An electric flying car system and adaptive fault-tolerant control method

Abstract

The application relates to an electric flying car system and an adaptive fault-tolerant control method, which comprises a flying car body, a first propeller fan, a second propeller fan, wheels, a consistency fault-tolerant control module, hub motors and a power battery component; the first propeller fan is fixed above the flying car body, the second propeller fan is fixed at the tail of the flying car body, the number of the wheels is multiple, each wheel is fixed on the two sides of the flying car body, the number of the hub motors is multiple, and each hub motor is connected to each wheel in a driving mode; the consistency fault-tolerant control component comprises a sensing module, a communication module and a main control module; the main control module is connected to a management system of the power battery component, a controller of the hub motor, the sensing module and the communication module. Compared with the prior art, the application solves the problems of communication obstacles among electric flying cars and the multi-agent fault-tolerant control problem of unknown control direction.

Description

Technical Field

[0001] This invention relates to the field of adaptive fault-tolerant control technology, and in particular to an electric flying car system and an adaptive fault-tolerant control method. Background Technology

[0002] Electric flying cars are a new concept in transportation. When road conditions are good, they function as cars on the road, and when encountering obstacles, they can temporarily fly over them. During flight, the output speed and torque of the front and rear fans must be constantly adjusted according to the current flight attitude to maintain vehicle balance and prevent rollovers. Furthermore, when flying cars are traveling in a convoy, the lead car and following cars must maintain the same speed to prevent collisions.

[0003] However, the above implementation requires real-time communication between different vehicles through a controller. Current vehicle platoon control suffers from actuator time-varying faults and communication obstacles, which hinder the realization of autonomous driving technology for platooned vehicles. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art in vehicle queuing control, such as actuator failure and communication obstacles, and to provide an electric flying car system and an adaptive fault-tolerant control method.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An electric flying car system includes a flying car body, a first propulsion fan, a second propulsion fan, wheels, a consistency fault-tolerant control module, hub motors, and a power battery component;

[0007] The first propulsion fan is fixed above the flying car body, and the second propulsion fan is fixed at the rear of the flying car body. There are multiple wheels, each fixed to one side of the flying car body. There are multiple hub motors, each driving and connecting to one of the wheels. The consistency fault-tolerant control component is located in the middle of the flying car body. The consistency fault-tolerant control component includes a sensing module, a communication module, and a main control module. The main control module connects to the management system of the power battery component, the controller of the hub motors, the sensing module, and the communication module. The second propulsion fan provides thrust to enable the vehicle to fly, and the first propulsion fan controls the steering of the flying car body, adjusting its flight attitude.

[0008] Furthermore, the power battery component is a lithium iron phosphate battery, a ternary material battery, a sodium-ion battery, a hybrid solid-liquid battery, an all-solid-state battery, a lithium-sulfur battery, or a lithium metal battery.

[0009] Furthermore, the consistency fault-tolerant control component also includes a safety module and a main control module. The safety module performs health management and fault diagnosis on key components of the vehicle. The main control module is connected to the management system of the power battery component and the controller of the wheel hub motor. The sensing module includes lidar, millimeter-wave radar, camera and vehicle attitude sensor.

[0010] Furthermore, the vehicle attitude sensor includes a speed monitoring unit, an altitude monitoring unit, a position monitoring unit, and a gyroscope.

[0011] like Figure 7 As shown, an adaptive fault-tolerant control method for an electric flying car includes the following steps:

[0012] Step 1: Construct a multi-agent system, which includes a leader system and a follower system, and obtain the dynamic equations of the multi-agent system;

[0013] Step 2: Obtain the communication graph between the leader system and the follower system, and construct a distributed observer;

[0014] Step 3: Construct an adaptive fault-tolerant controller based on a distributed observer, and solve the multi-agent closed-loop system using the adaptive fault-tolerant controller.

