Method and device for fixed-time formation control of multi quadrotor unmanned aerial vehicle

Abstract

The application discloses a kind of multi-four rotor unmanned plane fixed time formation control method and device, comprising: S1, the nonlinear model considering actuator time-varying fault and unknown external disturbance is established;S2, the fixed time adaptive fault-tolerant controller of boundary estimation method and fuzzy logic system is used to realize the adaptive compensation of actuator time-varying fault;S3, a kind of dynamic event-triggered control strategy is constructed.The application realizes the fixed time formation control of multi-four rotor unmanned plane.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) formation control, and in particular to a method and apparatus for fixed-time formation control of quadcopter UAVs. Background Technology

[0002] Inspired by the collective behavior of animals in nature, in practical engineering applications, scholars have connected multiple quadcopter unmanned aerial vehicle (UAV) systems via communication to achieve collaborative operations. In military applications, multi-aircraft systems, also known as intelligent aircraft swarm systems, are integrated systems comprised of numerous aircraft based on an open architecture. Centered on a communication network, they leverage the system's swarm intelligence capabilities, rely on inter-platform collaborative interaction, and are supported by the node combat capabilities of individual platforms. This results in an integrated aircraft system with advantages such as survivability, low cost, distributed functionality, and intelligent characteristics. Unlike traditional single-aircraft combat platforms, intelligent aircraft swarm systems emphasize three key characteristics: swarm intelligence, collaborative interaction, and single-platform node combat capabilities, offering the following advantages in combat:

[0003] (1) Functional distribution: The various functions of a single complete combat platform, such as reconnaissance and surveillance, electronic jamming, strike and assessment capabilities, are "broken down" and distributed to a large number of low-cost, single-function combat platforms. The complex system functions are realized through a large number of heterogeneous and non-standard individuals. The multiplier effect and intelligence of the system will enable the cluster to have combat capabilities far exceeding those of a single platform.

[0004] (2) System survivability: Intelligent aircraft swarms have the characteristics of being "decentralized" and "autonomous and collaborative". Individuals in the swarm do not depend on a specific, existing node to operate. During the confrontation, when some individuals lose their combat capabilities, the entire swarm still maintains a certain degree of integrity and can continue to carry out combat missions.

[0005] (3) Cost-effectiveness ratio: By reducing the cost of single-function UAV platforms, when conducting combat missions, the enemy will have to spend tens or even hundreds of times more to defend against a large number of individual aircraft. At the same time, the research and application of aircraft self-assembly and payload recovery and reuse technologies will also significantly reduce the cost of individual aircraft, ultimately bringing significant cost-effectiveness advantages. Summary of the Invention

[0006] The purpose of this invention is to provide a method and apparatus for fixed-time formation control of quadcopter drones, aiming to solve the problem of fixed-time formation control of quadcopter drones.

[0007] This invention provides a method for fixed-time formation control of quadcopter unmanned aerial vehicles, comprising:

[0008] S1. A nonlinear model considering time-varying actuator faults and unknown external disturbances was established.

[0009] S2. Adaptive compensation for time-varying actuator faults is achieved by using boundary estimation methods and a fixed-time adaptive fault-tolerant controller based on fuzzy logic systems.

[0010] S3. A dynamic event-triggered control strategy was constructed.

[0011] This invention also provides a device for fixed-time formation control of a quadcopter unmanned aerial vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the above method.

[0012] This invention also provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor, implements the steps of the above-described method.

[0013] The embodiments of the present invention implement fixed-time formation control of UAVs.

[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for fixed-time formation control of a quadcopter UAV according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a quadcopter drone illustrating the method for fixed-time formation control of quadcopter drones according to an embodiment of the present invention.

