Hierarchical control method and device for multi-quadrotor UAV formation under unknown disturbance

Abstract

The present invention provides a hierarchical control method and device for a multi-quad-rotor UAV formation under unknown disturbances. The hierarchical control strategy including a lower-layer adaptive sliding mode anti-disturbance trajectory tracking control and an upper-layer distributed cooperative control is adopted. The control implementation process comprises the following steps: according to the set distributed communication network topology of the quad-rotor UAV formation, a UAV motion model and a formation error mathematical model considering internal parameter uncertainty and external disturbance are established in a lower-layer attitude formation controller, a sliding mode function is constructed using the formation error, and compensation inputs under uncertain interference are set; a distributed cooperative algorithm is set in an upper-layer trajectory tracking controller, the values ​​of control parameters that stabilize the system are determined according to the communication topology structure and Lyapunov's theorem, the speed of the reference trajectory on the X, Y and Z axes and the control input of the yaw angular velocity are calculated by the distributed cooperative algorithm, so that the reference trajectory of each UAV is gradually tracked to the state of a virtual navigator.

Description

Technical Field

[0001] The present invention belongs to the field of multi-quadrotor UAV formation, and specifically, relates to a distributed hierarchical control technology solution for a multi-quadrotor UAV formation, comprising two parts: a lower-layer adaptive sliding mode anti-disturbance attitude formation control and an upper-layer reference trajectory distributed collaborative control, to realize a multi-quadrotor UAV self-anti-disturbance formation based on a double-layer control framework. Background Art

[0002] Quadrotors have been widely used in civil and military fields such as power inspection, search and rescue, and reconnaissance and attack due to their small size, convenient operation, and flexible flight. However, due to the limitation of communication capabilities and the superposition of interference between multiple quadrotors, the design, anti-interference and stability analysis of quadrotor formation controllers have become very complex and difficult. It is worth noting that the displacement, velocity and other parameter errors caused by sensor accuracy will affect the control performance, and external interference will further deteriorate the stability of the quadrotor controller. At present, sliding mode and adaptive observer are mainly used to suppress the uncertainty interference problem. In most quadrotor formation controllers based on sliding mode and adaptive observation, it is difficult to achieve high-speed response, accurate tracking and strong robustness at the same time. At present, researchers have also proposed many formation strategies based on the leader-follower method, behavioral strategy and virtual structure. However, the existing quadrotor formation control generally only considers the position formation in the global coordinate system, while ignoring the relative position and yaw angle formation in the body coordinate system. In order to overcome the limitations of quadrotor UAV's communication capabilities, formation control strategies are mostly based on distributed control. However, in existing distributed control methods, the real-time status information of a single quadrotor is still coupled in the formation control loop, which inevitably leads to the formation error of the UAV with deeper nodes in the communication topology.

[0003] In view of the shortcomings of the prior art, the present invention provides a hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances, which solves the problem of step-by-step error accumulation in a large-scale distributed formation of quadrotors and increases the self-anti-disturbance capability of the formation under model uncertainty and external interference. Summary of the invention

[0004] The present invention proposes a hierarchical control technology for the formation of multiple quad-rotor drones, which realizes the distributed coordination of reference trajectories and the tracking control of quad-rotor drone trajectories by establishing a reasonable two-layer control network. The upper layer adopts a distributed coordination algorithm to realize the displacement, yaw angle, linear velocity and angular velocity tracking virtual navigator of all reference trajectories, and the lower layer adopts an adaptive terminal sliding mode anti-disturbance method to realize the formation of quad-rotor drones with the reference trajectory at a set distance, thereby realizing the formation control of multiple quad-rotors under unknown disturbances as a whole.

