An adaptive fault-tolerant control method, device and equipment for a quadrotor unmanned aerial vehicle based on unknown time-varying rotor faults and a medium

Abstract

The application discloses a quadrotor unmanned aerial vehicle adaptive fault-tolerant control method and device based on unknown time-varying rotor faults, equipment and medium. The method comprises the following steps: modeling unknown time-varying rotor faults by using a linear parameterization structure to obtain a rotor fault model of a quadrotor unmanned aerial vehicle; constructing a nominal control law containing a fault compensation signal according to the rotor fault model; constructing an adaptive control law and an adaptive control law parameter updating mechanism according to the nominal control law to construct a quadrotor unmanned aerial vehicle adaptive fault-tolerant controller; and performing adaptive fault-tolerant control on the quadrotor unmanned aerial vehicle by using the quadrotor unmanned aerial vehicle adaptive fault-tolerant controller. The rotor fault model is constructed for unknown time-varying faults of a single rotor, unified modeling of various rotor fault forms is realized, and the problem that existing methods cannot adapt to unknown time-varying faults is solved. Meanwhile, the adaptive control law parameter updating mechanism constructed by the application takes fault dynamic information as the driving force, can update fault compensation parameters online and adaptively, does not need to know the specific form and variation law of the fault in advance, can guarantee the stability and expected tracking performance of the system when the quadrotor unmanned aerial vehicle encounters unknown time-varying rotor faults, and improves the safety and reliability of quadrotor unmanned aerial vehicle flight.

Description

Technical Field

[0001] This application relates to the field of aircraft control technology, and in particular to an adaptive fault-tolerant control method, device, equipment and medium for quadrotor unmanned aerial vehicles based on unknown time-varying rotor faults. Background Technology

[0002] Quadrotor drones are widely used in both military and civilian fields due to their simple structure, ease of operation, low cost, and ability to take off and land vertically and hover. Examples include military reconnaissance, precision strikes, air pollution detection, urban traffic monitoring, power line inspection, agricultural plant protection, early fire detection, and aerial search and rescue. For quadrotor drone systems, preventing personnel injury and property damage caused by flight loss of control is the primary safety objective. Among various failure modes, rotor failure (such as partial thrust loss, complete failure, or time-varying performance degradation) directly affects the lift and control torque generated by the quadrotor drone, making it one of the most serious threats to system stability and maneuverability.

[0003] Existing control methods for rotor faults are primarily designed for specific types of rotor faults, such as fixed-proportional thrust loss, step faults, or complete failures. They struggle to achieve unified modeling and effective compensation for various fault types, including step faults, gradual faults, and continuously time-varying faults. When the fault type is complex or its severity varies over time, the control performance and fault tolerance of existing methods are often limited, failing to guarantee stable flight for quadcopter UAVs when encountering unknown time-varying rotor faults.

[0004] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide an adaptive fault-tolerant control method, device, equipment and medium for quadrotor UAVs based on unknown time-varying rotor faults, in order to address the shortcomings of the existing technology.

[0006] To address the aforementioned technical problems, the first aspect of this application provides an adaptive fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) based on an unknown time-varying rotor fault, wherein the method includes: A linear parameterized structure is used to model unknown time-varying rotor faults to obtain a rotor fault model for a quadcopter UAV. Based on the rotor fault model, a nominal control law containing fault compensation signals is constructed; An adaptive control law and an adaptive control law parameter update mechanism are constructed based on the nominal control law to build an adaptive fault-tolerant controller for a quadcopter UAV. The adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the adaptive control law parameter update mechanism includes the fault dynamic information of the rotor fault model. The quadcopter drone is adaptively fault-tolerant controlled using the aforementioned quadcopter drone adaptive fault-tolerant controller.

[0007] The adaptive fault-tolerant control method for a quadcopter UAV based on unknown time-varying rotor faults, wherein the rotor fault model of the quadcopter UAV is represented as: , in, This represents a known time-varying regression basis function. Represents an unknown constant parameter. This indicates the number of time-varying regression basis functions.