[0018] Figure 3 This is a control schematic diagram of a fixed-time formation control method for quadcopter UAVs according to an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the communication topology between quadrotor drones in the method for fixed-time formation control of quadrotor drones according to an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of a device for fixed-time formation control of a quadcopter UAV according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Method Implementation Examples

[0023] According to embodiments of the present invention, a method for fixed-time formation control of quadcopter unmanned aerial vehicles is provided. Figure 1 This is a flowchart of a method for fixed-time formation control of a quadcopter UAV according to an embodiment of the present invention, as shown below. Figure 1 As shown, it specifically includes:

[0024] The present invention differs from existing technologies in three main ways. First, considering the underactuated characteristics of quadcopter UAVs—multiple inputs and multiple outputs, strong coupling—a nonlinear model considering time-varying actuator faults and unknown external disturbances is established. Second, for the position subsystem, the present invention designs a fixed-time adaptive fault-tolerant controller using boundary estimation methods and fuzzy logic systems to achieve adaptive compensation for time-varying actuator faults. Third, by introducing the rate of change of the control signal as an adaptive parameter, a periodic adaptive event-triggered control (PAETC) scheme is constructed.

[0025] In this invention, a leader and A multi-intelligence system consisting of 0 followers. For ease of description, the leader is labeled 0, and the followers are labeled 0. Each drone consists of four independent rotors, and the drone's ascent and descent are achieved by controlling the rotational speed of the four motors, such as... Figure 1 As shown, This indicates the rotational speeds of the four rotors. This represents the corresponding lift generated by the rotor. Euler angles are used to describe the attitude dynamics of the quadcopter UAV. These represent the pitch angle, yaw angle, and roll angle, respectively. The three-dimensional position of the quadcopter UAV during its spatial motion is... The speed is One of the technical means that distinguishes this invention from existing technologies is that it considers multi-UAV systems with externally bounded perturbations and establishes the first... The dynamic model of the quadcopter UAV is as follows:

[0026] (1)

[0027] In the formula Indicates the first The weight of a quadcopter drone It is gravitational acceleration. , and It is the rotational inertia of the drone. It is the distance from the tip of each rotor to the center of gravity of the drone. , , , , and This represents the damping coefficient. , , , , and This indicates an externally bounded disturbance.

[0028] Figure 2 This is a schematic diagram of a quadcopter drone used in a fixed-time formation control method according to an embodiment of the present invention.

[0029] Define virtual control laws.

[0030] (2)

[0031] The position subsystem part in equation (1) can be simplified as follows:

[0032] (3)

[0033] In the formula and It is the state vector of the position subsystem. and These represent the position subsystem output and control input, respectively. This represents the ratio of damping coefficient to mass, and indicates unknown external disturbances to the position subsystem.

[0034] In practical applications, due to external uncertainties or hardware aging, wear, or damage, the actuators of intelligent agents inevitably malfunction, which can lead to input problems. and actuator output The differences between these factors will affect system performance. This difference is modeled as follows:

[0035] (4)

[0036] in

[0037] It is a health factor. This represents the uncontrollable parameter portion of the control signal. Indicates actuator input, This indicates the moment when the actuator malfunctioned.

[0038] Considering the fault model (4), the position word system model (3) is rewritten as follows:

[0039] (5)

[0040] The fixed directed communication topology of a multi-UAV system is defined as follows: ,in It is a combination of leaders and followers; Represents the set of edges. It is a weighted adjacency matrix among followers. Indicates drone and drones The weights of the connections between them. If and Followers Able to use drones If its status information is obtained from the location, then ,otherwise, Therefore, followers The neighbor is If followers If status information can be obtained from the leader, then ,otherwise The in-degree matrix is ​​represented as... ,in The Laplace matrix of a weighted directed graph is defined as follows: .

[0041] Lemma 1: For the system ,in It is a state vector. For a positive definite continuous function ,satisfy,

[0042] (6)

[0043] in , , , and It is a constant. The system is derived. It converges in a fixed time. And... The set time, the residual set of the solution, and the preset convergence time are respectively...

[0044] (7)

[0045] (8)

[0046] in .

[0047] Lemma 2: For and ,have,

[0048] (9)

[0049] Lemma 3: For , , , , Then the following expression is true:

[0050] (10)

[0051] Lemma 4: For , and ,

[0052] (11)

[0053] (12)

[0054] Lemma 5: Young's Inequality: For and ,

[0055] (13)

[0056] in , , , .

[0057] Lemma 6: For and any constant ,

[0058] (14)

[0059] Lemma 7: Definition , , ,get,

[0060] (15)

[0061] in and Representing tracking error and The smallest singular value.

[0062] Assumption 1: Every follower has a path from the leader to themselves. The leader's output. It is bounded and continuous, and its second derivative is... Available.