[0015] Furthermore, the expression for the follower system described in step 1 is:

[0016] y i =p i e i =y i -y0

[0017] Where, p i q i These are the position and velocity state variables of the i-th follower system, respectively, b i It is a constant, ω i (t) represents the external disturbance, u i y i e i These are the control input, measurement output, and error output of the i-th follower system, respectively.

[0018] Furthermore, the expression for the leader system described in step 1 is:

[0019] y0=χ(v)

[0020] Where y0∈R q The output of the external system is ψ(·) and χ(·), which are smooth functions that are 0 at the origin.

[0021] Furthermore, the signal η of the distributed observer in step 2 i It uses its own information η i and neighbor information η j The dynamic equations of the distributed observer are determined to be:

[0022]

[0023] Wherein, the weight constant a in the adjacency matrix ij The value of γ is determined by the communication diagram. ij It is the gain of the observer, ρ i (·) is a perfectly smooth, non-decreasing positive function, and ψ(·) is a perfectly smooth function that is 0 at the origin.

[0024] Furthermore, the expression for the adaptive fault-tolerant controller in step 3 is:

[0025]

[0026] Wherein, N(k) i ) is the Nussbaum function, k i It is the independent variable, k 2i β i δ i , These are positive constants, i = 1, ..., N; sgn(·) is the sign function. As an intermediate variable; The filtered tracking error is expressed as follows: in, c i It is a positive number.

[0027] The intermediate variable satisfy:

[0028]

[0029] Where, k 1i It is a positive number. in, yes The element in the l-th column, 1≤l≤n.

[0030] Furthermore, the dynamic equations of the multi-agent closed-loop system in step 3 are as follows:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] Where i = 1, ..., N, It is the error derived from the observer signal. yes The differential, yes Differential of , q i It's speed. The observer output χ(η) i The differential of ) yes The differential of the variable σ i (t)=b i h i (t), b i h is a constant whose sign is unknown. i (t) represents the health factor of the actuator in the i-th system, N(k) i ) is the Nussbaum function, k i It is the independent variable, k 2i β i δ i , It is a positive constant, φ i (t) represents the bias fault of the actuator in the i-th system, ω i (t) represents the external disturbance. As an intermediate variable, For the filtered tracking error, It is an unknown bounded positive constant ∈ i The estimated quantity, It is an unknown bounded positive constant. The estimate.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] (1) This scheme uses wheels for propulsion, followed by thrust from a second propulsion fan to achieve vehicle flight. The first propulsion fan controls the vehicle's steering and adjusts its flight attitude. Simultaneously, the equipped consistency-tolerance control components can collect information such as the flying car's position and status, and enable real-time communication with the cloud data center and other vehicles in the queue. This effectively solves the communication barriers between different vehicles, laying the foundation for adaptive fault-tolerant control of a multi-agent system composed of flying vehicles.

[0039] (2) This scheme solves the problem of communication barriers between electric flying cars by constructing an adaptive fault-tolerant controller and online observation, and further solves the problem of consistent fault-tolerant control between electric flying cars based on distributed self-adjusting observers; the introduction of the Nussbaum function into the controller solves the problem of unknown control direction, and the time-varying faults suffered by the actuator and the time-varying disturbances in the system can be adaptively estimated. Attached Figure Description

[0040] Figure 1 A first-view structural schematic diagram of the electric flying car system provided by the present invention;

[0041] Figure 2 A second-view structural schematic diagram of the electric flying car system provided by the present invention;

[0042] Figure 3 This invention provides a control framework for an electric flying car in land driving mode.

[0043] Figure 4 The present invention provides a control framework for an electric flying car in air flight mode.

[0044] Figure 5 The working process of the vehicle attitude sensor provided by this invention;

[0045] Figure 6 A flowchart illustrating the construction process of an adaptive fault-tolerant controller provided by this invention;

[0046] Figure 7 The electric flying car formation communication diagram provided by this invention;

[0047] In the diagram, 1-electric flying car body, 2-first propulsion fan, 3-second propulsion fan, 4-wheel, 5-seat, 6-sensor module, 7-consistency fault-tolerant control component, 8-communication module, 9-hub motor, 10-power battery component; node 0 is the leader of the electric flying car, and nodes 1-N are the followers of the electric flying car. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0050] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0051] Example 1

[0052] like Figure 1 As shown, an electric flying car system includes a flying car body 1, a first propulsion fan 2, a second propulsion fan 3, wheels 4, a consistency fault-tolerant control module 7, a hub motor 9, and a power battery component 10.