[0008] The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault, wherein the construction of a nominal control law containing a fault compensation signal vector based on the rotor fault model specifically includes: The rotor fault model is embedded into the input vector of the quadcopter UAV, and the input vector of the quadcopter UAV is converted into an input vector description that includes the rotor fault model and the nominal control law. Based on the input vector form, a nominal control law is constructed according to the MRAC framework, wherein the driving signal of the nominal control law includes a state vector, a bounded reference input signal, and a fault compensation signal. The input vector is described as follows: , , in, Represents the control input vector. Represents the input matrix, This represents an adaptive control law. This represents the fault disturbance vector. This represents the fault indication matrix.

[0009] The aforementioned adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault, wherein the nominal control law is: , , , , in, This represents the nominal law of control. Represents the theoretical state gain vector. This represents the theoretical state gain. Represents the theoretical feedforward gain vector. This represents the theoretical feedforward gain. Represents the fault compensation signal vector. This indicates the fault compensation signals for the four rotors of a quadcopter drone. This indicates a bounded reference input signal. This represents the state vector.

[0010] The matching condition for the nominal control law is: , , , in, Represents the state matrix, Represents the identity matrix. Represents the reference state matrix. Represents the input matrix The List, Represents the input matrix The List, Indicates the first A rotor fault signal.

[0011] The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault, wherein the adaptive control law is: , , , , , in, This represents an adaptive control law. This represents the fault-tolerant state gain vector. This represents the gain in the fault-tolerant state. This represents the fault-tolerant feedforward gain vector. This represents the fault-tolerant feedforward gain. This represents the fault-tolerant fault compensation signal vector. This represents the fault-tolerant compensation signal for the four rotors of a quadcopter drone. This represents a known time-varying regression basis function. This indicates the number of time-varying regression basis functions. This represents the fault-tolerant fault compensation signal parameter. This indicates a bounded reference input signal. This represents the state vector.

[0012] The adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults, wherein the adaptive control law parameter update mechanism is as follows:

[0013]

[0014] , in, The derivative of the fault-tolerant state gain. The derivative of the fault-tolerant feedforward gain. The derivative of the fault-tolerant fault compensation signal parameter. Represents a symbolic function. Represents the first element of the reference input matrix. List, , , , Both represent adaptive gain. This indicates the number of time-varying regression basis functions. This represents a known time-varying regression basis function. This represents the tracking error determined based on the reference state vector and the state vector. This indicates a bounded reference input signal. Represents the state vector. Let be a positive definite matrix such that for any given positive definite matrix , . satisfy .

[0015] The adaptive fault-tolerant control method for a quadrotor UAV based on unknown time-varying rotor faults, wherein the adaptive fault-tolerant control of the quadrotor UAV using the adaptive fault-tolerant controller specifically includes: Receive the bounded reference input signal sent by the host computer and determine the reference state vector using the reference model of the quadcopter UAV; The tracking error is determined based on the reference state vector and the current state vector of the quadcopter UAV. Based on the tracking error, the adaptive control law is determined by calling the quadcopter UAV adaptive fault-tolerant controller. The adaptive control law is applied to the quadcopter drone to perform adaptive fault-tolerant control on the quadcopter drone.

[0016] A second aspect of this application provides an adaptive fault-tolerant control device for a quadcopter unmanned aerial vehicle (UAV) based on an unknown time-varying rotor fault, wherein the device comprises: The rotor fault modeling module is used to model unknown time-varying rotor faults using a linear parameterized structure to obtain the rotor fault model of the quadcopter UAV. The nominal control law construction module is used to construct a nominal control law containing fault compensation signals based on the rotor fault model. The fault-tolerant controller construction module is used to construct an adaptive control law and an adaptive control law parameter update mechanism based on the nominal control law, so as to construct an adaptive fault-tolerant controller for a quadcopter UAV. The adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the adaptive control law parameter update mechanism includes the fault dynamic information of the rotor fault model. The control module is used to perform adaptive fault-tolerant control of the quadcopter using the quadcopter's adaptive fault-tolerant controller.

[0017] A third aspect of this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the adaptive fault-tolerant control method for quadrotor unmanned aerial vehicles based on unknown time-varying rotor faults as described above.

[0018] A fourth aspect of this application provides a terminal device, which includes: a processor and a memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults as described above.

