Cooperative suppression of interference and imu spoofing drone fixed time control method and system

Abstract

The application discloses a kind of unmanned aerial vehicle fixed time control method and system of cooperative inhibition interference and IMU deception, the method includes: constructing explicit nonlinear attitude dynamics model and controlled model including IMU deception attack and external lumped disturbance;According to nonlinear attitude dynamics model, and introducing virtual intermediate variable, obtain feedforward compensation signal;Auxiliary function is introduced by piecewise definition, and according to controlled model, construct fixed-time sliding mode function;According to observation value and fixed-time sliding mode function, build total control law, ensure that the attitude tracking error of closed-loop system converges to stable state within the preset fixed time, and total convergence time upper bound depends on controller parameter.The application can effectively distinguish and inhibit interference and attack in the strong confrontation environment of unmanned aerial vehicle, realize the rapid recovery and stable control of flight attitude, so as to enhance the adaptability and task execution reliability of unmanned aerial vehicle in complex electromagnetic and acoustic interference environment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV fixed-time control method and a UAV fixed-time control system that collaboratively suppresses interference and IMU spoofing. Background Technology

[0002] In increasingly complex environments, drones not only need to cope with the physical disturbances caused by strong gusts of wind in severe weather, but are also exposed to the severe threat of various non-contact "soft-kill" weapons. In particular, acoustic deception attacks targeting the core navigation component—the IMU (Inertial Measurement Unit)—can use the principle of acoustic resonance to covertly inject false guidance information into the gyroscope, causing the drone to become unstable, deviate from its flight path, or even crash under controlled conditions without warning, severely damaging its mission success rate. Flight control systems in related technologies are mostly designed for physical wind disturbance resistance in conventional scenarios. When faced with complex conditions where "strong external physical disturbances" and "malicious deception by internal sensors" coexist, they often fail due to a lack of effective situational awareness and collaborative defense mechanisms. Therefore, how to improve the robustness of drones in extreme combat environments and achieve precise decoupling and rapid suppression of malicious deception and environmental interference has become a key bottleneck restricting the improvement of drone combat effectiveness.

[0003] Currently, anti-jamming control for UAVs mainly relies on active disturbance rejection control (ADRC), sliding mode control (SMU), and their improved methods. While extended state observers (IVOs) can enhance system robustness by estimating lumped disturbances, they essentially treat external disturbances and spoofed sensor signals as the same type of disturbance, failing to effectively distinguish and decouple them. Furthermore, IVOs typically assume that disturbances are smooth and gradually changing, making them prone to estimation lag when facing acoustic spoofing signals with fast time-varying and sudden characteristics, resulting in a lack of targeted compensation strategies. Although intermediate variable observers (IVOs) theoretically possess the ability to distinguish different types of disturbances, existing IVO designs are mostly designed for linear systems, making them difficult to directly apply to the dynamic models of quadrotor UAVs with strong coupling and highly nonlinear characteristics. Regarding control strategies, while SMU is widely used for disturbance rejection control, its exponential convergence characteristic leads to a slow system response. When an IMU spoofing attack causes a sudden and significant deviation in the UAV's attitude, the convergence time of SMU will be significantly prolonged, failing to meet the stringent safety requirements for rapid attitude recovery in emergency scenarios. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the objective of this invention is to propose a fixed-time control method and system for unmanned aerial vehicles (UAVs) that collaboratively suppresses interference and IMU spoofing, thereby enhancing the adaptability and mission execution reliability of UAVs in complex electromagnetic and acoustic interference environments.

[0005] To achieve the above objectives, according to the first aspect of this application, embodiments of the present invention propose a method for fixed-time control of unmanned aerial vehicles (UAVs) that coordinates the suppression of interference and IMU spoofing. The method includes:

[0006] Based on the mechanism of acoustic resonance on gyroscope, the frequency characteristics of IMU affected by resonance are obtained, and combined with the physical structure of UAV, an explicit nonlinear attitude dynamics model and a controlled model including IMU spoofing attack and external lumped disturbance are constructed.

[0007] Based on the nonlinear attitude dynamics model, and by introducing virtual intermediate variables, a feedforward compensation signal is obtained, wherein the feedforward compensation signal includes a consistent upper bound of the observed value and the observation error;

[0008] A piecewise defined auxiliary function is introduced, and a fixed-time sliding mode function is constructed based on the controlled model.