[0053] The first propulsion fan 2 is fixed above the flying car body 1, the second propulsion fan 3 is fixed at the rear of the flying car body 1, there are multiple wheels 4, each wheel 4 is fixed on both sides of the flying car body 1, there are multiple hub motors 9, each hub motor 9 drives and connects to each wheel, the consistency fault-tolerant control component 7 is located in the middle of the flying car body 1; the consistency fault-tolerant control component 7 includes a sensing module 6, a communication module 8 and a main control module, the main control module is connected to the management system of the power battery component 10, the controller of the hub motor 9, the sensing module 6 and the communication module 8, the second propulsion fan 3 is used to provide thrust to realize the flight of the vehicle, the first propulsion fan 2 controls the steering of the flying car body 1 and adjusts the flight attitude of the flying car body 1.

[0054] Working principle: Power is supplied to each module through the power battery module 10. The hub motor 9 drives the wheels 4 on land as a run-up phase before flight. The second propulsion fan 3 is turned on, and after the wheels leave the ground, they generate thrust on the flying car body 1, enabling the vehicle to fly. During flight, the first propulsion fan 2 controls the vehicle's steering and adjusts its flight attitude. The consistency fault-tolerant control component 7 collects the vehicle's status information in real time through the sensor module 6, and can achieve real-time communication with the cloud data center and other vehicles in the queue through the communication module 8.

[0055] This solution utilizes wheels 4 for propulsion, followed by thrust from the second propulsion fan 3 to achieve flight. The first propulsion fan 2 controls the vehicle's steering and adjusts its flight attitude. Simultaneously, the equipped consistency and fault-tolerant control component 7 collects information such as the position and status of the flying vehicle 1, and enables real-time communication with the cloud data center and other vehicles in the queue. This effectively solves the communication barriers between different vehicles, laying the foundation for the adaptive adjustment of a multi-agent system composed of flying vehicles.

[0056] like Figure 2 As shown, in this embodiment, the electric flying car includes two first propulsion fans 2 and one second propulsion fan 3. The first propulsion fans 2 mainly realize vehicle steering and attitude control, while the second propulsion fan 3 mainly realizes vehicle propulsion. In terms of the selection of the lift system and rotors, in addition to this arrangement, it can also include single-axis single-rotor, single-axis dual-rotor, scissor rotor, multi-axis multi-rotor, etc. Vectoring deflectors can also be installed. By controlling the vectoring deflectors, the direction of the car during flight can be controlled and the vehicle body spin can be prevented. By adjusting the speed or pitch of the left and right adjusting fans, the vehicle can be balanced in the air, preventing the vehicle from tipping over or rolling.

[0057] The power battery component 10 is a lithium iron phosphate battery, a ternary material battery, a sodium-ion battery, a hybrid solid-liquid battery, an all-solid-state battery, a lithium-sulfur battery, or a lithium metal battery.

[0058] The consistency fault-tolerant control component 7 also includes a safety module and a main control module. The safety module performs health management and fault diagnosis for key components of the vehicle. The main control module is connected to the management system of the power battery component 10 and the controller of the hub motor 9. The sensing module 6 includes a lidar, a millimeter-wave radar, a camera, and a vehicle attitude sensor.

[0059] This solution uses a safety module to manage the health and diagnose faults of key components of the vehicle, while the main control module is responsible for data analysis and control decisions for the vehicle. It is also connected to the management system of the power battery component 10 and the controller of the hub motor 9 to control the working strategies of the power battery and hub motor. Through multiple modules in the consistency fault-tolerant control component 7, the solution realizes the state acquisition, working decision-making and motion control of the electric flying car.