[0063] Assumption 2: For positive and bounded time-varying parameters There exists an unknown nonnegative constant. and , making For bounded external disturbances ,exist , making .

[0064] Assumption 3: The health factor and uncontrollable additive-driven faults in the actuator fault model are unknown, time-varying, but bounded, and exist as constants. and satisfy , .

[0065] As is well known, any unknown nonlinear continuous function can be approximated using a fuzzy logic system. A fuzzy logic system is represented in the following form:

[0066] (16)

[0067] In the formula It is the input vector. , It is a fuzzy membership function. It is a fuzzy rule number.

[0068] Define the following fuzzy basis functions:

[0069] (17)

[0070] make , Then the fuzzy logic system (16) can be represented as,

[0071] (18)

[0072] Lemma 8: For unknown continuous functions , Using the fuzzy logic system (18), the following result holds true:

[0073] (19)

[0074] Where any given constant .

[0075] Note 1: The formation of multiple drones is composed of... This means that if for any initial condition, and for all If a constant exists If the following expression is satisfied, it indicates that there is a leader and The multi-UAV system with one follower (5) achieves formation tracking control

[0076] (20)

[0077] in To track error boundaries.

[0078] A. Position subsystem controller design,

[0079] First, the followers were defined. The coordinate transformation is as follows:

[0080] (twenty one)

[0081] (twenty two)

[0082] in , It is the leader's output; It is the formation synchronization error; It is a virtual error; This is the virtual controller that we will design next.

[0083] Step 1: Select the first Lyapunov function as follows,

[0084] (twenty three)

[0085] According to (5) and (21), The time derivative is,

[0086] (twenty four)

[0087] The time derivative is,

[0088] (25)

[0089] Virtual control law The design is as follows:

[0090] (26)

[0091] in and These are design parameters.

[0092] Substituting (26) into (25) yields,

[0093] (27)

[0094] Step 2: Based on (5), Then, we defined the function. and According to assumptions 2-3, we know that and It is bounded. Therefore, we define the boundary. , , The second technical aspect of this invention, which distinguishes it from existing technologies, involves using fuzzy logic systems and adaptive control techniques to estimate the relevant boundaries of the unknown time-varying parameter vector and the unknown state of the actuator fault, in order to address unknown time-varying actuator faults, control gains, and external disturbances, thus reflecting the worst-case impact on the system. Two adaptive parameters are involved. and Defined to estimate and To obtain the estimation error and .

[0095] Choose the second Lyapunov function as follows,

[0096] (28)

[0097] in , and For design parameters; yes The estimate will be designed later.

[0098] The time derivative is:

[0099] (29)

[0100] in,

[0101] , ,if , . Since it is a continuous function, according to Lemma 7, it can be approximated using a fuzzy logic system. And assume

[0102] (30)

[0103] Where the approximation error satisfy , .

[0104] According to Lemma 5, we obtain...

[0105] (31)

[0106] in It is a positive design parameter. .

[0107] Substituting (27) and (31) into (29) yields

[0108] (32)

[0109] In most existing relative threshold event-triggered control strategies, a larger formation error corresponds to a larger control signal amplitude to obtain a longer update interval, while a smaller threshold corresponds to a more stable state to obtain a more precise control effect. However, when the control signal amplitude is large, using a longer update interval may affect the formation speed of the multi-agent system, or even cause the system to lose control. The third difference between this invention and existing technologies lies in the proposed dynamic event-triggered control strategy. When the rate of change is large, a shorter update interval is used to improve the system's stabilization speed; while when the control signal... When the rate of change is small, i.e., when the system state tends to be in equilibrium, a longer update interval is used to further save communication resources. The event-triggered control strategy is designed as follows:

[0110] (33)

[0111] (34)

[0112] (35)

[0113] In the formula It is the controller update time. Indicates the initial time; , , It is a positive design constant. , This indicates the interval between two consecutive triggering moments.

[0114] Note 2: Whenever (33) is triggered, the control signal It will be applied to the actuator immediately, and the next trigger point will be obtained according to (33). The control signal is at a time interval. The value will remain constant. Clearly, this control signal update mechanism reduces the update frequency, thereby saving communication resources.

