; in, The zero-lift pitch moment coefficient, Let be the static stability derivative of the pitching moment with respect to the angle of attack. For the angle of attack of flight, For the pitch control derivatives of the left and right elevators, Fault bias estimate This is a static disturbance rejection feedforward trim command; The trim equations are solved algebraically to obtain the feedforward trim command for the healthy side elliptic. .

[0011] Furthermore, the calculation formula for the high-frequency sinusoidal dithering signal is as follows: ; Where A is the amplitude. (0, ), ω represents the safe upper limit threshold for jitter signals, where ω is the angular frequency.

[0012] Furthermore, the formula for calculating the remaining physical travel is: ; in, This refers to the physical limit deflection angle of the elevons. This is a pitch command. This is a feedforward balancing command.

[0013] Furthermore, the integrated control command for the healthy side control surface is obtained by summing the feedforward trim command, the longitudinal pitch stabilization command, and the high-frequency sinusoidal jitter signal weighted by dynamic weighting coefficients, and then processing it through a saturation limiting function. The limiting range of the saturation limiting function is the physical limit deflection angle range of the control surface; the dynamic weighting coefficients... The rules for determining the value are as follows: ; in, This is the safe upper limit threshold for jitter signals. This is the safe lower limit threshold for jitter signals. For the remaining physical journey.

[0014] On the other hand, a fault-tolerant control system for a tailless flying-wing UAV with a single aileron soft-lock fault is provided, including: The health state dynamics model module is configured to: establish a conventional virtual control surface dynamics model of the UAV in a healthy state, and determine the pitch moment equation, roll moment equation, and yaw moment equation; The fault diagnosis and offset estimation module is configured to: establish a soft jamming nonlinear decoupling model, decompose the actual physical deflection angle of the faulty control surface into the jamming center position offset and residual small dynamic components, and substitute them into the pitching moment equation to separate the constant interference source. The decoupled model is configured to: calculate the absolute position error and response residual of the faulty control surface, diagnose the fault when the joint boundary crossing condition is met, and extract the estimated value of the fault offset. The static compensation module is configured to: based on the estimated fault bias, perform an algebraic inverse solution using the soft-lock nonlinear decoupling model to obtain the feedforward trim command for the healthy side control surface, so as to counteract the constant interference source. The heading reconstruction module is configured to: inject a high-frequency sinusoidal jitter signal into the healthy side control surface to induce a sideslip angle and generate a yaw moment using the wind vane effect for heading correction; at the same time, calculate the remaining physical travel of the healthy side control surface in real time and adjust the injection intensity of the high-frequency sinusoidal jitter signal according to the remaining physical travel. The instruction generation module is configured to generate a comprehensive control instruction for the healthy side control surface by combining the feedforward trim instruction, the longitudinal pitch stabilization instruction, and the adjusted high-frequency sinusoidal jitter signal.

[0015] Furthermore, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, performs the method of the first aspect.

[0016] The above technical solution has the following advantages or beneficial effects: (1) This invention abandons the traditional control allocation and reconfiguration approach based on redundant hardware and adopts a scheme that combines algebraic feedforward decoupling and active jitter reconfiguration. It is suitable for the extreme underactuated working condition of single aileron jamming, without the need to add additional control surfaces and other hardware, thus reducing the engineering implementation cost and the structural complexity of the UAV.

[0017] (2) This invention breaks through the existing control logic’s suppression of lateral aerodynamic coupling, actively utilizes the aerodynamic coupling effect to generate yaw moment, fills the blind spot of yaw control mechanism when tailless flying wing UAVs are without rudder, and realizes effective heading correction in underactuated state.

[0018] (3) The present invention introduces a fault diagnosis mechanism with dual position-response verification, which combines dynamic pressure adaptive position threshold and motor adaptive response threshold for joint judgment, greatly improving the reliability and accuracy of fault diagnosis.