[0060] The vehicle attitude sensor includes a speed monitoring unit, an altitude monitoring unit, a position monitoring unit, and a gyroscope.

[0061] like Figure 5 As shown, this solution acquires the vehicle's status information through a speed monitoring unit, an altitude monitoring unit, a position monitoring unit, and a gyroscope, and then transmits the acquired status information to the vehicle attitude sensor for fusion to obtain the analysis results of the vehicle information, including the vehicle's speed, position, and other information.

[0062] Based on the above components, this embodiment provides the control principles for an electric flying car in land driving mode and in air flight mode. For example... Figure 3 As shown, in the control framework of the electric flying car in land driving mode, the power battery component 10 provides energy for the entire vehicle. It connects to the DC / DC module via a battery management system, regulating the output voltage to the rated input voltage of the motor controller. Each wheel 4 has a motor controller connected to the hub motor 9, controlling the speed and torque of each hub motor 9 via commands from the main control module. The consistency-tolerance control component 7 includes a sensing module 6, a safety module, a communication module 8, and a main control module. The sensing module 6 includes one or more monitoring units such as lidar, millimeter-wave radar, cameras, and vehicle attitude sensors, enabling the acquisition of vehicle status and information.

[0063] like Figure 4 As shown, in the control framework of the electric flying car in flight mode, the power battery system 10 provides energy for the entire vehicle. It connects to the DC / DC module via the battery management system, regulating the output voltage to the rated input voltage of the motor controller. Each propulsion fan has a motor controller connected to the motor, controlling the speed and torque of each fan motor via commands from the main control module. The consistency-tolerance control component 7 includes a sensing module 6, a safety module, a communication module 8, and a main control module. The main control module is responsible for data analysis and control decisions for the entire vehicle, connecting to the battery management system and the motor controller to control the operating strategies of the power battery and fan motors.

[0064] An adaptive fault-tolerant control method for an electric flying car includes the following steps:

[0065] Step 1: Construct a multi-agent system, which includes a leader system and a follower system, and obtain the dynamic equations of the multi-agent system;

[0066] Step 2: Obtain the communication graph between the leader system and the follower system, and construct a distributed observer;

[0067] Step 3: Construct an adaptive fault-tolerant controller based on a distributed observer, and solve the multi-agent closed-loop system using the adaptive fault-tolerant controller.

[0068] The expression for the follower system described in step 1 is:

[0069] y i =p i e i =y i -y0

[0070] Where, pi q i These are the position and velocity state variables of the i-th follower system, respectively, b i It is a constant whose sign is unknown, ω i (t) represents the external disturbance, u i y i e i These are the control input, measurement output, and error output of the i-th follower system, respectively.

[0071] The expression for the leader system described in step 1 is:

[0072] y0=χ(v)

[0073] Where y0∈R q The output of the external system is ψ(·) and χ(·), which are fully smooth functions that are 0 at the origin.

[0074] The signal η of the distributed observer in step 2 i It uses its own information η i and neighbor information η j The dynamic equations of the distributed observer are determined to be:

[0075]

[0076] Among them, the weight constant a in the adjacency matrix ij The value of γ is determined by the communication diagram. ij It is the gain of the observer, ρ i (·) is a perfectly smooth, non-decreasing positive function, and ψ(·) is a perfectly smooth function that is 0 at the origin.

[0077] The expression for the adaptive fault-tolerant controller in step 3 is:

[0078]

[0079] Wherein, N(k) i ) is the Nussbaum function, k i It is the independent variable, k 2i β i δ i , These are positive constants, i = 1, ..., N; sgn(·) is the sign function. As an intermediate variable; The filtered tracking error is expressed as follows: in, c i These are positive constants, where the error is derived using the observer signal.

[0080] The intermediate variable satisfy:

[0081]

[0082] Where, k 1i It is a positive number. in, yes The element in the l-th column, 1≤l≤n.