[0005] In order to solve the above technical problems, the present invention adopts a hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances, which adopts a hierarchical control strategy including a lower-layer adaptive sliding mode anti-disturbance trajectory tracking control and an upper-layer distributed collaborative control. The control implementation process includes the following steps:

[0006] Step 1, setting the distributed communication network topology of the quadrotor UAV formation, setting a hierarchical control structure including an upper-layer trajectory tracking controller and a lower-layer attitude formation controller, setting a corresponding reference trajectory for each quadrotor UAV in the upper-layer trajectory tracking controller, and passing the real-time updated reference trajectory state to the lower-layer attitude formation controller;

[0007] Step 2: In the lower-level attitude formation controller, the reference trajectory and the motion relationship between the physical drone in the body coordinate system and the inertial coordinate system are combined to establish a drone motion model that takes into account internal parameter uncertainty and external disturbances;

[0008] Step 3: Based on the reference trajectory and the motion model of the physical drone, the relationship between the relative distance and angle between the two on the X, Y, and Z axes in the body coordinate system is established, and a mathematical model of the formation error is constructed. The model includes a part that can be accurately represented and a part containing disturbance terms.

[0009] Step 4, adding a sliding mode control strategy to the lower-level attitude formation control algorithm, constructing a sliding mode function using the formation error, and solving the input of the attitude formation controller without considering the interference term based on the sliding mode control theory and the condition when the formation error converges to the sliding mode surface;

[0010] Step 5: According to the disturbance term in the formation error mathematical model, a disturbance observer is constructed, and the disturbance term is characterized by the sum of the constant term and the formation error term. The updating strategy of the disturbance observer is set according to the characterization relationship. Based on the results of the observer and the Lyapunov stability analysis, the compensation input of the attitude formation controller under uncertain interference is set.

[0011] Step 6, set the distributed collaborative algorithm in the upper-level trajectory tracking controller, determine the values ​​of the control parameters that can stabilize the system according to the communication topology and Lyapunov stability analysis, and calculate the control inputs of the reference trajectory in the X, Y, and Z axes and the yaw angular velocity through the distributed collaborative algorithm, so that the reference trajectory of each UAV can gradually track the state of the virtual navigator.

[0012] Moreover, the upper-level trajectory tracking controller uses virtual reference values ​​instead of real values ​​for distributed cooperative control of the formation to avoid error accumulation caused by the increase of node depth in the distributed formation.

[0013] Moreover, in the step 2, in order to reflect the influence of internal parameter uncertainty and external interference on the UAV in practice, a UAV motion model with disturbance term is constructed.

[0014]

[0015] The perturbation term set represents the influence of internal parameter uncertainty and external disturbance on the linear velocity and yaw angular velocity of UAV i in the X, Y, and Z axes, and the displacement set p i =[x i ,y i ,z i ,ψ i ] is the set of displacement and yaw angle of the i-th UAV in the inertial coordinate system X, Y, and Z axes, is the corresponding derivative, the velocity set represents the set of speed and angular velocity of the i-th drone in the inertial coordinate system X, Y, and Z axes, They represent the linear speed of the i-th UAV in the X, Y, and Z axes of the body coordinate system respectively.

[0016] Moreover, in the step 4, a sliding mode control strategy is added to the lower-level attitude formation control algorithm, and the sliding mode function S of the i-th UAV constructed by the formation error is used. i Expressed as

[0017]

[0018] Among them, the control gain k1>0, k2>0, the exponential term coefficient γ1∈(0,1), the function sgn(E i ) is the symbol function, is a power function, where |E i | means take E i The absolute value of, index γ1∈(0,1), is the formation error set, They are the formation error and yaw angle error of the i-th quadrotor drone and its reference trajectory on the X, Y, and Z axes, respectively. The integral term Indicates that for the interval [0,t] Integrate the value of .

[0019] Moreover, in the step 5, the uncertainty disturbance and the formation error E are obtained according to the formation error mathematical model analysis. i The linear relationship between them is established, and the disturbance observer is set up The update strategy is

[0020]

[0021] The update coefficient h 1,i >0,h 2,i >0.