[0009] Based on the observed values ​​and the fixed-time sliding mode function, a total control law is constructed to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and the upper bound of the total convergence time depends on the controller parameters.

[0010] In addition, the UAV fixed-time control method for collaborative interference suppression and IMU spoofing in the embodiments of the present invention may also have the following additional technical features:

[0011] According to an embodiment of the present invention, the steps of constructing the nonlinear attitude dynamics model and the controlled model specifically include:

[0012] Quantization of IMU acoustic resonance deception attack signal;

[0013] Based on the physical structure of the UAV, a controlled matrix model is constructed;

[0014] The nonlinear attitude dynamics model is constructed based on the attack signal and the controlled matrix model.

[0015] According to an embodiment of the present invention, the step of quantizing the IMU acoustic resonance spoofing attack signal specifically includes:

[0016] Obtain the resonant frequency range corresponding to the IMU model of the UAV to clarify the characteristics of the effect of acoustic attacks on the gyroscope;

[0017] Based on the aforementioned characteristics, the original noise signal output by the IMU under acoustic resonance attack is defined as follows: ;

[0018] The original noise signal is filtered by the IMU's built-in filter, removing high-frequency components and retaining low-frequency components to form the attack signal: ;

[0019] in, They are rolling The original noise components of the pitch θ and yaw ψ axes. These are the low-frequency attack components of the drone on the three axes.

[0020] According to an embodiment of the present invention, the controlled matrix model is:

[0021] ;

[0022] in, Represents the attitude angle vector. , Represents the three-axis angular velocity vector. , , , These represent the angular velocities of the drone on the three axes; ;

[0023] , , This indicates the torque of the drone on its three axes. Represents the moment of inertia of the motor. Indicates the residual motor speed. , This represents the triaxial aerodynamic drag coefficient. , This represents the three-axis control input vector. These represent the control inputs of the drone on its three axes. , , This represents the physical perturbation vector. These represent the lumped disturbances on the three axes, respectively. This represents the three-axis angular velocity of the drone collected by the IMU.

[0024] According to an embodiment of the present invention, the nonlinear attitude dynamics model is as follows:

[0025] ;

[0026] in, , , , , , , This represents the actual output of the IMU after it has been deceived.

[0027] According to an embodiment of the present invention, the step of obtaining the feedforward compensation signal based on the nonlinear attitude dynamics model and by introducing virtual intermediate variables includes:

[0028] Based on the aforementioned nonlinear attitude dynamics model, intermediate variables are obtained through coordinate transformation:

[0029]

[0030] Based on the intermediate variables and the controlled matrix model, the dynamic equations of the intermediate variables are obtained as follows:

[0031] ;

[0032] Based on the aforementioned intermediate variable dynamic equation and the aforementioned nonlinear attitude dynamics model, an intermediate variable observer is designed:

[0033] ;

[0034] Based on the intermediate variable and the intermediate variable observer, construct the observation error dynamic equation:

[0035] ;

[0036] Based on the observation error dynamic equation, construct the first Lyapunov function:

[0037] ;

[0038] By applying the stability criterion using the first Lyapunov function, the upper bound of the convergence of the observation error is obtained:

[0039] ;

[0040] in, Represents the transformation coefficients. , , , , They are , , , , The observed values, This represents the designable observer gain matrix. , , , , The weighting coefficients and , It is a positive definite matrix. yes The smallest eigenvalue, and The convergence coefficients are given by... and Sure, This represents the uniform upper bound of the observation error.

[0041] According to an embodiment of the present invention, the step of introducing a piecewise defined auxiliary function and constructing a fixed-time sliding mode function based on the controlled model specifically includes:

[0042] Based on the controlled matrix model, the error dynamic equation is defined as follows:

[0043] ;

[0044] The auxiliary function is designed as follows:

[0045] ;

[0046] Based on the error dynamic equation and the auxiliary function, the fixed-time sliding mode function is constructed as follows:

[0047] ;

[0048] in, Represents the desired angular acceleration. , , Indicates adjustment The upper bound of the gain coefficient, , , .