[0115] According to (33) and (34), for ,have Furthermore, we obtained... , and .when ,get,

[0116] (36)

[0117] According to Lemmas 2 and 5, the above equation can be rewritten as follows:

[0118] (37)

[0119] in .

[0120] when ,get,

[0121] (38)

[0122] According to Lemmas 2 and 5, the above equation can be rewritten as follows:

[0123] (39)

[0124] in .

[0125] By combining (37) and (39), we get:

[0126] (40)

[0127] Control Law It is designed as follows,

[0128] (41)

[0129] According to Lemma 7, from (41), we can obtain,

[0130] (42)

[0131] The following formula is correct.

[0132] (43)

[0133] Substituting equations (40), (42), and (43) into equation (32) yields

[0134] (44)

[0135] in,

[0136] (45)

[0137] Virtual control law The design is as follows:

[0138] (46)

[0139] in , and These are design parameters.

[0140] Substituting (46) into (44) yields,

[0141] (47)

[0142] The three adaptive laws are designed as follows:

[0143] (48)

[0144] (49)

[0145] (50)

[0146] in , and These are positive design parameters.

[0147] According to Lemma 2, substituting (48)-(50) into (47) yields,

[0148] (51)

[0149] According to Lemma 5,

[0150] (52)

[0151] (53)

[0152] (54)

[0153] Using Lemma 3, (52)-(54) can be rewritten as follows:

[0154] (55)

[0155] (56)

[0156] (57)

[0157] in, .

[0158] Substituting equations (55)-(57) into (51), we get:

[0159] (58)

[0160] in,

[0161] (59)

[0162] according to , and The definition is as follows:

[0163] (60)

[0164] (61)

[0165] (62)

[0166] According to Lemma 5, we have,

[0167] (63)

[0168] (64)

[0169] (65)

[0170] (66)

[0171] (67)

[0172] (68)

[0173] in .

[0174] Combining equations (60) and (68), we get:

[0175] (69)

[0176] (70)

[0177] (71)

[0178] Substituting (69)-(71) into (58) yields,

[0179] (72)

[0180] in,

[0181] (73)

[0182] According to Lemma 4, we obtain...

[0183] (74)

[0184] in , , , , , , , .

[0185] (74) was rewritten as,

[0186] (75)

[0187] in , .

[0188] To solve the position control law, the desired attitude angle needs to be solved first.

[0189] From equation (2), we can see that

[0190] (76)

[0191] (77)

[0192] (76) can be rewritten as,

[0193] (78)

[0194] Substituting (77) into (78) yields,

[0195] (79)

[0196] From (79), we can obtain that

[0197] (80)

[0198] (81)

[0199] The required attitude angle can then be obtained. and for,

[0200] (82)

[0201] (83).

[0202] B. Attitude subsystem controller design,

[0203] A sliding mode controller for attitude trajectory tracking control is designed to make the system tend toward the desired attitude angle while ensuring tracking performance.

[0204] First, the equation for the attitude angle error of the UAV is defined as follows:

[0205] (84)

[0206] (85)

[0207] (86)

[0208] The sliding mode function is designed as follows.

[0209] (87)

[0210] (88)

[0211] (89)

[0212] in , , .

[0213] According to (1), (84), (85) and (86), the time derivative of the sliding mode function is,

[0214] (90)

[0215] (91)

[0216] (92)

[0217] The sliding mode control law is designed as follows:

[0218] (93)

[0219] (94)

[0220] (95)

[0221] in , , , , , .

[0222] The Lyapunov function is selected as follows.

[0223] (96)

[0224] (97)

[0225] (98)

[0226] Substituting (90)-(95) into (96)-(98) yields , , The time derivatives of and are

[0227] (99)

[0228] (100)

[0229] (101)

[0230] because , and This ensures the sliding surface , and Exponential convergence, i.e. , and The exponents converge to , and .

[0231] Combining (27), (75), (99), (100) and (101), it is shown that the overall system stability is guaranteed, and the position trajectory and attitude angle tracking capabilities are guaranteed.

[0232] Figure 3 This is a control schematic diagram of a fixed-time formation control method for quadcopter UAVs according to an embodiment of the present invention;

[0233] Next, simulation examples will be used to demonstrate the effectiveness and control performance of the proposed method.