[0019] Beneficial effects: 1. This application is a rotor fault model constructed for unknown time-varying faults of a single rotor, realizing unified modeling of multiple rotor fault forms and solving the problem that existing methods cannot adapt to unknown time-varying faults.

[0020] 2. The adaptive control law parameter update mechanism constructed in this application is driven by fault dynamic information and can adaptively update fault compensation parameters online without knowing the specific form and change law of the fault in advance. It can still ensure the stability of the system and the expected tracking performance when the quadcopter UAV encounters an unknown time-varying rotor fault, thereby improving the safety and reliability of the quadcopter UAV flight.

[0021] 3. The method provided in this application embodiment has been evaluated in real quadcopter drone flight and can handle any single rotor fault, including step changes and continuous thrust changes, and exhibits consistent stability and high-precision attitude tracking.

[0022] 4. The adaptive control law in this application operates independently of explicit fault detection and does not require precise control effectiveness matrix information, ensuring robustness to diagnostic delays, modeling uncertainties, and actuator performance changes, while also being easy to adapt to other multi-rotor platforms. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of an adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault, provided in an embodiment of this application.

[0025] Figure 2 The diagram illustrates the definition of rotor sequence and direction of rotation, and the airframe following the right-hand rule.

[0026] Figure 3 This is a schematic diagram of the quadcopter drone used in the experiment.

[0027] Figure 4 The attitude angle tracking response diagram of a quadcopter UAV under continuous failure.

[0028] Figure 5 The attitude angle tracking response diagram of a quadcopter UAV under a step fault.

[0029] Figure 6 A schematic diagram of the principle of an adaptive fault-tolerant control device for a quadrotor UAV based on an unknown time-varying rotor fault, provided in an embodiment of this application.

[0030] Figure 7 A schematic block diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0031] This application provides an adaptive fault-tolerant control method, apparatus, device, and medium for quadrotor unmanned aerial vehicles (UAVs) based on unknown time-varying rotor faults. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description, with reference to the accompanying drawings and embodiments, further illustrates this application. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0033] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0034] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0035] Research has shown that quadcopter drones have wide applications in both military and civilian fields due to their simple structure, ease of operation, low cost, and ability to achieve vertical takeoff and landing and hovering. Examples include military reconnaissance, precision strikes, air pollution detection, urban traffic monitoring, power line inspection, agricultural plant protection, early fire detection, and aerial search and rescue. For quadcopter drone systems, preventing personnel injury and property damage caused by flight loss of control is the primary safety objective. Among various failure modes, rotor failure (such as partial thrust loss, complete failure, or time-varying performance degradation) is one of the most serious threats to system stability and maneuverability because it directly affects the lift and control torque generated by the quadcopter drone.

[0036] Existing control methods for rotor faults are primarily designed for specific types of rotor faults, such as fixed-proportional thrust loss, step faults, or complete failures. They struggle to achieve unified modeling and effective compensation for various fault types, including step faults, gradual faults, and continuously time-varying faults. When the fault type is complex or its severity varies over time, the control performance and fault tolerance of existing methods are often limited, failing to guarantee stable flight for quadcopter UAVs when encountering unknown time-varying rotor faults.

[0037] To address the aforementioned issues, this application employs a linear parameterized structure to model unknown time-varying rotor faults, thereby obtaining a rotor fault model for a quadcopter UAV. A nominal control law incorporating fault compensation signals is constructed based on this rotor fault model. An adaptive control law and an adaptive control law parameter update mechanism are then built based on the nominal control law to construct an adaptive fault-tolerant controller for the quadcopter UAV. This adaptive fault-tolerant controller is then used to perform adaptive fault-tolerant control on the quadcopter UAV. This application constructs a rotor fault model for a single unknown time-varying rotor fault, achieving unified modeling for multiple rotor fault types and solving the problem that existing methods cannot adapt to unknown time-varying faults. Furthermore, the adaptive control law parameter update mechanism constructed in this application is driven by dynamic fault information, enabling online adaptive updates of fault compensation parameters without prior knowledge of the specific form and variation patterns of the fault. This ensures system stability and desired tracking performance even when the quadcopter UAV encounters an unknown time-varying rotor fault, improving the safety and reliability of quadcopter UAV flight.