[0022] Moreover, in step 6, the distributed collaborative algorithm is expressed as

[0023]

[0024] where a ij (t), b i (t) are the adjacency matrices and the element of the degree matrix B, j∈N i (t) indicates that drone j belongs to the neighbor set N of drone i i (t), is the reference displacement set of UAV j, is the reference velocity set of UAV j, control gain parameter γ>1, k>0, set p0=[x0,y0,z0,ψ0] and are the displacements and yaw angles of the virtual navigator and the reference trajectory i on the X, Y, and Z axes, respectively. and are the speeds and yaw angular velocities of the virtual navigator and the reference trajectory i on the X, Y, and Z axes, respectively, and the acceleration set of the virtual navigator And there is gather It is the control input of the reference trajectory in the X, Y, Z axis speed and yaw angular velocity.

[0025] Moreover, in step 6, in order to stabilize the distributed collaborative controller, the value of the controller parameter k is set as:

[0026]

[0027] The matrix H = L + B, L is the Laplace matrix of the formation communication topology, For the matrix The minimum eigenvalue of .

[0028] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described above is implemented.

[0029] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described above is implemented.

[0030] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbance as described above.

[0031] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: the hierarchical formation control method of the embodiments of the present invention is based on a two-layer network control architecture. The upper-layer distributed collaborative algorithm uses virtual reference values ​​to generate reference trajectories for each quadrotor, thereby avoiding the problem of error accumulation in large-scale distributed formations of quadrotors. The proposed lower-layer attitude formation controller takes into account the formation of relative position and yaw angle in the body coordinate system, ensuring that the relative position in the direction of motion remains unchanged and the yaw angle is consistent during formation flight. The constructed sliding mode function and adaptive interference observer improve the convergence speed and robustness of the quadrotor formation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of a hierarchical control framework proposed in this embodiment.

[0033] Figure 2 It is a schematic diagram of the piloting and following in the body coordinate system designed in the embodiment of the present invention.

[0034] Figure 3 It is a schematic diagram of the gazebo semi-physical simulation platform according to an embodiment of the present invention.

[0035] Figure 4 It is a communication topology diagram of an embodiment of the present invention.

[0036] Figure 5 Schematic diagram of experimental results of distributed reference trajectories according to an embodiment of the present invention.

[0037] Figure 6 Schematic diagram of tracking error of a reference trajectory according to an embodiment of the present invention.

[0038] Figure 7 Schematic diagram of a speed curve of a reference trajectory according to an embodiment of the present invention.

[0039] Figure 8 It is a schematic diagram of the trajectory of a multi-quadrotor UAV formation according to an embodiment of the present invention.

[0040] Fig. 9 Schematic diagram of the error curve of the multi-quadrotor UAV formation according to an embodiment of the present invention.

[0041] Fig.10 Schematic diagram of the linear velocity curve of the multi-quadrotor UAV according to an embodiment of the present invention.

[0042] Fig.11It is a schematic diagram of the yaw angle, angular velocity and angular error curves of the multi-quadrotor drone according to an embodiment of the present invention.

[0043] Fig.12 It is a schematic diagram of the formation error curve of the existing method. DETAILED DESCRIPTION

[0044] The present invention proposes a distributed hierarchical control system architecture for a formation of multiple quadrotors, in which the lower layer uses an adaptive terminal sliding mode interference suppression method to achieve attitude formation, and the upper layer uses a distributed collaborative algorithm to generate reference trajectories for the lower layer, thereby realizing the formation control of multiple quadrotors under unknown disturbances.

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the invention.

[0046] The embodiment of the present invention provides a new method for quadrotor drone formation. A distributed hierarchical control framework is designed for multiple quadrotor drones, including a lower-layer adaptive terminal sliding mode anti-interference method and an upper-layer distributed collaborative algorithm. Specifically, the adaptive sliding mode control strategy can solve the problem of quadrotor drones resisting external disturbances in tracking reference signals to form a formation, and increase the robustness of the formation. The distributed collaborative controller can avoid the problem of step-by-step accumulation of formation errors in large-scale distributed formations, and improve the accuracy of quadrotor drone formation control. Multiple quadrotor drones were simulated using the physical simulation platform Gazebo to verify the effectiveness of the solution.