[0234] Consider a networked, uncertain, nonlinear quadrotor unmanned aerial vehicle (UAV) system with time-varying actuator failure, consisting of one leader and four followers. Figure 4 This is a schematic diagram of the communication topology between quadrotor drones in the method for fixed-time formation control of quadrotor drones according to an embodiment of the present invention. Figure 4 This represents the communication topology among quadcopter drones (0 represents the leader, 1, 2, 3, 4 represent the followers), the... ( The dynamic model and position subsystem model of the quadcopter UAV are as follows:

[0235] (102)

[0236] (103)

[0237] in and These represent the position and speed of the quadcopter drone, respectively. These represent pitch angle, yaw angle, and roll angle, respectively. , , and Indicates control input; This indicates unknown external interference. Indicates the damping coefficient; Indicates the mass of a quadcopter drone. This indicates the distance from the rotor tip to the aircraft's center of gravity; This represents the moment of inertia of a quadcopter drone.

[0238] In the simulation, the position subsystem considers the case of time-varying actuator faults, and the actuator fault model is as follows:

[0239] (104)

[0240] in , ,

[0241] (105)

[0242] (106)

[0243] In the simulation, the physical parameters of each quadcopter UAV are designed as follows: , , , , , , , , , , , , .

[0244] Initial state of each quadcopter drone: , , , , , , , , , , , The leader's expected trajectory was selected as follows: The formation vector is , , .

[0245] The designed fixed-time adaptive event-triggered controller is shown below:

[0246] (107)

[0247] (108)

[0248] (109)

[0249] (110)

[0250] (111)

[0251] (112)

[0252] (113)

[0253] (114)

[0254] (115)

[0255] (116)

[0256] (117)

[0257] (118)

[0258] (119)

[0259] (120)

[0260] (121)

[0261] (122)

[0262] (123)

[0263] (124)

[0264] in , , , , , The remaining design parameters are shown in Table 1.

[0265] Table 1 Controller Design Parameters

[0266]

[0267] The fuzzy membership function is selected as follows:

[0268] (125)

[0269] The fuzzy basis function is defined as follows:

[0270] (126)

[0271] (1) Compared with most existing formation control technologies for multiple UAVs, this invention takes into account the underactuated characteristics of quadrotor UAVs with multiple inputs and multiple outputs and strong coupling. It establishes a nonlinear model that considers time-varying actuator faults and unknown external disturbances. The designed control overcomes the problem of quadrotor UAVs being difficult to control in precise formation, making the system more robust and stable within a fixed time.

[0272] (2) This invention takes into account the actuator failure problem that is difficult to avoid in actual working conditions of quadcopter UAVs, and the failure is also time-varying. The proposed control method can cope with most actuator failure problems. The actual results also verify that the quadcopter UAV formation control method has good fault-tolerant control performance and improves the system's application capability under complex working conditions.

[0273] (3) Implementing formation control of a multi-quadcopter UAV system will increase the communication cost of each quadcopter UAV, while the energy carried by each UAV is limited. Compared with most existing event-triggered control methods, this invention introduces the rate of change of the control signal as an adaptive parameter to construct a periodic adaptive event-triggered control (PAETC) scheme, which can save communication resources well without affecting system performance.

[0274] Device Example 1

[0275] This invention provides a device for fixed-time formation control of quadcopter unmanned aerial vehicles, such as... Figure 5 As shown, it includes: a memory 50, a processor 52, and a computer program stored on the memory 50 and executable on the processor 52. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0276] Device Example 2

[0277] This invention provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor 52, implements the steps described in the above method embodiments.

[0278] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 or all of the technical features; and these modifications or substitutions to the technical solutions of the embodiments of the present invention do not cause the essence of the corresponding technical solutions to deviate from the scope of the present solution.