[0038] The application content will be further explained below with reference to the accompanying drawings and the description of the embodiments.

[0039] This embodiment provides an adaptive fault-tolerant control method for a quadrotor unmanned aerial vehicle (UAV) based on an unknown time-varying rotor fault, such as... Figure 1 As shown, the adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults specifically includes: S10. Model the unknown time-varying rotor fault using a linear parameterized structure to obtain the rotor fault model of the quadcopter UAV.

[0040] It is understandable that in the fault-tolerant control of a quadcopter drone, rotor failure will disrupt the thrust distribution, directly leading to torque imbalance and attitude instability. Attitude essentially reflects the spatial orientation of the quadcopter drone, typically described by Euler angles; therefore, attitude control is mathematically equivalent to angle adjustment. In this embodiment, the origin of the body coordinate system coincides with the center of mass of the quadcopter drone, and both the body coordinate system and the Earth coordinate system are right-handed coordinate systems. The angular rate of the quadcopter drone is expressed as: , , , , in, This represents the angular velocity vector within the body frame. This represents the torque vector exerted by the rotor on the fuselage. The inertial matrix of a quadcopter drone is represented. Indicates the roll rate. Indicates pitch rate, Indicates the yaw rate. express The rolling torque of the shaft, express The pitching moment of the shaft, express Yaw moment of the shaft, express Moment of inertia of the shaft express Moment of inertia of the shaft express Moment of inertia of the shaft.

[0041] Under the yaw-pitch-roll (ZYX) rotation sequence, the relationship between the Euler angular rate and the fuselage frame angular velocity can be expressed as: , in, This indicates the roll angle of a quadcopter drone. This indicates the pitch angle of a quadcopter drone. This indicates the yaw angle of a quadcopter drone. The sine function representing the roll angle. The cosine function representing the roll angle. The tangent function representing the pitch angle. The cosine function representing the pitch angle.

[0042] In this embodiment of the application, taking the quadcopter drone maintaining a relatively small Euler angle (0~0.17 radians) during flight as an example (of course, in practical applications, quadcopter drones can also maintain other Euler angles during flight, all of which are applicable to the method provided in this embodiment of the application; this is a specific limitation), it is possible to: .

[0043] Correspondingly, the relationship between the Euler angular rate and the fuselage frame angular velocity can be rewritten as: , For quadcopter drones, from the torque vector To rotor thrust vector The transformation is described as follows: , in, It is to satisfy The rotor thrust vector, This indicates the maximum thrust of the rotor. This indicates the arm length of a quadcopter drone. This represents the rotor's torque coefficient. In the machine system The angle between the shaft and the No. 1 rotor arm (e.g.) Figure 2 (as shown) Indicates the included angle The sine function, Indicates the included angle The cosine function. In the embodiments of this application, This indicates that the quadcopter UAV studied adopts " "Configuration".

[0044] In summary, the quadcopter drone model can be represented as: , , , , , in, Represents the state vector. Represents the state matrix, Represents the input matrix, This represents the control input vector.

[0045] Furthermore, in this quadcopter UAV system, the uncertainty caused by rotor faults can be represented by a linear parameterized form. Therefore, the unknown time-varying rotor fault can be modeled using a linear parameterized structure to obtain the rotor fault model of the quadcopter UAV. The rotor fault model of the quadcopter UAV is expressed as follows: , in, This represents a known time-varying regression basis function. Represents an unknown constant parameter. This indicates the number of time-varying regression basis functions.

[0046] Control input vector It can be achieved through unknown constant parameters. and known time-varying regression basis functions Parameterization is performed to approximate actual time-varying faults, which may include actuator efficiency degradation, deviations, or even complete failure. In this embodiment, the parameterization is achieved by setting... A known time-varying regression basis function This can improve the approximation accuracy.

[0047] This application embodiment utilizes a linear parameterized structure to model unknown time-varying rotor faults, resulting in a rotor fault model represented as follows: A known time-varying regression basis function The linear combination of these parameters allows rotor fault modeling to encompass both step faults and continuous time-varying faults, including various fault forms such as complete failure, fixed-proportion thrust loss, and gradual degradation. This achieves a unified description of multiple different fault forms, eliminating the need to design separate models for different fault types, reducing modeling complexity, and adapting to fault scenarios with unknown changing patterns. Furthermore, this rotor fault model enables the subsequent adaptive fault-tolerant controller for quadcopter UAVs built upon it to maintain control performance and fault tolerance even when facing complex fault forms or fault severity that varies over time, ensuring stable flight of the quadcopter UAV even when encountering unknown time-varying rotor faults.