[0019] (4) This invention establishes an engineering bottom line of absolute priority for longitudinal altitude preservation. By calculating the remaining physical travel of the healthy control surface in real time, and adjusting the injection intensity of the high-frequency jitter signal by combining the dynamic weight coefficient of the piecewise function, and cooperating with the saturation limiting function to avoid saturation of the healthy control surface, it prevents altitude loss and mechanical damage to the actuator caused by heading correction, thereby improving flight stability and survivability in fault conditions. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 A schematic diagram of the method flow in Embodiment 1 of the present invention; Figure 2 The overall architecture diagram of the method in Embodiment 1 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. Those skilled in the art should understand that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] Example 1 This embodiment provides a fault-tolerant control method for a tailless flying-wing UAV with a single aileron soft-jamming fault. Figure 1 This is an overall flowchart of the method according to Embodiment 1 of the present invention. The method includes the following steps: S101: Establish a conventional virtual control surface dynamics model of the UAV under healthy conditions, and determine the pitch moment equation, roll moment equation, and yaw moment equation; S102: Establish a soft jamming nonlinear decoupling model, decompose the actual physical deflection angle of the faulty control surface into the jamming center position offset and residual small dynamic components, and substitute them into the pitching moment equation to separate the constant interference source. S103: Calculate the absolute position error and response residual of the faulty control surface. When the joint boundary crossing condition is met, the fault is diagnosed and the estimated value of the fault offset is extracted. S104: Based on the fault bias estimate, the algebraic inverse solution is performed using the soft-jamming nonlinear decoupling model to obtain the feedforward trim command for the healthy side control surface, so as to counteract the constant interference source. S105: Inject high-frequency sinusoidal jitter signals into the healthy side control surface to induce sideslip angle and generate yaw moment using the wind vane effect for heading correction. At the same time, calculate the remaining physical travel of the healthy side control surface in real time and adjust the injection intensity of the high-frequency sinusoidal jitter signal according to the remaining physical travel. S106: Combines the feedforward trim command, the longitudinal pitch stabilization command, and the adjusted high-frequency sinusoidal jitter signal to generate a comprehensive control command for the healthy side control surface.

[0025] In step S101, the conventional virtual control surface dynamics model of the flying wing UAV in a healthy state is used as the basis. This model includes the three moment equations of pitch, roll, and yaw, and is the conventional basic model for the flight control of tailless flying wing UAVs. Specifically: Pitch Moment Equation (M): ; Roll Moment Equation (L): ; Yaw Moment Equation (N): ; In the above formulas, the resting dynamic pressure during flight Wing reference area S, aerodynamic mean chord c, wingspan b, airspeed V, angle of attack α, sideslip angle β, fuselage axial roll rate p, pitch rate q, yaw rate r, zero-lift pitch moment coefficient Zero sideslip rolling moment coefficient Zero sideslip yaw moment coefficient The static stability derivative of pitch moment with respect to angle of attack Static stability of rolling torque with respect to sideslip angle The static stability derivative of yaw moment with respect to sideslip angle Damped derivative of pitch moment with respect to pitch angular velocity Damping derivative of rolling torque with respect to rolling angular velocity Damping derivative of yaw moment with respect to yaw angular velocity The cross derivative of rolling moment with respect to yaw rate The cross derivative of yaw moment with respect to roll rate elevator pitch operation derivative Aileron roll operation derivative Aileron yaw cross operation derivative Virtual total elevator deflection The engine thrust pitching moment, maintained by the bottom velocity loop, is based on the fundamental dynamic pressure. Virtual total aileron deflection These are all standard physical parameters in the field of flight control for tailless flying wing UAVs, and will not be elaborated here.

[0026] In step S102, a soft-lock nonlinear decoupling model is established. The core of this model is to break the conventional virtual control surface assumption, establish the control distribution mapping relationship between the deflection angles of the left and right independent physical elevators and ailerons and the virtual control surfaces, and decompose the actual physical deflection angle of the faulty control surface into the offset of the jam center position and the residual small dynamic components, and then substitute them into the pitch moment equation to separate the constant interference source that needs to be eliminated at a fixed point.

[0027] Specifically, the first step is to establish the deflection angles of the left and right independent physical elliptic wings. , With virtual total elevator deflection Virtual total aileron deflection The control allocation mapping relationship is defined by the following formula: ; Taking the soft jamming failure of the right elevon as an example, the actual physical deflection angle of the faulty control surface is explicitly decomposed into the offset of the center position of the aileron jamming. With residual small dynamic component Δ (t), the splitting formula is: (t) = + Δ (t) After splitting (t) Substituting into the pitch moment equation of the tailless flying wing UAV, the constant interference sources that need to be eliminated at specific points are accurately isolated by variable separation, resulting in the pitch moment equation containing the fault bias: ; in, and The pitch control derivatives for the left and right elevators are inherent parameters in the aerodynamic design of the UAV.