[0049] According to an embodiment of the present invention, the step of constructing a total control law based on the observed values ​​and the fixed-time sliding mode function to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and the upper bound of the total convergence time depends on the controller parameters, specifically includes:

[0050] Constructing equivalent control laws :

[0051] ;

[0052] Constructing a fixed-time approach law :

[0053] ;

[0054] According to the equivalent control law and the fixed-time approach law Construct the overall control law :

[0055] ;

[0056] Based on the fixed-time sliding mode function, the upper time bound of the approaching segment is analyzed, and a second Lyapunov function is designed as follows: Differentiating it, we get:

[0057] ;

[0058] according to The convergence time is derived. :

[0059] ;

[0060] When the system state reaches the sliding surface, it satisfies ,Right now Design the third Lyapunov function as follows: Combining Take the derivative to obtain the upper bound of the steady-state time. ;

[0061] Obtain the upper bound of the total convergence time of the attitude tracking error of the closed-loop system: ;

[0062] in, , , , Indicates control gain. For design parameters and .

[0063] According to one embodiment of the present invention, the control gain The selection of [a] follows the following constraints:

[0064] ;

[0065] in, yes about The Lipschitz continuity constant.

[0066] According to a second aspect of this application, a drone fixed-time control system for collaboratively suppressing interference and IMU spoofing is provided. The drone fixed-time control system for collaboratively suppressing interference and IMU spoofing includes: a command end, an algorithm end, and a device end.

[0067] The command terminal includes a ground station and a control device. The ground station displays the specific operating status of the UAV in real time, and the control device is used to send the desired attitude control command according to the user's needs.

[0068] The algorithm terminal is connected to the command terminal and is used to execute the above-mentioned fixed-time control method for UAV that coordinates the suppression of interference and IMU spoofing according to the desired attitude control command, and output the execution control command.

[0069] The device end, corresponding to the quadcopter drone, is connected to the algorithm end and is used to receive the execution control command to complete fixed-time stabilization in an environment subject to physical layer disturbances and information layer attacks, thereby achieving coordinated suppression of IMU spoofing attacks and external interference.

[0070] The UAV fixed-time control method for synergistically suppressing interference and IMU spoofing of the present invention has the following beneficial effects:

[0071] (1) By explicitly establishing a nonlinear dynamic model that includes IMU acoustic spoofing attacks, the shortcomings of traditional UAV control models that usually only regard attacks as general noise or lumped disturbances are overcome. This model can accurately quantify the specific impact of time-varying acoustic spoofing signals on flight attitude, providing a reliable theoretical model basis for subsequent signal decoupling and compensation.

[0072] (2) The intermediate variable observer is adopted to effectively solve the problem of not being able to distinguish between external disturbances and internal sensor false deception signals in related technologies. The observer can jointly estimate and separate attack signals and external disturbances, which significantly improves the observation accuracy.

[0073] (3) The proposed fixed-time sliding mode control strategy theoretically ensures that the convergence time of the closed-loop system has a fixed upper bound that is independent of the initial state of the system. This means that even if an IMU spoofing attack causes a sudden and significant deviation in the attitude of the UAV, the system can still quickly recover to a stable state within a preset fixed time.

[0074] (4) By introducing auxiliary functions to construct sliding surfaces, the singularity problem and the exponential "boom" of control gain that are prone to occur in traditional fixed-time control algorithms when the error is large are effectively solved. This not only avoids system oscillation caused by actuator saturation, but also further improves the flight stability and mission execution reliability of UAVs in complex environments where interference and deception coexist. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 This is a flowchart illustrating a method for fixed-time control of unmanned aerial vehicles (UAVs) that coordinates interference suppression and IMU spoofing according to an embodiment of the present invention.

[0077] Figure 2 This is a schematic diagram of the structure of a fixed-time control system for unmanned aerial vehicles (UAVs) that coordinates interference suppression and IMU spoofing according to an embodiment of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0079] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0080] It should be understood that the invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0081] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0082] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0083] The following is a reference appendix. Figure 1 - Appendix Figure 2This invention describes a fixed-time control method and system for unmanned aerial vehicles (UAVs) that coordinates the suppression of interference and IMU spoofing, according to embodiments of the present invention.

[0084] Figure 1 This is a flowchart illustrating a method for fixed-time control of unmanned aerial vehicles (UAVs) in accordance with an embodiment of the present invention, which involves collaborative suppression of interference and IMU spoofing.