[0048] S20. Construct a nominal control law containing fault compensation signals based on the rotor fault model.

[0049] Specifically, after constructing the rotor fault model, it can be integrated into the control input vector. The control input vector is described using the rotor fault model and the nominal control law. Then, the state vector is determined under fault-free operation and unknown time-varying rotor fault conditions, ensuring that all signals in the closed-loop system remain bounded. The given reference state vector generated by the asymptotic tracking reference system An adaptive control law is constructed to achieve the control objective. This process involves first constructing a nominal control law based on the MRAC framework, and then using this nominal control law to construct the adaptive control law.

[0050] The reference state vector represents the reference state corresponding to the preset flight attitude of the quadcopter UAV. It is generated by the reference system and used to provide a tracking target for the actual flight state. The reference system can be represented as: , in, Represents the reference state matrix. All eigenvalues ​​lie in the open left half of the complex plane. Represents the reference input matrix. This represents a bounded reference input signal.

[0051] Therefore, in one embodiment, constructing the nominal control law containing the fault compensation signal vector based on the rotor fault model specifically includes: The rotor fault model is embedded into the input vector of the quadcopter UAV, and the input vector of the quadcopter UAV is converted into an input vector description that includes the rotor fault model and the nominal control law. Based on the input vector form, a nominal control law is constructed according to the MRAC (Model Reference Adaptive Control) framework.

[0052] Specifically, the input vector is described as follows: , , in, Represents the control input vector. Represents the input matrix, This represents an adaptive control law. This represents the fault disturbance vector. This represents the fault indication matrix.

[0053] Furthermore, after obtaining the input vector description, a nominal control law is constructed. This nominal control law is built based on a model reference adaptive control framework, and its driving signals include a state vector, a bounded reference input signal, and a fault compensation signal. The nominal control law is as follows: , , , , in, This represents the nominal law of control. Represents the theoretical state gain vector. This represents the theoretical state gain. Represents the theoretical feedforward gain vector. This represents the theoretical feedforward gain. Represents the fault compensation signal vector. This indicates the fault compensation signals for the four rotors of a quadcopter drone. This indicates a bounded reference input signal. Represents the state vector; The matching condition for the nominal control law is: , , , in, Represents the state matrix, Represents the identity matrix. Represents the reference state matrix. Represents the input matrix The List, Represents the input matrix The List, Indicates the first A rotor fault signal.

[0054] S30. Based on the nominal control law, construct an adaptive control law and an adaptive control law parameter update mechanism to build an adaptive fault-tolerant controller for the quadcopter UAV.

[0055] Specifically, for ease of explanation, the faulty rotor will be denoted as the first... One rotor, that is However, in practical applications, the time of failure, the location of the faulty rotor, and the type of fault are all unknown. Therefore, taking this first... For each rotor acting as a faulty rotor, an adaptive control law is constructed based on the nominal control law. During tolerance control, the parameters of the adaptive control law are adaptively updated according to the actual situation, based on the adaptive control law parameter update mechanism. This adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the mechanism includes the fault dynamic information of the rotor fault model.

[0056] The adaptive control law is as follows: , , , , , in, This represents an adaptive control law. This represents the fault-tolerant state gain vector. This represents the gain in the fault-tolerant state. This represents the fault-tolerant feedforward gain vector. This represents the fault-tolerant feedforward gain. This represents the fault-tolerant fault compensation signal vector. This represents the fault-tolerant compensation signal for the four rotors of a quadcopter drone. This represents a known time-varying regression basis function. This indicates the number of time-varying regression basis functions. This represents the fault-tolerant fault compensation signal parameter. This indicates a bounded reference input signal. This represents the state vector. yes The estimated value, yes The estimated value, yes The estimated value, yes The estimated value.