Claims

1. A method for fixed-time formation control of multiple quadcopter unmanned aerial vehicles, characterized in that, include: S1. A nonlinear model considering time-varying actuator faults and unknown external disturbances was established. S2. Adaptive compensation for time-varying actuator faults is achieved by using boundary estimation methods and a fixed-time adaptive fault-tolerant controller based on fuzzy logic systems. S3. A dynamic event triggering control strategy was constructed. Specifically, S1 includes: considering a multi-UAV system with external bounded disturbances, establishing the first... The dynamic model of the quadcopter UAV is as follows: (1)； In the formula Indicates the first The weight of a quadcopter drone It is gravitational acceleration. , and It is the rotational inertia of the drone. It is the distance from the tip of each rotor to the center of gravity of the drone. , , , , and Indicates the damping coefficient. , , , , and Indicates an externally bounded disturbance; Define virtual control laws: (2)； The position subsystem part in equation (1) can be simplified as follows: (3)； In the formula and It is the state vector of the position subsystem. and These represent the position subsystem output and control input, respectively. This represents the ratio of the damping coefficient to the mass. This indicates that the location subsystem is subject to unknown external interference. In practical applications, due to the influence of external uncertainties or hardware aging, wear, and damage, the actuators of intelligent agents inevitably malfunction, which can lead to input failures. and actuator output The differences between them will affect system performance; this difference is modeled as follows: (4)； in, It is a health factor. This represents the uncontrollable parameter portion of the control signal. Indicates actuator input, Indicates the moment when the actuator malfunctioned; Considering the fault model (4), the location subsystem model (3) is rewritten as follows: (5)； The fixed directed communication topology of a multi-UAV system is defined as follows: ,in It is a combination of leaders and followers; Represents the set of edges; It is a weighted adjacency matrix among followers. Indicates drone and drones The weights of the connections between them; if and Followers Able to use drones If its status information is obtained from the location, then ,otherwise, Therefore, followers The neighbor is If followers If status information can be obtained from the leader, then ,otherwise ; ,in The Laplace matrix of a weighted directed graph is defined as follows: ; Lemma 1: For the system ,in It is a state vector. For a positive definite continuous function ,satisfy: (6)； in , , , and It is a constant; the system is derived It converges in a fixed time; and The set time, the residual set of the solution, and the preset convergence time are respectively: (7)； (8)； in ; Lemma 2: For and ,have, (9)； Lemma 3: For , , , , Then the following expression is true: (10)； Lemma 4: For , and ,have, (11)； (12)； Lemma 5: Young's Inequality: For and ,have, (13)； in , , , ; Lemma 6: For and any constant ,have, (14)； Lemma 7: Definition , , ,get, (15)； in and Representing tracking error and The minimum singular value; Assumption 1: All followers have a path from the leader to themselves; the leader's output... It is bounded and continuous, and its second derivative is... it works; Assumption 2: For positive and bounded time-varying parameters There exists an unknown nonnegative constant. and , making For bounded external disturbances ,exist , making ; Assumption 3: The health factor and uncontrollable additive-driven faults in the actuator fault model are unknown, time-varying, but bounded, and exist as constants. and satisfy , ; Fuzzy logic systems are represented in the following form: (16)； In the formula It is the input vector. , It is a fuzzy membership function. It is a fuzzy rule number; Define the following fuzzy basis functions: (17)； make , Then the fuzzy logic system (16) can be represented as: (18)； Lemma 8: For unknown continuous functions , Using the fuzzy logic system (18), the following results hold true: (19)； Where any given constant ; Note 1: The formation of multiple drones is composed of... This means that under any initial conditions, if a constant exists... If the following expression is satisfied, it indicates that there is a leader and The multi-UAV system with one follower (5) achieves formation tracking control （20）； in To track error boundaries, ; A. Position subsystem controller design: First, the followers were defined. The coordinate transformation is as follows: (21)； (22)； in , It is the leader's output; It is the formation synchronization error; It is a virtual error; It is a virtual controller; Step 1: Select the first Lyapunov function as follows: (23)； According to (5) and (21), The time derivative is: (24)； The time derivative is: (25)； Virtual control law The design is as follows: (26)； in and These are design parameters; Substituting (26) into (25) yields: (27)； Step 2: Based on (5), Then, define the function. and According to assumptions 2-3, we know that and It is bounded; therefore, we define the boundary. , , .