[0028] Through the above decomposition and substitution, the constant interference terms are clearly identified. The components were successfully separated, providing a clear target for subsequent feedforward compensation, while the remaining small dynamic components... Then the absorption can be naturally handled by the inner ring damping.

[0029] In step S103, to further improve the reliability of fault diagnosis and eliminate false alarms that may be caused by aerodynamic deformation or single-point sensor failure, this embodiment introduces an adaptive dual verification mechanism. This mechanism simultaneously monitors the control surface position deviation and the flight attitude response residual, and the specific implementation process is as follows.

[0030] First, calculate the command deflection angle of the faulty right control surface in real time. Actual feedback deflection angle The absolute positional error between them | Simultaneously, the desired roll angular velocity based on the dynamic model is calculated. Compared with the roll angular velocity actually measured by the IMU The response residuals between .

[0031] Among them, the command deflection angle of the right-side faulty control surface Right elevator control command output from flight control system; actual feedback deflection angle of the faulty right control surface. The desired roll angular velocity is collected in real time by the position sensor built into the right actuator. Generated by attitude control loop; actual roll rate The data was obtained in real time by the airborne inertial measurement unit (IMU).

[0032] Secondly, set the dynamic pressure adaptive position threshold. and the adaptive response threshold of the maneuver The dynamic pressure adaptive position threshold is: ; The adaptive response threshold for maneuver is: ; in The position tolerance parameter is 5° (preferably). To provide a response tolerance parameter (preferably 0.18 rad / s), where V is the airspeed. The dynamic pressure adaptive position threshold coefficient is preferably 0.005. The threshold coefficient for the adaptive response is preferably 0.15.

[0033] Optionally, the above preferred values ​​can be adaptively adjusted according to the specific model of the UAV, its aerodynamic characteristics, and the accuracy of the sensor hardware.

[0034] When both conditions are met and When the combined out-of-bounds conditions are met, a soft jam fault in the right elevon is formally diagnosed. Upon diagnosis, the system immediately ceases sending any control commands to the faulty control surface.

[0035] To obtain a stable and accurate estimate of the fault bias, a first-order low-pass filter (LPF) is used to measure the actual feedback deflection angle of the faulty control surface. Filtering is performed to effectively remove high-frequency measurement noise from the position sensor and mechanical vibration interference, thereby extracting a stable estimate of the fault bias. The filtering formula is: ; in, Used to describe the frequency domain characteristics of a low-pass filter. The time constant of the low-pass filter can be selected according to the noise characteristics of the UAV sensor. In this embodiment, it is preferably 0.05~0.2s.

[0036] After the above filtering process, the extracted fault bias estimate is obtained. It exhibits high stability and accuracy, providing a reliable numerical basis for subsequent feedforward compensation.

[0037] In step S104, in order to avoid the traditional PID integrator diverging under the huge dead torque generated by the fault, this embodiment uses the fault bias estimate extracted in step S103 and combines it with the soft-locked nonlinear decoupling model constructed in step S102 to perform algebraic inverse solution, thereby accurately obtaining the balance point of torque balance.

[0038] Specifically: First, set the pitch aerodynamic trim moment The specific calculations are as follows: +

[0039] High-frequency fluctuating fault residual small dynamic components The fundamental influence of engine thrust pitching moment is considered as a zero-mean disturbance and is therefore omitted. and pitch angular velocity damping term It is usually in a small, stable constant state. Based on this, to compensate for the fundamental effects of dynamic disturbance terms and engine thrust pitching moment, the following algebraic balancing equations are established: ; By algebraically solving the above trim equations, the static disturbance rejection feedforward trim command for the healthy side (left side) elevon can be obtained. The solution formula is: ; After receiving the feedforward trim command, As a feedforward compensation term, it is preferentially injected into the control channel of the left healthy control surface, which can quickly pull the UAV back to the pitch moment balance point at the moment the fault occurs, thereby effectively offsetting the asymmetric dead moment generated by the fault.

[0040] For the small dynamic disturbance Δ left by the faulty control surface Since the amplitude of (t) is small and the frequency is high, there is no need to design an additional control strategy. It can be passively absorbed by the inherent high-gain damping term in the UAV attitude inner loop.