[0085] like Figure 1 As shown, the UAV fixed-time control method for collaboratively suppressing interference and IMU spoofing includes:

[0086] S1, based on the mechanism of acoustic resonance on gyroscope, obtains the frequency characteristics of IMU affected by resonance, and combines the physical structure of UAV to construct an explicit nonlinear attitude dynamics model and a controlled model that include IMU spoofing attacks and external lumped disturbances.

[0087] Specifically, based on the mechanism of acoustic resonance on the gyroscope, the frequency characteristics of the IMU affected by resonance can be obtained, and the acoustic deception signal can be introduced into the system equation as a sensor state deviation. Combined with the physical structure of the quadcopter UAV, a nonlinear attitude dynamics model that explicitly includes the IMU deception attack component and the external lumped disturbance component can be constructed.

[0088] S2. Based on the nonlinear attitude dynamics model and by introducing virtual intermediate variables, a feedforward compensation signal is obtained, wherein the feedforward compensation signal includes a consistent upper bound of the observed value and the observation error.

[0089] Specifically, the disturbance and attack are decoupled by introducing virtual intermediate variables; an observer is designed based on the intermediate variables, and the time-varying IMU spoofing signal and external disturbance are estimated in real time by observing the dynamic equations, so as to achieve accurate separation of multi-source interference.

[0090] S3, introduce a piecewise defined auxiliary function, and construct a fixed-time sliding mode function based on the controlled model.

[0091] Specifically, for the attitude tracking error of UAVs, a piecewise defined auxiliary function is introduced. This function adjusts the gain through a specific exponential and logarithmic mapping within different error amplitude ranges to eliminate the singularity problem near the equilibrium point and the problem of excessive gain when the error changes abruptly. A fixed-time sliding mode function is designed in conjunction with the auxiliary function.

[0092] S4. Based on the observed values ​​and the fixed-time sliding mode function, construct the overall control law to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and the upper bound of the overall convergence time depends on the controller parameters.

[0093] Specifically, by combining the sliding mode function to design a fixed-time approach rate, a final control law is constructed to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time. The upper bound of the convergence time depends only on the controller parameters and not on the initial state of the system or the instantaneous deviation caused by the attack.

[0094] The UAV fixed-time control method for synergistically suppressing interference and IMU spoofing of the present invention has the following beneficial effects:

[0095] (1) By explicitly establishing a nonlinear dynamic model that includes IMU acoustic spoofing attacks, the shortcomings of traditional UAV control models that usually only regard attacks as general noise or lumped disturbances are overcome. This model can accurately quantify the specific impact of time-varying acoustic spoofing signals on flight attitude, providing a reliable theoretical model basis for subsequent signal decoupling and compensation.

[0096] (2) The intermediate variable observer is adopted to effectively solve the problem of not being able to distinguish between external disturbances and internal sensor false deception signals in related technologies. The observer can jointly estimate and separate attack signals and external disturbances, which significantly improves the observation accuracy.

[0097] (3) The proposed fixed-time sliding mode control strategy theoretically ensures that the convergence time of the closed-loop system has a fixed upper bound that is independent of the initial state of the system. This means that even if an IMU spoofing attack causes a sudden and significant deviation in the attitude of the UAV, the system can still quickly recover to a stable state within a preset fixed time.

[0098] (4) By introducing auxiliary functions to construct sliding surfaces, the singularity problem and the exponential "boom" of control gain that are prone to occur in traditional fixed-time control algorithms when the error is large are effectively solved. This not only avoids system oscillation caused by actuator saturation, but also further improves the flight stability and mission execution reliability of UAVs in complex environments where interference and deception coexist.

[0099] In some embodiments, the steps of constructing the nonlinear attitude dynamics model and the controlled model specifically include:

[0100] S1.1, Quantize the IMU acoustic resonance spoofing attack signal.

[0101] Specifically, firstly, based on the IMU model of this UAV, the frequency range and form of resonance impact are obtained. When the IMU is subjected to a spoofing attack based on acoustic resonance, the noise signal output by the IMU is then filtered by the IMU's built-in filter. The high-frequency components of the noise signal are filtered out, while the low-frequency components are retained, forming the attack signal.

[0102] S1.2, Based on the physical structure of the UAV, construct a controlled matrix model.