[0047] See also Figure 1 The hierarchical control method of a multi-quadrotor UAV formation under unknown disturbance of an embodiment of the present invention comprises the following steps:

[0048] Step 1: Set up the distributed communication network topology of the quadrotor UAV formation. To avoid the error accumulation caused by the increase of node depth in the distributed formation, set up a hierarchical control structure including an upper-level trajectory tracking controller and a lower-level attitude formation controller. In the upper-level trajectory tracking controller, set the corresponding reference trajectory for each quadrotor UAV, and pass the real-time updated reference trajectory state to the lower-level attitude formation controller.

[0049] Specifically, the communication network topology is the communication relationship between drone nodes. In the embodiment, only one follower drone communicates with the virtual navigator, and other follower drones only communicate with their neighboring drones.

[0050] The present invention further proposes that the upper-level trajectory tracking controller uses virtual reference values ​​instead of real values ​​for distributed collaborative control of the formation, thereby avoiding error accumulation caused by increased node depth in the distributed formation.

[0051] Step 2: In the lower-level attitude formation controller, the reference trajectory and the motion relationship between the physical drone in the body coordinate system and the inertial coordinate system are combined to establish a drone motion model that takes into account internal parameter uncertainty and external disturbances. The specific process is as follows:

[0052] According to the hierarchical control structure proposed by the present invention, each quadrotor drone needs to track its own reference trajectory with the expected deviation in the body coordinate system to achieve attitude formation. Let the set Γ = {1,...,n}, n is the number of quadrotors in the formation, are the reference displacement and reference yaw angle of the i-th UAV in the inertial coordinate system X, Y, and Z axes, respectively. Let the reference displacement set Reference speed set is the reference speed and reference angular velocity of the i-th drone in the inertial coordinate system X, Y, and Z axes. Speed ​​set is the reference linear velocity of the X, Y, and Z axes of the i-th UAV in the body coordinate system. Figure 2 The reference trajectory and the physical UAV kinematic model combining the body coordinate system and the inertial coordinate system are established. In order to reflect the influence of the internal parameter uncertainty and external interference of the UAV in practice, the UAV motion model considering the disturbance term is:

[0053]

[0054] The perturbation term set represents the influence of internal parameter uncertainty and external disturbance on the linear velocity and yaw angular velocity of UAV i in the X, Y, and Z axes, and the displacement set p i =[x i ,y i ,z i ,ψ i ] is the set of displacement and yaw angle of the i-th UAV in the inertial coordinate system X, Y, and Z axes, is the corresponding derivative, the velocity set represents the set of speed and angular velocity of the i-th drone in the inertial coordinate system X, Y, and Z axes, They represent the linear speed of the i-th UAV in the X, Y, and Z axes of the body coordinate system respectively.

[0055] Step 3: In order for the lower-level attitude formation controller to realize the formation of relative position and yaw angle in the body coordinate system, so that the relative position and yaw angle of the leading UAV and the following UAV in the direction of motion remain unchanged, the relationship between the formation distance between the two in the body coordinate system is established according to the reference trajectory and the motion model of the physical UAV, and the mathematical model of the formation error is constructed. are the relative distance and angle of the i-th UAV on the X, Y, and Z axes of its reference trajectory, is the desired formation distance. The formation distance in the body coordinate system of the present invention can be expressed as

[0056]

[0057] The formation errors between the i-th quadrotor drone and its reference trajectory on the X, Y, and Z axes are defined as And yaw angle error Formation Error Set In order to analyze the stability of the formation system, E i The mathematical model of formation error is obtained by derivative.

[0058] Step 4: In order to enhance the response speed of the formation system and the robustness of the system, the present invention adds a sliding mode control strategy to the lower-level attitude formation control algorithm. According to the sliding mode control theory, the condition when the formation error converges to the sliding mode surface is used to solve the input of the attitude formation controller without considering the interference term.