[0083] The dynamic equations of the multi-agent closed-loop system in step 3 are as follows:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] Where i = 1, ..., N, It is the error derived from the observer signal. yes The differential, yes Differential of , q i It's speed. The observer output χ(η) i The differential of ) yes The differential of the variable σ i (t)=b i h i (t), b i h is a constant whose sign is unknown. i (t) represents the health factor of the actuator in the i-th system, N(k) i ) is the Nussbaum function, k i It is the independent variable, k 2i β i δ i ζ i It is a positive constant, φ i (t) represents the bias fault of the actuator in the i-th system, ω i (t) represents the external disturbance. As an intermediate variable, For the filtered tracking error, It is an unknown bounded positive constant ∈ iThe estimated quantity, It is an unknown bounded positive constant. The estimate.

[0090] like Figure 6 As shown, this embodiment also provides a method for constructing an adaptive fault-tolerant controller:

[0091] Step S1: Obtain the dynamic equations of the electric flying car swarm.

[0092] In this example, the second-order nonlinear kinematic equations of the follower system are given in the following form: y i =p i e i =y i -y0. Where i = 1, ..., N, It is the state variable of the i-th system.

[0093] in and These are the different state variables of the i-th system, b i It is a constant with an unknown sign, representing the input direction of the i-th system input. It is the i-th system input. It is a bounded but uncertain external disturbance. These are the measurement output and error output of the i-th system, respectively. It is the output of the external system.

[0094] In this example, the leader signal v is generated by a nonlinear autonomous system of the following form: in, It is the output of the external system, and a(·) and g(·) are globally defined fully smooth functions. A function that is 0 at the origin of space, where G1 is the Jacobian matrix of g(v) at the origin, and G2(v) = diag{d1(v), ..., d n (v)}, where d j (v)≤0, j=1,…,n.

[0095] Step S2: Provide a failure model for the actuator:

[0096] u i =h i (t)u ci +φ i (t).

[0097] Among them, h i (t) represents the health factor of the actuator in the i-th system, which is an unknown bounded time-varying function, u ciφ is the control input of the actuator in the i-th system. i (t) represents the bias fault of the actuator in the i-th system, which is an unknown bounded time-varying function.

[0098] Step S3: Give the dynamic equations of the multi-agent system whose actuators have failed.

[0099] Based on steps S1 and S2, the dynamic equation of the multi-agent system suffering from actuator failure is: y i =p i e i =y i -y0, y0 = g(v).

[0100] Where, σ i (t)=b i h i (t).

[0101] Step S4: Obtain the communication diagram of the leader-follower system, such as... Figure 7 As shown.

[0102] A system consisting of a leader system and a follower system can be defined as a multi-agent system with N+1 agents. A communication barrier can be defined as the control input u of the i-th agent system. i Information about all other nodes cannot be accessed. The communication graph is defined using graph theory as follows: The point set Node 0 is associated with the leader system, and nodes i, i = 1, ..., N, are associated with the i-th agent in the follower system. It is an edge set. (Graph) The subgraph is defined as: in From Remove 0 nodes and The neighbor set of agent i is obtained by connecting the edges between the points in the network. Defined as: Among them, Figure weight matrix for Where a ij These are the weighting coefficients.

[0103] Step S5: Give the assumptions.

[0104] The nonlinear multi-agent system whose actuators suffer continuous time-varying faults satisfies the following assumptions: Assumption 1, the communication graph contains a directed spanning tree rooted at the leader node and the communication subgraph is undirected; Assumption 2, for any v0(0)∈R m The solution to the leader system exists and is bounded; Assumption 3: For the i-th follower, i = 1, ..., N, there exists an unknown positive constant d. i , so that ||ω i (t)||≤d i Assumption 4: For the i-th follower, i = 1, ..., N, there exists an unknown bounded positive constant ∈ i , so that ||φ i (t)||≤∈ i Assumption 5, for the effectiveness factor h of the executor in the i-th follower. i (t), where there are unknown positive constants. σ i and normal numbers Make Step S6: Design a consistency-tolerant fault controller to solve the fault-tolerant control problem when the actuator suffers a time-varying fault and the control direction is unknown. Specifically, the consistency-tolerant fault control problem involves designing a controller to ensure that a solution exists for the multi-agent system composed of a nonlinear follower system and a nonlinear leader system suffering a time-varying actuator fault as described in Step S2, according to the communication graph assumed in Assumption 1 of S5. This solution satisfies... Step S601: Design a self-adjusting distributed observer.