[0103] Specifically, based on the IMU data received by the drone containing hidden deception attack components, the controlled model of the drone is constructed as follows:

[0104] ;

[0105] This can be further represented in matrix form as the controlled matrix model:

[0106] ;

[0107] in, These are the angular accelerations of the drone on the three axes. Represents the attitude angle vector. , Represents the three-axis angular velocity vector. , , , These represent the angular velocities of the drone on the three axes; ;

[0108] , , This indicates the torque of the drone on its three axes. Represents the moment of inertia of the motor. Indicates the residual motor speed. , This represents the triaxial aerodynamic drag coefficient. , This represents the three-axis control input vector. These represent the control inputs of the drone on its three axes. , , This represents the physical perturbation vector. These represent the lumped disturbances on the three axes, respectively. This represents the three-axis angular velocity of the drone collected by the IMU.

[0109] S1.3, Construct the nonlinear attitude dynamics model based on the attack signal and the controlled matrix model.

[0110] Specifically, based on the attitude model of the attack component and the implicit deception attack component, the nonlinear attitude dynamics model explicitly including the IMU deception attack and external perturbation is constructed in matrix form as follows:

[0111] ;

[0112] in, , , , , , , This represents the actual output of the IMU after it has been deceived.

[0113] In this embodiment, the physical characteristics of the IMU acoustic resonance deception attack are accurately quantified, and the attack component and the external disturbance component are explicitly separated in the model. This provides a reliable model basis that fits the real adversarial scenario for the subsequent decoupled observation of the attack and disturbance and precise compensation control.

[0114] In some embodiments, the step of quantizing the IMU acoustic resonance spoofing attack signal specifically includes:

[0115] S1.1.1 Obtain the resonance influence frequency range corresponding to the IMU model of the UAV and clarify the characteristics of the effect of acoustic attack on the gyroscope.

[0116] S1.1.2, Based on the aforementioned operational characteristics, the original noise signal output by the IMU under acoustic resonance attack is defined as follows: .

[0117] S1.1.3, the original noise signal is filtered using the filter built into the IMU, removing high-frequency components and retaining low-frequency components to form the attack signal: .

[0118] in, They are rolling The original noise components of the pitch θ and yaw ψ axes. These are the low-frequency attack components of the drone on the three axes.

[0119] In this embodiment, based on the true resonance characteristics and built-in filtering characteristics of the IMU, the effective acoustic deception attack signal that can be actually injected into the flight control system is accurately quantified, providing accurate attack term inputs and physical basis that fit the engineering reality for subsequent explicit attack modeling, decoupling observation and compensation control.

[0120] In highly contested environments, the operation of UAVs must first ensure that they can decouple attacks and disturbances and conduct coordinated observations to eliminate the impact of external interference. Traditional observation algorithms, when the IMU is attacked and data becomes inaccurate, couple the disturbance and attack components together, leading to observation failure.

[0121] In some embodiments, the step of obtaining the feedforward compensation signal based on the nonlinear attitude dynamics model and by introducing virtual intermediate variables includes:

[0122] S2.1, Based on the aforementioned nonlinear attitude dynamics model, intermediate variables are obtained through coordinate transformation:

[0123] ;

[0124] S2.2, Based on the intermediate variables and the controlled matrix model, the dynamic equation of the intermediate variables is obtained:

[0125] ;

[0126] S2.3, Based on the intermediate variable dynamic equation and the nonlinear attitude dynamics model, design the intermediate variable observer:

[0127] ;

[0128] S2.4, Based on the intermediate variable and the intermediate variable observer, construct the dynamic equation for the observation error:

[0129] ;

[0130] S2.5, Based on the observed error dynamic equation, construct the first Lyapunov function:

[0131] ;

[0132] It is worth mentioning that, in order to simultaneously ensure the observation effect of both disturbance and attack, it is necessary to make , Simultaneous convergence is achieved, and for this purpose, the first Lyapunov function was designed.

[0133] S2.6, Using the first Lyapunov function as the stability criterion, the upper bound of the convergence of the observation error is obtained:

[0134] ;

[0135] in, Represents the transformation coefficients. , , , , They are , , , , The observed values, This represents the designable observer gain matrix. , , , , The weighting coefficients and , It is a positive definite matrix. yes The smallest eigenvalue, and The convergence coefficients are given by... and Sure, This represents the uniform upper bound of the observation error.