[0059] In the embodiment, the sliding mode function S of the i-th UAV constructed using the formation error is i for

[0060]

[0061] Among them, the control gain k1>0, k2>0, the function sgn(E i ) is the symbol function, is a power function, where |E i | means take E i The absolute value of, the exponent γ1∈(0,1), the integral term Indicates that for the interval [0,t] Integrate the value of .

[0062] Let the sliding mode function set make is the speed and angular velocity control input of the drone on the X, Y, and Z axes. According to the sliding mode control theory, E i Converges to the sliding surface S i =0 and When , solve the control input without considering the interference term It can be expressed as

[0063]

[0064] Among them, H i and F i is the coefficient matrix in the formation error mathematical model in step 3.

[0065] Step 5, in order to reduce the impact of uncertain interference on the UAV formation, improve the robustness of the formation and reduce the formation error, the present invention constructs a disturbance observer based on the disturbance term in the formation error mathematical model, characterizes the disturbance term with the sum of the constant term and the formation error term, sets the update strategy of the disturbance observer according to the characterization relationship, and sets the compensation input of the attitude formation controller under uncertain interference based on the observer result and Lyapunov stability analysis;

[0066] Embodiment Designing Compensated Control Input Based on Observer Results Let the set of perturbations be represents the influence of internal parameter uncertainty and external disturbance on the formation distance and yaw angle of UAV i in the X, Y, and Z axes. The disturbance term D can be analyzed from the formation error mathematical model in step 2. i Bounded and available constant term C 1,i and the error term C 2,i |E i | is represented by the sum of Designing a disturbance observer The update strategy is

[0067]

[0068] The update coefficient h 1,i >0,h 2,i > 0, further use the results of the observer, for the interference term D i Design Compensation Input for

[0069]

[0070] Among them, the control gains b1, b2>0, and the exponential term coefficient γ2∈(0,1). According to Lyapunov stability analysis, in the total control input Under this condition, the UAV formation error tends to 0.

[0071] Step 6, in order to generate a reference trajectory for each following quadrotor UAV in the upper-level trajectory tracking controller, the present invention sets a distributed collaborative algorithm in the upper-level trajectory tracking controller, determines the values ​​of control parameters that can stabilize the system according to the communication topology and Lyapunov stability analysis, and calculates the control inputs of the reference trajectory in the X, Y, and Z axes and the yaw angular velocity through the distributed collaborative algorithm, so that the reference trajectory of each UAV progressively tracks the state of the virtual navigator.

[0072] The embodiment of the present invention proposes a distributed cooperative control algorithm in the upper-layer trajectory tracking control, so that each reference trajectory tracks the state of the virtual navigator. Let x0, y0, z0, ψ0 be the displacement of the virtual navigator on the X, Y, and Z axes and the yaw angle, respectively. The displacement set p0 = [x0, y0, z0, ψ0], ω0 are the speed of the virtual navigator on the X, Y, and Z axes and the yaw angular velocity, and the speed set Acceleration Collection And there is make is the control input of the reference trajectory in the X, Y, and Z axis speeds and the yaw angular velocity. Based on this, the distributed collaborative algorithm proposed in the present invention can be expressed as

[0073]

[0074] where a ij (t), b i (t) are the adjacency matrices and the element of the degree matrix B, j∈N i (t) indicates that drone j belongs to the neighbor set N of drone i i (t), is the reference displacement set of UAV j, is the reference velocity set of UAV j, the control gain parameter γ>1, k>0, let L be the Laplace matrix of the formation communication topology, H=L+B,

[0075] In order to make the proposed distributed cooperative controller stable, the value of the controller parameter k is set as follows:

[0076]

[0077] in For the matrix As long as the value of k satisfies this condition, the distributed collaborative algorithm trajectory tracking controller proposed in the present invention can achieve asymptotic stability, that is, the reference trajectory of each UAV gradually tracks the state of the virtual navigator.