[0041] Through the aforementioned static feedforward compensation, the UAV's pitch attitude was quickly restored to stability, providing a good precondition for subsequent heading correction.

[0042] In step S105, in order to achieve reliable fault-tolerant control, this embodiment performs high-frequency jitter heading reconstruction based on dynamic resource allocation. By actively injecting high-frequency jitter signals into the healthy control surface to induce aerodynamic coupling, yaw moment generation and heading correction are achieved without rudder. At the same time, an engineering bottom line of absolute priority for longitudinal altitude preservation is established to avoid altitude loss caused by control surface saturation.

[0043] The specific implementation process is as follows: First, a high-frequency sinusoidal jitter signal is actively injected into the control channel of the left-side healthy elevator aileron. The signal formula is: ; Where A is the amplitude. (0, ), The safe upper limit threshold for jitter signals (preferably 5°), angular frequency The parameters to be designed are selected based on the aerodynamic coupling characteristics of the UAV.

[0044] This high-frequency command drives the healthy control surfaces to produce a slight periodic deflection, breaking the static dead zone of the aircraft and inducing alternating roll motion. The tiny alternating roll angle projects the gravity vector laterally, forcibly inducing a sideslip angle. Relying on the inherent weathervane effect of the fuselage (i.e., the derivative of the yaw moment coefficient with respect to the sideslip angle) > The sideslip angle β is converted into a yaw control moment, thereby achieving heading correction under rudder-less conditions. By actively inducing lateral aerodynamic coupling through high-frequency jitter signals, and relying on the fuselage weathervane effect to generate a yaw moment, the asymmetric dead moment caused by the fault is effectively counteracted and the yaw moment is actively generated to complete the heading correction, thereby improving the survivability of the tailless flying wing UAV in fault conditions.

[0045] Secondly, to ensure that the course correction process does not jeopardize longitudinal flight safety, the remaining physical travel of the left healthy control surface is calculated in real time. To determine the physical deflection margin that a healthy control surface can use for course reconfiguration, the calculation formula is as follows: ; in, The physical limit deflection angle of the elevons is determined by the hardware performance of the UAV actuators. This is the longitudinal pitch stabilization command, a standard control command for the UAV's longitudinal pitch stabilization. It is output from the attitude inner loop and used to maintain flight altitude.

[0046] Then, based on the remaining physical journey A dynamic weighting coefficient σ is introduced to adjust the injection intensity of the jitter signal. It is generated using a piecewise function, and the specific value selection rules are as follows: ; Among them, the safety limit Strictly greater than the maximum amplitude A of the jitter signal (preferably 5°), safety lower limit Set according to the actuator hardware buffer limit (preferably 1°).

[0047] In step S106, based on the aforementioned steps, the feedforward trim command is further... Longitudinal pitch stabilization command The high-frequency sinusoidal jitter signal after being weighted by the dynamic weighting coefficient σ By superimposing these commands, a comprehensive control command for the left-side healthy control surface can be constructed. To ensure the physical feasibility of the command, a saturation limiting function `sat(` is introduced.)` is also introduced. Physical limiting is implemented to achieve a dynamic balance between heading correction and longitudinal altitude maintenance. The specific formula is as follows: ; Among them, the limiting range of the saturation limiting function is strictly set to the physical limit deflection angle range of the control surface. , As the absolute safety boundary of system control, the constraint is executed before the comprehensive control command is output to the bottom actuator, which can effectively prevent the actuator from being damaged by mechanical impact or stall due to receiving over-limit command.

[0048] After completing the construction of the comprehensive instructions, The actuators output to the left healthy control surface monitor the UAV's flight attitude (roll angle, pitch angle, yaw angle) and the remaining physical travel of the healthy control surface in real time, and dynamically adjust the weighting coefficients. and jitter signal The amplitude A achieves a dynamic balance between heading correction and longitudinal altitude maintenance. The entire closed-loop adjustment process continues until the UAV enters a stable and controllable flight state, thereby completing the fault-tolerant control task.

[0049] It is particularly important to emphasize that this method consistently prioritizes maintaining longitudinal height as the absolute engineering constraint. This applies when the remaining physical travel of the healthy control surface falls below a preset safety lower limit threshold. In such cases, the system will automatically reduce or even completely disable the heading reconstruction function (i.e., set σ=0) to fully ensure the longitudinal stability and flight safety of the UAV. All of the above operations do not require the addition of redundant control surfaces, backup actuators, or additional sensors, and can be directly integrated into the flight control system of existing tailless flying wing UAVs. It achieves efficient fault-tolerant control through pure algorithms, significantly reducing structural complexity and engineering modification costs.