[0136] In this embodiment, an intermediate variable is used to decouple disturbances and attacks, and an intermediate variable observer is designed based on this to collaboratively observe disturbances and IMU spoofing attacks. Subsequently, a fixed-time sliding mode control algorithm is introduced to enable the UAV to more efficiently cope with working environments where attacks and interference coexist. Therefore, this invention employs a control strategy combining an intermediate variable observer and a fixed-time sliding mode control algorithm, improving the UAV's ability to cope with attacks and interference.

[0137] Building upon the above steps that enable the observation of disturbances and IMU spoofing attacks, a fixed-time sliding mode control algorithm is further combined to achieve coordinated suppression and rapid response of the quadcopter UAV against disturbances and attacks. The key to rapid response lies in designing a fixed-time sliding surface to ensure that the state stabilizes within a fixed time after reaching the sliding surface. A sufficiently fast approach velocity is also required during the process of reaching the sliding surface; therefore, a fixed-time approach rate is also designed to guarantee the approach velocity. In the stability analysis of sliding mode control, Lyapunov analysis is typically used, and the same method can be applied to the analysis of time performance.

[0138] Based on the above analysis, this invention first designs a fixed-time sliding mode function based on an auxiliary function, and further designs a fixed-time approach rate based on the analysis of observation errors. The synergistic effect of the fixed-time approach rate and the fixed-time sliding mode function ensures that the UAV can track the desired state within a fixed time.

[0139] Specifically, the step of introducing a piecewise defined auxiliary function and constructing a fixed-time sliding mode function based on the controlled model includes:

[0140] It should be noted that a controlled matrix model is used in the design of the control algorithm. Combined with the desired state, a state error equation is designed for the control algorithm. Considering the symmetrical structure of the UAV, this invention focuses on the control algorithm design along the φ axis; the control algorithms for the other two axes are the same as those for the φ axis.

[0141] S3.1, Based on the controlled matrix model, define the error dynamic equation:

[0142] ;

[0143] S3.2, Considering that errors can cause singularities in the sliding mode function, this invention designs an auxiliary function before designing the sliding mode function:

[0144] ;

[0145] S3.3, Based on the error dynamic equation and the auxiliary function, construct the fixed-time sliding mode function:

[0146] ;

[0147] in, Represents the desired angular acceleration. , , Indicates adjustment The upper bound of the gain coefficient, , , .

[0148] In this embodiment, by designing a piecewise continuous auxiliary function that adapts to the entire range of attitude error, and constructing a fixed-time sliding mode function in combination with the error dynamic equation, the fixed-time convergence characteristic of sliding mode motion that does not depend on the initial state is guaranteed, thereby improving the robustness of the control algorithm and its feasibility for engineering implementation.

[0149] After completing the fixed-time sliding mode function, it is only necessary to drive the state variables onto the sliding surface to ensure the fixed-time performance of the subsequent stable section. Therefore, it is necessary to design a fixed-time approach rate to ensure that the state variables can reach the sliding surface within a fixed time.

[0150] In some embodiments, the step of constructing a total control law based on the observed values ​​and the fixed-time sliding mode function to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and the upper bound of the total convergence time depends on the controller parameters, specifically includes:

[0151] S4.1 In the control algorithm, an equivalent control law is needed to offset the system state, which can be used to construct an equivalent control law. :

[0152] ;

[0153] S4.2, by designing a fixed-time reaching law, a fixed-time reaching law can be constructed. :

[0154] ;

[0155] S4.3, according to the equivalent control law and the fixed-time approach law Construct the overall control law :

[0156] ;

[0157] It should be noted that the overall control law The final controller designed for this purpose.

[0158] In addition, after completing the controller design, it is necessary to analyze the fixed-time performance of the entire system and give the upper bound of the stable time when the UAV is attacked and disturbed to guide the UAV in performing its mission.

[0159] S4.4, Based on the fixed-time sliding mode function, analyze the upper time bound of the approaching segment, and design the second Lyapunov function as follows: Differentiating it, we get:

[0160] ;

[0161] according to The convergence time is derived. :

[0162] ;

[0163] S4.5, when the system state reaches the sliding surface, satisfies ,Right now Design the third Lyapunov function as follows: Combining Take the derivative to obtain the upper bound of the steady-state time. This is used to prevent the controller from developing an infinite gain problem.

[0164] S4.6, Obtain the upper bound of the total convergence time of the closed-loop system attitude tracking error: ;

[0165] in, , , , Indicates control gain. For design parameters and .