2. The method according to claim 1, characterized in that, S2 specifically includes: two adaptive parameters and Defined to estimate and To obtain the estimation error and ; Choose the second Lyapunov function as follows: (28)； in , and For design parameters; yes The estimate; The time derivative is: (29)； in, , ,if , ; Since it is a continuous function, according to Lemma 7, it can be approximated using a fuzzy logic system. And assume, (30)； Among them, approximation error satisfy , ; According to Lemma 5, we obtain... (31)； in It is a positive design parameter. ; Substituting (27) and (31) into (29) yields (32)。 3. The method according to claim 2, characterized in that, S3 specifically includes: A dynamic event-triggered control strategy, triggered by a control signal. When the rate of change is large, a shorter update interval is used to improve the system's stabilization speed; while when the control signal... When the rate of change is small, i.e., when the system state tends to be in equilibrium, a longer update interval is adopted to further save communication resources; the event-triggered control strategy is designed as follows: (33)； (34)； (35)； In the formula It is the controller update time. Indicates the initial time; , , It is a positive design constant; , This indicates the control signal interval between two consecutive triggering moments; Note 2: Whenever (33) is triggered, the control signal It will be applied to the actuator immediately, and the next trigger point will be obtained according to (33). The control signal is at a time interval. The value will remain constant; obviously, this control signal update mechanism reduces the update frequency, thereby saving communication resources. According to (33) and (34), for ,have Furthermore, we obtain , and ;when ,get: (36)； According to Lemma 2 and Lemma 5, the above equation can be rewritten as: (37)； in ; when ,get: (38)； According to Lemma 2 and Lemma 5, the above equation can be rewritten as: (39)； in ; Combining (37) and (39), we get: (40)； Control Law It is designed as follows: (41)； According to Lemma 7, from (41) we can obtain: (42)； Similarly, we can conclude that: (43)； Substituting equations (40), (42), and (43) into equation (32), we get: (44)； in: (45)； Virtual control law The design is as follows: (46)； in , and For design parameters; Substituting (46) into (44) yields: (47)； The three adaptive laws are designed as follows: (48)； (49)； (50)； in , and Positive design parameters; According to Lemma 2, substituting (48)-(50) into (47) yields, (51)； According to Lemma 5, we have: (52)； (53)； (54)； Using Lemma 3, (52)-(54) can be rewritten as: (55)； (56)； (57)； in, ; Substituting equations (55)-(57) into (51), we get: (58)； in, (59)； according to , and The definition is as follows: (60)； (61)； (62)； According to Lemma 5, we have, (63)； (64)； (65)； (66)； (67)； (68)； in ; Combining equations (60) and (68), we get: (69)； (70)； (71)； Substituting (69)-(71) into (58) yields: (72)； in, (73)； According to Lemma 4, we have: (74)； in , , , , , , , ; (74) was rewritten as, (75)； in , ; To solve the position control law, the desired attitude angle needs to be solved first. From equation (2), we can see that: (76)； (77)； (76) can be rewritten as: (78)； Substituting (77) into (78) yields: (79)； From (79), we can obtain: (80)； (81)； The required attitude angle can then be obtained. and for, (82)； (83)； B. Attitude subsystem controller design, A sliding mode controller for attitude trajectory tracking control was designed to make the system tend toward the desired attitude angle while ensuring tracking performance. First, the equation for the attitude angle error of the UAV is defined as follows: (84)； (85)； (86)； The sliding mode function is designed as follows. (87)； (88)； (89)； in , , ; According to (1), (84), (85) and (86), the time derivative of the sliding mode function is, (90)； (91)； (92)； The sliding mode control law is designed as follows: (93)； (94)； (95)； in , , , , , ; The Lyapunov function is selected as follows: (96)； (97)； (98)； Substituting (90)-(95) into (96)-(98) yields , , The time derivatives of and are (99)； (100)； (101)； because , and This ensures the sliding surface , and Exponential convergence, i.e. , and The exponents converge to , and ; Combining (27), (75), (99), (100) and (101), it is shown that the overall system stability is guaranteed, and the position trajectory and attitude angle tracking capabilities are guaranteed.

4. A device for fixed-time formation control of multiple quadcopter unmanned aerial vehicles, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for fixed-time formation control of a multi-quadrotor unmanned aerial vehicle as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an implementation program for information transmission, which, when executed by a processor, implements the steps of the method for fixed-time formation control of a multi-quadrotor unmanned aerial vehicle as described in any one of claims 1 to 3.

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