[0105] The self-adjusting distributed observer is designed in the following form: Among them, i=1,...,N,j=0,1,...,N,η i Let η be the observation value of agent i. j Let ρ be the observation value of agent j. i (·) is a sufficiently smooth, non-decreasing positive function. Design γ ij It is a self-adjusting distributed observer gain. Step S602: Design the filtered tracking error.

[0106] Design the tracking error function s after filtering. i for: in, c i It is a positive constant, e i =x i1 -χ(v),

[0107] Step S603: Use an observer to design the filtered tracking error.

[0108] After using the S601 observer, design the filtered tracking error function. for: in, c i It is a positive number.

[0109] Step S604, Consistency Fault-Tolerant Controller u ci Designed as follows: Wherein, N(k) i ) is the Nussbaum function, k i It is the independent variable, k 2i β i δ i , These are positive constants, i = 1, ..., N. sgn(·) is the sign function, and the intermediate variable is... The dynamic equations are designed as follows: k 1i It is a positive number. in, yes The element in the l-th column, 1≤l≤n.

[0110] Step S7: Construct the dynamic equations of the closed-loop system.

[0111] Based on the above-mentioned consistent fault-tolerant controller, the dynamic equation of a multi-agent closed-loop system where the actuator suffers continuous time-varying faults and the control direction is unknown is as follows:

[0112] Step S8: The theoretical proof that the above adaptive fault-tolerant controller can solve the fault-tolerant control problem is as follows:

[0113] Step S801: Construct the Lyapunov energy function:

[0114] Step S802: After analysis, the derivative of the energy function along the closed-loop system in step twelfth is calculated as follows:

[0115] Step S803: Analyze the limits of the energy function along the derivative of the closed-loop system from step twelfth: Let the positive constant k 1i k 2i θ i and Satisfy: k 2i ≤k 1i θ i≤1 and It can be obtained

[0116] Step S804: Integrate both sides of the inequality obtained in S803 to get:

[0117] in, The boundedness of the function can be proved by contradiction using the properties of the Nussbaum function. The proof yields... Bounded.

[0118] Step S805, calculate derivative After analysis, it was obtained that It is bounded, therefore for any t≥0, Consistent and continuous.

[0119] Step S806: According to Barbalat's lemma, we obtain therefore, Gradually, and then obtained and Gradually. That is to say, and Gradually.

[0120] Step S807: Combining the verification results of the observer in step S302, 0 asymptotically, Asymptotically, we thus obtain: asymptotically and Asymptotically, this completes the theoretical proof of the consistency-tolerant control problem.

[0121] Step s9: Simulation verification.

[0122] The leader system in step s1 is generated by the Van der Pol system:

[0123]

[0124] make The remaining initial values ​​are generated randomly. Let The multi-agent system suffering actuator failure in step s4 is specifically defined as follows: y i =p i e i =y i -y0, i = 1, ..., 4.

[0125] Step s10: Experimental verification.

[0126] A prototype of the controller designed in this invention was developed and applied to an electric flying car to verify its effectiveness in consistent fault-tolerant control of multiple electric flying cars suffering actuator failures.