[0166] In this embodiment, by constructing a total control law and quantifying it through phased Lyapunov stability analysis, the upper bound of the total convergence time, which is determined solely by the controller parameters and does not depend on the initial state of the system or the amplitude of the attack disturbance, is obtained. This not only achieves precise compensation and suppression of IMU spoofing attacks and external interference, but also theoretically ensures that the UAV attitude converges stably within a preset fixed time, thereby improving the robustness and speed of the flight control system in a highly adversarial environment.

[0167] In some embodiments, the control gain The selection of [a] follows the following constraints:

[0168] ;

[0169] in, yes about The Lipschitz continuity constant.

[0170] In summary, this invention, through a comprehensive approach of explicit modeling of IMU spoofing attacks, design of intermediate variable observers, and fixed-time sliding mode control, enables quadcopter UAVs to achieve precise decoupling of interference signals and rapid recovery of flight attitude within a fixed time in complex adversarial environments where spoofing attacks and external disturbances coexist. This effectively solves the problem of uncontrollable recovery time under sudden strong attacks in traditional control systems, and has significant strategic importance and application value in various application scenarios.

[0171] In addition, this application also provides a fixed-time control system for unmanned aerial vehicles that collaboratively suppresses interference and IMU spoofing, such as... Figure 2 As shown, the UAV fixed-time control system for collaboratively suppressing interference and IMU spoofing includes: a command terminal 10, an algorithm terminal 20, and a device terminal 30.

[0172] Command terminal 10 includes a ground station and control equipment. The ground station displays the specific operating status of the UAV in real time, and the control equipment is used to send desired attitude control commands according to user needs.

[0173] The algorithm terminal 20 is connected to the command terminal 10 and is used to execute the above-mentioned fixed-time control method for UAV that coordinates the suppression of interference and IMU spoofing according to the desired attitude control command, and output the execution control command.

[0174] The device end 30 corresponds to the quadcopter drone and is connected to the algorithm end 20. It is used to receive the execution control command and complete fixed-time stabilization in an environment subject to physical layer disturbances and information layer attacks, thereby achieving coordinated suppression of IMU spoofing attacks and external interference.

[0175] The UAV fixed-time control system of the present invention, which coordinates the suppression of interference and IMU spoofing, can effectively distinguish and suppress interference and attacks in a highly contested environment, and achieve rapid recovery and stable control of flight attitude, thereby enhancing the adaptability and mission reliability of the UAV in complex electromagnetic and acoustic interference environments.

[0176] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0177] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0179] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0180] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, the structure of this application should not be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0181] Those skilled in the art will understand that, apart from the mutual exclusion of features, any combination of all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any device so disclosed may be employed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0182] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0183] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application.