[0050] The overall step flow of the fault-tolerant control method for tailless flying-wing UAVs addressing single aileron soft-lock faults of the present invention can be found in [link to relevant documentation]. Figure 2 Through steps S101-S106, this invention achieves active fault-tolerant control of a single-sided elevator soft jam fault without relying on redundant hardware, effectively offsetting the asymmetric dead moment generated by the fault, and actively generating a yaw moment to complete the heading correction, thereby significantly improving the flight survivability and safety of the tailless flying wing UAV in fault conditions.

[0051] Example 2 This embodiment provides a fault-tolerant control system for a tailless flying-wing UAV with a single aileron soft-jamming fault, including: The health state dynamics model module is configured to: establish a conventional virtual control surface dynamics model of the UAV in a healthy state, and determine the pitch moment equation, roll moment equation, and yaw moment equation; The fault diagnosis and offset estimation module is configured to: establish a soft jamming nonlinear decoupling model, decompose the actual physical deflection angle of the faulty control surface into the jamming center position offset and residual small dynamic components, and substitute them into the pitching moment equation to separate the constant interference source. The decoupled model is configured to: calculate the absolute position error and response residual of the faulty control surface, diagnose the fault when the joint boundary crossing condition is met, and extract the estimated value of the fault offset. The static compensation module is configured to: based on the fault bias estimate, use the soft-lock nonlinear decoupling model to perform inverse algebraic solution to obtain the feedforward trim command of the healthy side control surface, so as to counteract the constant interference source. The heading reconfiguration module is configured to: inject a high-frequency sinusoidal jitter signal into the healthy side control surface to induce a sideslip angle and generate a yaw moment using the wind vane effect for heading correction; at the same time, calculate the remaining physical travel of the healthy side control surface in real time and adjust the injection intensity of the high-frequency sinusoidal jitter signal according to the remaining physical travel. The command generation module is configured to generate a comprehensive control command for the healthy side control surface by combining the feedforward trim command, the longitudinal pitch stabilization command, and the adjusted high-frequency sinusoidal jitter signal.

[0052] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation process is the same, so it will not be repeated here.

[0053] Example 3 This embodiment also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the method of Embodiment 1.

[0054] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fault-tolerant control method for a tailless flying-wing UAV experiencing a single aileron soft-jamming fault, characterized in that, include: Establish a conventional virtual control surface dynamics model of the UAV under healthy conditions, and determine the pitch moment equation, roll moment equation, and yaw moment equation; A soft jamming nonlinear decoupling model is established, which decomposes the actual physical deflection angle of the faulty control surface into the jamming center position offset and the residual small dynamic component, and substitutes them into the pitching moment equation to separate the constant interference source. Calculate the absolute position error and response residual of the faulty control surface, diagnose the fault when the joint boundary crossing condition is met, and extract the estimated value of the fault offset. Based on the estimated fault bias, the soft-jamming nonlinear decoupling model is used to perform an algebraic inverse solution to obtain the feedforward trim command for the healthy side control surface, so as to counteract the constant interference source. A high-frequency sinusoidal jitter signal is injected into the healthy side control surface to induce sideslip angle and generate yaw moment using the wind vane effect for course correction. At the same time, the remaining physical travel of the healthy side control surface is calculated in real time, and the injection intensity of the high-frequency sinusoidal jitter signal is adjusted according to the remaining physical travel. By combining the feedforward trim command, the longitudinal pitch stabilization command, and the adjusted high-frequency sinusoidal jitter signal, a comprehensive control command for the healthy side control surface is generated.

2. The method according to claim 1, characterized in that, The aforementioned soft-jamming nonlinear decoupling model decomposes the actual physical deflection angle of the faulty control surface into the jamming center position offset and residual minute dynamic components, specifically: Establish independent physical elliptic deflection angles for left and right elliptic wings , With virtual total elevator deflection Virtual total aileron deflection Control allocation mapping relationship: ； The actual physical deflection angle of the faulty control surface is decomposed using the following formula: (t) = + Δ (t); in Δ is the offset of the center position of the aileron jamming. (t) represents the residual small dynamic component. This is the virtual total elevator deflection angle. Virtual total aileron deflection.