[0078] In such Figure 3The PX4 quadrotor model is used on the gazebo semi-physical simulation platform shown in the figure to verify the effectiveness of the formation controller mentioned in the above embodiment, and the distributed communication topology of the drone formation is set as follows Figure 4 As shown, UAV0 is a virtual navigator, and UAV1-UAV6 are follower drones.

[0079] In order to evaluate the effectiveness and innovation of the proposed control strategy, some comparative tests were carried out based on the implementation scheme, and attempts were made to compare the convergence speed and pilot-following capability of the distributed collaborative controller, the formation keeping capability and robust performance of the adaptive sliding mode formation controller with the performance of the existing quadrotor formation controller.

[0080] The parameters are γ=1.5, k=6, The formation control parameters are k1=0.1, k2=0.1, γ1=0.6, γ2=0.9, the parameters of the adaptive observer are b1=0.5, b2=0.8, b1=0.5, b2=0.8, and the trajectory of the virtual navigator is ω0=0.2rad / s, The initial state of the reference trajectory is

[0081] A. Performance Testing of Distributed Collaborative Controllers

[0082] The reference trajectory generated by the upper-level distributed collaborative controller is as follows: Figure 5 As shown in Figure 2, the tracking error with the virtual navigator is Figure 6 As shown, the speed changes as Figure 7 As shown in Figure 2, it can be seen that the position and velocity errors of all reference trajectories converge to zero quickly within 15 seconds.

[0083] B. Adaptive Sliding Mode Formation Controller Test

[0084] The performance of the quadrotor formation using the adaptive sliding mode formation controller of the present invention is as follows: Figure 8 As shown in Fig. 9 As shown, the line speed is Fig.10 As shown, the yaw angle, angular velocity and angle error curves of the quadrotor are as follows Fig.11 The experiment shows that through the distributed cooperative controller and the adaptive sliding mode formation controller, the multi-quadrotor UAV has excellent formation performance.

[0085] C. Comparative experiment with existing formation controller

[0086] In order to verify the advantages of the controller of the present invention, the existing method is used to realize the above-mentioned multi-quadrotor UAV formation control. The results are as follows Fig.12The experimental results show that the multi-quadrotor formation controller proposed in the present invention has a faster convergence speed and a smaller formation error.

[0087] In specific implementation, the method proposed in the technical solution of the present invention can be implemented by technical personnel in this field using computer software technology to realize automatic operation process. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of the present invention and computer equipment running the corresponding computer program, should also be within the scope of protection of the present invention.

[0088] In another embodiment, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described above is implemented.

[0089] In another embodiment, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described above is implemented.

[0090] In another embodiment, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described above.