[0127] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. An adaptive fault-tolerant control method for an electric flying car, characterized in that, Includes the following steps: Step 1: Construct a multi-agent system, which includes a leader system and a follower system, and obtain the dynamic equations of the multi-agent system; Step 2: Obtain the communication graph between the leader system and the follower system, and construct a distributed observer; Step 3: Construct an adaptive fault-tolerant controller based on a distributed observer, and solve the multi-agent closed-loop system using the adaptive fault-tolerant controller; The expression for the follower system described in step 1 is: ， ， ， in, , These are the position and velocity state variables of the i-th follower system, respectively. It is a constant. It is an external disturbance. , , They are the first The control input, measurement output, and error output of a follower system; The signal from the distributed observer in step 2 Used its own information Information with neighbors The dynamic equations of the distributed observer are determined to be: Among them, the weight constants in the adjacency matrix The value is determined by the communication diagram. It is the gain of the observer. (∙) is a sufficiently smooth, non-decreasing positive function. A function that is sufficiently smooth and equal to 0 at the origin; The expression for the adaptive fault-tolerant controller in step 3 is: ， ， ， ， in, It is the Nussbaum function. It is the independent variable. , , It is a positive number. ; It is a symbolic function. As an intermediate variable; Its expression is: ,in, , It is a positive number. , ; The intermediate variable satisfy: in, It is a positive number. ,in, yes The Column elements, .

2. The adaptive fault-tolerant control method for an electric flying car according to claim 1, characterized in that, The expression for the leader system described in step 1 is: ， in, It is the output of the external system. , These are smooth functions that are zero at the origin.

3. The adaptive fault-tolerant control method for an electric flying car according to claim 1, characterized in that, The dynamic equations of the multi-agent closed-loop system in step 3 are as follows: ， ， ， in, , It is the error derived from the observer signal. yes The differential, yes The differential, It's speed. It is the observer output The differential, yes Differential, variable , It is a constant whose sign is unknown. For the first Health factors of actuators in each system It is the Nussbaum function. It is the independent variable. , , It is a positive number. For the first Bias fault of actuator in the system It is an external disturbance. As an intermediate variable, , It is an unknown bounded positive constant. The estimated quantity, It is an unknown bounded positive constant. The estimate.

4. The adaptive fault-tolerant control method for an electric flying car according to claim 1, characterized in that, It includes the flying car body (1), the first propulsion fan (2), the second propulsion fan (3), the wheels (4), the consistency fault-tolerant control module (7), the hub motor (9), and the power battery component (10). The first propulsion fan (2) is fixed above the flying car body (1), the second propulsion fan (3) is fixed at the rear of the flying car body (1), there are multiple wheels (4), each wheel (4) is fixed on both sides of the flying car body (1), there are multiple hub motors (9), each hub motor (9) drives and connects to each wheel, the consistency fault-tolerant control component (7) is located in the middle of the flying car body (1); the consistency fault-tolerant control component (7) includes a sensing module (6), a communication module (8) and a main control module, the main control module is connected to the management system of the power battery component (10), the controller of the hub motor (9), the sensing module (6) and the communication module (8), the second propulsion fan (3) is used to provide thrust to realize the flight of the vehicle, the first propulsion fan (2) controls the steering of the flying car body (1) and adjusts the flight attitude of the flying car body (1).

5. The adaptive fault-tolerant control method for an electric flying car according to claim 4, characterized in that, The power battery component (10) is a lithium iron phosphate battery, a ternary material battery, a sodium-ion battery, a hybrid solid-liquid battery, an all-solid-state battery, a lithium-sulfur battery, or a lithium metal battery.

6. The adaptive fault-tolerant control method for an electric flying car according to claim 4, characterized in that, The consistency fault-tolerant control component (7) also includes a safety module and a main control module. The safety module performs health management and fault diagnosis on key components of the vehicle. The main control module is connected to the management system of the power battery component (10) and the controller of the hub motor (9). The sensing module (6) includes a lidar, a millimeter-wave radar, a camera and a vehicle attitude sensor.

7. The adaptive fault-tolerant control method for an electric flying car according to claim 6, characterized in that, The vehicle attitude sensor includes a speed monitoring unit, an altitude monitoring unit, a position monitoring unit, and a gyroscope.

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