[0184] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0185] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for fixed-time control of unmanned aerial vehicles (UAVs) to collaboratively suppress interference and IMU spoofing, characterized in that, The method includes: Based on the mechanism of acoustic resonance affecting gyroscopes, the frequency characteristics of the IMU affected by resonance are obtained. Combined with the physical structure of the UAV, an explicit nonlinear attitude dynamics model and a controlled model are constructed, incorporating IMU spoofing attacks and external lumped perturbations. Specifically, these include: Quantization of IMU acoustic resonance deception attack signals; Based on the physical structure of the UAV, a controlled matrix model is constructed; Based on the attack signal and the controlled matrix model, the nonlinear attitude dynamics model is constructed; wherein, the step of quantizing the IMU acoustic resonance spoofing attack signal specifically includes: Obtain the resonant frequency range corresponding to the IMU model of the UAV to clarify the characteristics of the effect of acoustic attacks on the gyroscope; Based on the aforementioned characteristics, the original noise signal output by the IMU under acoustic resonance attack is defined as follows: ; The original noise signal is filtered by the IMU's built-in filter, removing high-frequency components and retaining low-frequency components to form the attack signal: ; in, They are rolling The original noise components of the pitch θ and yaw ψ axes. These are the low-frequency attack components of the drone on the three axes; Based on the nonlinear attitude dynamics model, and by introducing virtual intermediate variables, a feedforward compensation signal is obtained, wherein the feedforward compensation signal includes a consistent upper bound of the observed value and the observation error; A piecewise defined auxiliary function is introduced, and a fixed-time sliding mode function is constructed based on the controlled model. Based on the observed values ​​and the fixed-time sliding mode function, a total control law is constructed to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and the upper bound of the total convergence time depends on the controller parameters. The controlled matrix model is as follows: in, Represents the attitude angle vector. , Represents the three-axis angular velocity vector. , , , These represent the angular velocities of the drone on the three axes; , , , This indicates the torque of the drone on its three axes. Represents the moment of inertia of the motor. Indicates the residual motor speed. , This represents the triaxial aerodynamic drag coefficient. , This represents the three-axis control input vector. These represent the control inputs of the drone on its three axes. , , This represents the physical perturbation vector. These represent the lumped disturbances on the three axes, respectively. This represents the three-axis angular velocity of the drone collected by the IMU; The nonlinear attitude dynamics model is as follows: in, , , , , , , This represents the actual output of the IMU after it was deceived; The step of obtaining the feedforward compensation signal based on the nonlinear attitude dynamics model and by introducing virtual intermediate variables includes: Based on the aforementioned nonlinear attitude dynamics model, intermediate variables are obtained through coordinate transformation: Based on the intermediate variables and the controlled matrix model, the dynamic equations of the intermediate variables are obtained as follows: Based on the aforementioned intermediate variable dynamic equation and the aforementioned nonlinear attitude dynamics model, an intermediate variable observer is designed: Based on the intermediate variable and the intermediate variable observer, construct the observation error dynamic equation: Based on the observation error dynamic equation, construct the first Lyapunov function: By applying the stability criterion using the first Lyapunov function, the upper bound of the convergence of the observation error is obtained: in, Represents the transformation coefficients. , , , , They are , , , , The observed values, This represents the designable observer gain matrix. , , , , The weighting coefficients and , It is a positive definite matrix. yes The smallest eigenvalue, and The convergence coefficients are given by... and Sure, This represents the uniform upper bound of the observation error.

2. The UAV fixed-time control method for collaboratively suppressing interference and IMU spoofing according to claim 1, characterized in that, The steps of introducing a piecewise defined auxiliary function and constructing a fixed-time sliding mode function based on the controlled model specifically include: Based on the controlled matrix model, the error dynamic equation is defined as follows: The auxiliary function is designed as follows: Based on the error dynamic equation and the auxiliary function, the fixed-time sliding mode function is constructed as follows: in, Represents the desired angular acceleration. , , Indicates adjustment The upper bound of the gain coefficient, , , .

3. The UAV fixed-time control method for collaboratively suppressing interference and IMU spoofing according to claim 2, characterized in that, The step of constructing a total control law based on the observed values ​​and the fixed-time sliding mode function to ensure that the attitude tracking error of the closed-loop system converges to a stable state within a preset fixed time, and that the upper bound of the total convergence time depends on the controller parameters, specifically includes: Constructing equivalent control laws : Constructing a fixed-time approach law : According to the equivalent control law and the fixed-time approach law Construct the overall control law : Based on the fixed-time sliding mode function, the upper time bound of the approaching segment is analyzed, and a second Lyapunov function is designed as follows: Differentiating it, we get: according to The convergence time is derived. : When the system state reaches the sliding surface, it satisfies ,Right now Design the third Lyapunov function as follows: Combining Take the derivative to obtain the upper bound of the steady-state time. ; Obtain the upper bound of the total convergence time of the attitude tracking error of the closed-loop system: ; in, , , , Indicates control gain. For design parameters and .

4. The UAV fixed-time control method for collaboratively suppressing interference and IMU spoofing according to claim 3, characterized in that, The control gain The selection of [a] follows the following constraints: in, yes about The Lipschitz continuity constant.

5. A fixed-time control system for unmanned aerial vehicles (UAVs) that collaboratively suppresses interference and IMU spoofing, characterized in that, The system includes: a command terminal, an algorithm terminal, and a device terminal; The command terminal includes a ground station and a control device. The ground station displays the specific operating status of the UAV in real time, and the control device is used to send the desired attitude control command according to the user's needs. The algorithm terminal is connected to the command terminal and is used to execute the UAV fixed-time control method for cooperative suppression of interference and IMU spoofing according to any one of claims 1-4, and output the execution control command according to the desired attitude control command. The device end, corresponding to the quadcopter drone, is connected to the algorithm end and is used to receive the execution control command to complete fixed-time stabilization in an environment subject to physical layer disturbances and information layer attacks, thereby achieving coordinated suppression of IMU spoofing attacks and external interference.

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