[0091] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances, characterized in that: A hierarchical control strategy is adopted, which includes the lower-level adaptive sliding mode anti-disturbance trajectory tracking control and the upper-level distributed cooperative control. The control implementation process includes the following steps: Step 1, setting the distributed communication network topology of the quadrotor UAV formation, setting a hierarchical control structure including an upper-layer trajectory tracking controller and a lower-layer attitude formation controller, setting a corresponding reference trajectory for each quadrotor UAV in the upper-layer trajectory tracking controller, and passing the real-time updated reference trajectory state to the lower-layer attitude formation controller; Step 2: In the lower-level attitude formation controller, the reference trajectory and the motion relationship between the physical drone in the body coordinate system and the inertial coordinate system are combined to establish a drone motion model that takes into account internal parameter uncertainty and external disturbances; Step 3: Based on the reference trajectory and the motion model of the physical drone, the relationship between the relative distance and angle between the two on the X, Y, and Z axes in the body coordinate system is established, and a mathematical model of the formation error is constructed. The model includes a part that can be accurately represented and a part containing disturbance terms. Step 4: Add the sliding mode control strategy to the lower-level attitude formation control algorithm and construct the sliding mode function S using the formation error. i , according to the sliding mode control theory, the input of the attitude formation controller without considering the interference term is solved using the condition when the formation error converges to the sliding mode surface; Step 5: According to the disturbance term in the formation error mathematical model, a disturbance observer is constructed, and the disturbance term is characterized by the sum of the constant term and the formation error term. The updating strategy of the disturbance observer is set according to the characterization relationship. Based on the results of the observer and the Lyapunov stability analysis, the compensation input of the attitude formation controller under uncertain interference is set. Step 6: Set the distributed collaborative algorithm in the upper-layer trajectory tracking controller, determine the values ​​of the control parameters that can stabilize the system according to the communication topology and Lyapunov stability analysis, and calculate the control inputs of the reference trajectory in the X, Y, and Z axes and the yaw angular velocity through the distributed collaborative algorithm, so that the reference trajectory of each UAV gradually tracks the state of the virtual navigator; In step 5, the uncertainty disturbance and formation error E are obtained according to the formation error mathematical model analysis. i The linear relationship between them is established, and the disturbance observer is set up The update strategy is The update coefficient h 1,i >0,h 2,i >0; In step 6, the distributed collaborative algorithm is expressed as where a ij (t), b i (t) are the adjacency matrices and the element of the degree matrix B, j∈N i (t) indicates that drone j belongs to the neighbor set N of drone i i (t), is the reference displacement set of UAV j, is the reference velocity set of UAV j, control gain parameter γ>1, k>0, set p0=[x0,y0,z0,ψ0] and are the displacements and yaw angles of the virtual navigator and the reference trajectory i on the X, Y, and Z axes, respectively. and are the speeds and yaw angular velocities of the virtual navigator and the reference trajectory i on the X, Y, and Z axes, respectively, and the acceleration set of the virtual navigator And there is gather It is the control input of the reference trajectory in the X, Y, Z axis speed and yaw angular velocity; In step 6, in order to stabilize the distributed collaborative controller, the value of the controller parameter k is set as: The matrix H = L + B, L is the Laplace matrix of the formation communication topology, For the matrix The minimum eigenvalue of .

2. The hierarchical control method for a multi-quadrotor UAV formation under unknown disturbance according to claim 1 is characterized in that: The upper-level trajectory tracking controller uses virtual reference values ​​instead of real values ​​for distributed cooperative control of the formation to avoid error accumulation caused by the increase of node depth in the distributed formation.

3. The hierarchical control method for a multi-quadrotor UAV formation under unknown disturbance according to claim 1 is characterized in that: In step 2, in order to reflect the influence of internal parameter uncertainty and external interference on the actual UAV, a UAV motion model with disturbance terms is constructed. The perturbation term set represents the influence of internal parameter uncertainty and external disturbance on the linear velocity and yaw angular velocity of UAV i in the X, Y, and Z axes, and the displacement set p i =[x i ,y i ,z i ,ψ i ] is the set of displacement and yaw angle of the i-th UAV in the inertial coordinate system X, Y, and Z axes, is the corresponding derivative, the velocity set represents the set of speed and angular velocity of the i-th drone in the inertial coordinate system X, Y, and Z axes, They represent the linear speed of the i-th UAV in the X, Y, and Z axes of the body coordinate system respectively.

4. The hierarchical control method for a multi-quadrotor UAV formation under unknown disturbance according to claim 3 is characterized in that: In step 4, a sliding mode control strategy is added to the lower-level attitude formation control algorithm, and the sliding mode function S of the i-th UAV constructed by the formation error is used. i Expressed as Among them, the control gain k1>0, k2>0, the exponential term coefficient γ1∈(0,1), the function sgn(E i ) is the symbol function, is a power function, where |E i | means take E i The absolute value of, index γ1∈(0,1), is the formation error set, They are the formation error and yaw angle error of the i-th quadrotor drone and its reference trajectory on the X, Y, and Z axes, respectively. The integral term Indicates that for the interval [0,t] Integrate the value of .

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the hierarchical control method for a multi-quadrotor UAV formation under unknown disturbance as described in any one of claims 1 to 4 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described in any one of claims 1 to 4 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, a hierarchical control method for a multi-quadrotor UAV formation under unknown disturbances as described in any one of claims 1 to 4 is implemented.

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