[0057] Furthermore, the adaptive control law parameter update mechanism is as follows: , , , in, The derivative of the fault-tolerant state gain. The derivative of the fault-tolerant feedforward gain. The derivative of the fault-tolerant fault compensation signal parameter. Represents a symbolic function. Represents the first element of the reference input matrix. List, , , , Both represent adaptive gain. This indicates the number of time-varying regression basis functions. This represents a known time-varying regression basis function. This represents the tracking error determined based on the reference state vector and the state vector. This indicates a bounded reference input signal. Represents the state vector. Let be a positive definite matrix such that for any given positive definite matrix , . satisfy .

[0058] S40. Adaptive fault-tolerant control of the quadcopter drone is performed using the quadcopter drone adaptive fault-tolerant controller.

[0059] Specifically, after constructing the quadcopter UAV adaptive fault-tolerant controller, the quadcopter UAV is adaptively fault-tolerant controlled using the quadcopter UAV adaptive fault-tolerant controller. The process of using the quadcopter UAV adaptive fault-tolerant controller to perform adaptive fault-tolerant control on the quadcopter UAV involves receiving a bounded reference input signal from a host computer, determining a reference state vector using the quadcopter UAV's reference model, determining a tracking error based on the reference state vector and the quadcopter UAV's current state vector, using the tracking error as a driving force to call the quadcopter UAV adaptive fault-tolerant controller to determine an adaptive control law, and applying the adaptive control law to the quadcopter UAV to perform adaptive fault-tolerant control. In other words, through this quadcopter UAV adaptive fault-tolerant controller, the fault-tolerant state gain, fault-tolerant feedforward gain, and fault-tolerant fault compensation signal parameters are adaptively updated and adjusted according to the state tracking error. The adaptive control law parameters are continuously corrected to achieve real-time compensation for rotor actuator faults. This ensures that the actual flight state of the quadcopter UAV can stably track the preset reference flight state, maintain the stability and control accuracy of the quadcopter UAV during flight, and avoid flight loss of control or even crashes caused by rotor faults.

[0060] To illustrate the effectiveness of the methods provided in the embodiments of this application, experimental verification is conducted. In the experimental verification, methods such as... Figure 3 The quadcopter shown is equipped with a Pixhawk 6X Mini flight controller and has a wheelbase of 250 mm.

[0061] Experiment 1 To address continuous rotor faults during attitude tracking in a quadcopter UAV, a fault was injected into rotor number 1 randomly. Its thrust efficiency was continuously varied between 0.95 and 1.05 over 2-10 seconds via remote controller mode switching. It is important to emphasize that the fault information was completely unknown. Throughout the experiment, the reference state... Set to 0, The values ​​were set to 0.13 radians and -0.1 radians at approximately 2.3 seconds and 5.7 seconds, respectively. To verify the effectiveness of the proposed method, a comparative experiment was conducted using the Non-Fault-Tolerant Control (NFTC) method under the same flight mission and fault conditions. The control law of the NFTC method is derived from the control law of the Fault-Tolerant Control (AFTC) method, by removing the fault compensation signal vector from the AFTC control law. Therefore, the control law of the NFTC method is expressed as: .

[0062] Figure 4This demonstrates the attitude angle tracking performance of the quadrotor using AFTC and NFTC control laws under the considered fault conditions. Attitude angle data was calculated through the fusion of data from onboard gyroscope, accelerometer, and magnetometer sensors.

[0063] Experiment 2 A step fault was injected into rotor number 1, with efficiency coefficients set to 0.975 and 0.935 at approximately 3 seconds and 6.8 seconds, respectively. During this experiment, the fault information remained completely unknown. Throughout the experiment, the reference roll angle... Set to 0.15 radians at 3 seconds, referencing the pitch angle. Set to -0.12 radians at 6.7 seconds, and keep it at 0 in other states.

[0064] Figure 5 To determine the response of the quadcopter UAV under the considered control scheme, by Figure 5 It can be observed that, under unknown rotor fault conditions, the quadcopter UAV controlled by the NFTC method proposed in this application exhibits the desired closed-loop response.