3. The method according to claim 1, characterized in that, The absolute position error is calculated by the difference between the commanded deflection angle and the actual feedback deflection angle of the faulty control surface, and the response residual is calculated by the difference between the expected roll rate and the actual roll rate; the joint boundary crossing condition is: the absolute position error is greater than the dynamic pressure adaptive position threshold, and the response residual is greater than the maneuver adaptive response threshold. The dynamic pressure adaptive position threshold is: ,in, For location tolerance parameters, To address the tolerance parameter, V represents the airspeed. This is the dynamic pressure adaptive position threshold coefficient; The specific threshold for the adaptive maneuver response is: ,in, For the desired roll rate, This represents the threshold coefficient for the adaptive response of the maneuver.

4. The method according to claim 1, characterized in that, When extracting the estimated value of the fault offset, a first-order low-pass filter is used to filter the actual feedback offset angle of the faulty control surface in order to filter out sensor measurement noise and mechanical vibration interference.

5. The method according to claim 1, characterized in that, The inverse algebraic solution of the soft-lock nonlinear decoupling model yields the feedforward trim command for the healthy side control surface, specifically: Establish the algebraic balancing equation: ； in, The zero-lift pitch moment coefficient, Let be the static stability derivative of the pitching moment with respect to the angle of attack. For the angle of attack of flight, For the pitch control derivatives of the left and right elevators, Fault bias estimate This is a static disturbance rejection feedforward trim command; The trim equations are solved algebraically to obtain the feedforward trim command for the healthy side elliptic. .

6. The method according to claim 1, characterized in that, The calculation formula for the high-frequency sinusoidal dithering signal is as follows: ； Where A is the amplitude. (0, ), ω represents the safe upper limit threshold for jitter signals, where ω is the angular frequency.

7. The method according to claim 1, characterized in that, The formula for calculating the remaining physical travel is: ； in, This refers to the physical limit deflection angle of the elevons. This is a pitch command. This is a feedforward balancing command.

8. The method according to claim 1, characterized in that, The comprehensive control command for the healthy side control surface is obtained by summing the feedforward trim command, the longitudinal pitch stabilization command, and the high-frequency sinusoidal jitter signal weighted by dynamic weighting coefficients, and then processing it through a saturation limiting function. The limiting range of the saturation limiting function is the physical limit deflection angle range of the control surface; the dynamic weighting coefficients... The rules for determining the value are as follows: ； in, This is the safe upper limit threshold for jitter signals. This is the safe lower limit threshold for jitter signals. For the remaining physical journey.

9. A fault-tolerant control system for a tailless flying-wing UAV experiencing a single aileron soft-jamming fault, characterized in that it includes: The health state dynamics model module is configured to: establish a conventional virtual control surface dynamics model of the UAV in a healthy state, and determine the pitch moment equation, roll moment equation, and yaw moment equation; The fault diagnosis and offset estimation module is configured to: establish a soft jamming nonlinear decoupling model, decompose the actual physical deflection angle of the faulty control surface into the jamming center position offset and residual small dynamic components, and substitute them into the pitching moment equation to separate the constant interference source. The decoupling model is configured to: calculate the absolute position error and response residual of the faulty control surface, diagnose the fault when the joint boundary crossing condition is met, and extract the estimated value of the fault offset. The static compensation module is configured to: based on the estimated fault bias, perform an algebraic inverse solution using the soft-lock nonlinear decoupling model to obtain the feedforward trim command for the healthy side control surface, so as to counteract the constant interference source. The heading reconstruction module is configured to: inject a high-frequency sinusoidal jitter signal into the healthy side control surface to induce a sideslip angle and generate a yaw moment using the wind vane effect for heading correction; at the same time, calculate the remaining physical travel of the healthy side control surface in real time and adjust the injection intensity of the high-frequency sinusoidal jitter signal according to the remaining physical travel. The instruction generation module is configured to generate a comprehensive control instruction for the healthy side control surface by combining the feedforward trim instruction, the longitudinal pitch stabilization instruction, and the adjusted high-frequency sinusoidal jitter signal.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the fault-tolerant control method for a tailless flying-wing UAV with a single aileron soft jamming fault as described in any one of claims 1-8.

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