[0065] In summary, this application provides an adaptive fault-tolerant control method for a quadrotor unmanned aerial vehicle (UAV) based on an unknown time-varying rotor fault. The method utilizes a linear parameterized structure to model the unknown time-varying rotor fault, obtaining a rotor fault model for the quadrotor UAV. A nominal control law containing fault compensation signals is constructed based on the rotor fault model. An adaptive control law and an adaptive control law parameter update mechanism are constructed based on the nominal control law to build an adaptive fault-tolerant controller for the quadrotor UAV. The quadrotor UAV is then subjected to adaptive fault-tolerant control using this adaptive fault-tolerant controller. This application constructs a rotor fault model for a single unknown time-varying rotor fault, achieving unified modeling for multiple rotor fault forms and solving the problem that existing methods cannot adapt to unknown time-varying faults. Furthermore, the adaptive control law parameter update mechanism constructed in this application is driven by dynamic fault information, enabling online adaptive updates of fault compensation parameters without prior knowledge of the specific form and variation of the fault. This ensures system stability and desired tracking performance even when the quadrotor UAV encounters an unknown time-varying rotor fault, improving the safety and reliability of quadrotor UAV flight.

[0066] Based on the aforementioned adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults, this embodiment provides an adaptive fault-tolerant control device for quadrotor UAVs based on unknown time-varying rotor faults, such as... Figure 6 As shown, the device includes: The rotor fault modeling module 100 is used to model unknown time-varying rotor faults using a linear parameterized structure to obtain a rotor fault model for a quadcopter UAV. The nominal control law construction module 200 is used to construct a nominal control law containing fault compensation signals based on the rotor fault model. The fault-tolerant controller construction module 300 is used to construct an adaptive control law and an adaptive control law parameter update mechanism based on the nominal control law, so as to construct an adaptive fault-tolerant controller for a quadcopter UAV. The adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the adaptive control law parameter update mechanism includes the fault dynamic information of the rotor fault model. The control module 400 is used to perform adaptive fault-tolerant control of the quadcopter drone using the adaptive fault-tolerant controller of the quadcopter drone.

[0067] Based on the above-described adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults, this embodiment provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults as described in the above embodiment.

[0068] Based on the aforementioned adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults, this application also provides a terminal device, such as... Figure 6 As shown, it includes at least one processor 20; a display screen 21; and a memory 22, and may also include a communications interface 23 and a bus 24. The processor 20, display screen 21, memory 22, and communications interface 23 can communicate with each other via the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setup mode. The communications interface 23 can transmit information. The processor 20 can invoke logical instructions in the memory 22 to execute the methods described in the above embodiments.

[0069] Furthermore, the logical instructions in the aforementioned memory 22 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0070] The memory 22, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 22, thereby implementing the methods in the above embodiments.

[0071] The memory 22 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 22 may include high-speed random access memory (RAM) and non-volatile memory. Examples include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, as well as transient storage media.

[0072] Furthermore, the specific process of loading and executing multiple instruction processors in the aforementioned storage medium and terminal device has been described in detail in the above method, and will not be repeated here.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An adaptive fault-tolerant control method for a quadrotor unmanned aerial vehicle (UAV) based on an unknown time-varying rotor fault, characterized in that, The method includes: A linear parameterized structure is used to model unknown time-varying rotor faults to obtain a rotor fault model for a quadcopter UAV. Based on the rotor fault model, a nominal control law containing fault compensation signals is constructed; An adaptive control law and an adaptive control law parameter update mechanism are constructed based on the nominal control law to build an adaptive fault-tolerant controller for a quadcopter UAV. The adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the adaptive control law parameter update mechanism includes the fault dynamic information of the rotor fault model. The quadcopter drone is adaptively fault-tolerant controlled using the aforementioned quadcopter drone adaptive fault-tolerant controller.

2. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault as described in claim 1, characterized in that, The rotor fault model of the quadcopter UAV is represented as follows: ， in, This represents a known time-varying regression basis function. Represents an unknown constant parameter. This indicates the number of time-varying regression basis functions.

3. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault as described in claim 1, characterized in that, The construction of the nominal control law containing the fault compensation signal vector based on the rotor fault model specifically includes: The rotor fault model is embedded into the input vector of the quadcopter UAV, and the input vector of the quadcopter UAV is converted into an input vector description that includes the rotor fault model and the nominal control law. Based on the input vector form, a nominal control law is constructed according to the MRAC framework, wherein the driving signal of the nominal control law includes a state vector, a bounded reference input signal, and a fault compensation signal. The input vector is described as follows: ， ， in, Represents the control input vector. Represents the input matrix, This represents an adaptive control law. This represents the fault disturbance vector. This represents the fault indication matrix.

4. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault according to claim 1 or 3, characterized in that, The nominal control law is: ， ， ， ， in, This represents the nominal law of control. Represents the theoretical state gain vector. This represents the theoretical state gain. Represents the theoretical feedforward gain vector. This represents the theoretical feedforward gain. Represents the fault compensation signal vector. This represents the fault compensation signal for the four rotors of a quadcopter drone. Indicates a bounded reference input signal. Represents the state vector; The matching condition for the nominal control law is: ， ， ， in, Represents the state matrix, Represents the identity matrix. Represents the reference state matrix. Represents the input matrix The List, Represents the input matrix The List, Indicates the first A rotor fault signal.

5. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault according to claim 1, characterized in that, The adaptive control law is as follows: ， ， ， ， ， in, This represents an adaptive control law. This represents the fault-tolerant state gain vector. This represents the gain in the fault-tolerant state. This represents the fault-tolerant feedforward gain vector. This represents the fault-tolerant feedforward gain. This represents the fault-tolerant fault compensation signal vector. This represents the fault-tolerant compensation signal for the four rotors of a quadcopter drone. This represents a known time-varying regression basis function. This indicates the number of time-varying regression basis functions. This represents the fault-tolerant fault compensation signal parameter. Indicates a bounded reference input signal. This represents the state vector.

6. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault according to claim 1 or 5, characterized in that, The adaptive control law parameter update mechanism is as follows: ， in, The derivative of the fault-tolerant state gain. The derivative of the fault-tolerant feedforward gain. The derivative of the fault-tolerant fault compensation signal parameter. Represents a symbolic function. Represents the first element of the reference input matrix. List, , , , Both represent adaptive gain. This indicates the number of time-varying regression basis functions. This represents a known time-varying regression basis function. This represents the tracking error determined based on the reference state vector and the state vector. Indicates a bounded reference input signal. Represents the state vector. Let be a positive definite matrix such that for any given positive definite matrix , . satisfy .

7. The adaptive fault-tolerant control method for a quadrotor UAV based on an unknown time-varying rotor fault as described in claim 1, characterized in that, The adaptive fault-tolerant control of the quadcopter using the quadcopter's adaptive fault-tolerant controller specifically includes: Receive the bounded reference input signal sent by the host computer and determine the reference state vector using the reference model of the quadcopter UAV; The tracking error is determined based on the reference state vector and the current state vector of the quadcopter UAV. Based on the tracking error, the adaptive control law is determined by calling the quadcopter UAV adaptive fault-tolerant controller. The adaptive control law is applied to the quadcopter drone to perform adaptive fault-tolerant control on the quadcopter drone.

8. An adaptive fault-tolerant control device for a quadcopter unmanned aerial vehicle based on an unknown time-varying rotor fault, characterized in that, The device includes: The rotor fault modeling module is used to model unknown time-varying rotor faults using a linear parameterized structure to obtain the rotor fault model of the quadcopter UAV. The nominal control law construction module is used to construct a nominal control law containing fault compensation signals based on the rotor fault model. The fault-tolerant controller construction module is used to construct an adaptive control law and an adaptive control law parameter update mechanism based on the nominal control law, so as to construct an adaptive fault-tolerant controller for a quadcopter UAV. The adaptive control law parameter update mechanism is constrained by the Lyapunov stability rule, and the driving signal of the adaptive control law parameter update mechanism includes the fault dynamic information of the rotor fault model. The control module is used to perform adaptive fault-tolerant control of the quadcopter using the quadcopter's adaptive fault-tolerant controller.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the adaptive fault-tolerant control method for quadrotor UAVs based on unknown time-varying rotor faults as described in any one of claims 1-7.

10. A terminal device, characterized in that, include: Processor and memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the adaptive fault-tolerant control method for quadrotor unmanned aerial vehicles based on unknown time-varying rotor faults as described in any one of claims 1-7.

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