Passive sliding mode method for unmanned aerial vehicle formation distributed control

Abstract

The embodiment of the invention provides a passive sliding mode method for unmanned aerial vehicle formation distributed control, and belongs to the technical field of control, and the method specifically comprises the steps: obtaining the intermediate state quantity of a lead aircraft under the action of a preset control quantity, and taking the initial state quantity of a wing aircraft as the intermediate state quantity of the wing aircraft at an initial moment; according to a preset communication model and the topological structure, obtaining state quantity estimation of the wing aircrafts to the lead aircrafts based on a consistency estimation algorithm; based on the distributed formation control error model, the distributed formation error amount of the wing aircraft is obtained; according to a port Hamiltonian system theory, defining a Hamiltonian energy function, converting the unmanned aerial vehicle motion model into a port Hamiltonian system form, and further deriving to obtain the port Hamiltonian system form of the unmanned aerial vehicle formation system; according to the port Hamiltonian system form, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, and the passive sliding mode control quantity of the wing aircraft is deduced. Through the scheme disclosed by the invention, the stability and the disturbance resistance are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of control, in particular to a passive sliding mode method for unmanned aerial vehicle formation distributed control. BACKGROUND

[0002] In recent years, unmanned aerial vehicles (UAVs) play an increasingly important role in military applications and civilian applications. In today's society, UAVs are not only widely used in traditional military applications such as long-range reconnaissance and precision strikes, but also increasingly used in civilian applications such as agricultural irrigation and logistics transportation. UAV swarm systems have the advantages of high task execution efficiency, high economic value, good fault tolerance and robustness, and thus have become an important development trend in the application of UAV systems. UAV formation distributed control is an important topic in the field of UAV swarm systems and is also a research hotspot.

[0003] For UAV formation distributed control, commonly used control methods include backstepping control, model predictive control and sliding mode control. The backstepping control method introduces virtual control inputs and asymptotically approximates the system state to gradually design the control law at each stage, thereby achieving global stability of the system. The backstepping control method requires an accurate system model and the design process is very complex. The model predictive control method can predict the future behavior of the system within a given time window based on the system model, thereby making optimal decisions. The model predictive control method can well consider system constraints, but has a large optimization calculation burden and poor real-time performance. The sliding mode control method introduces a sliding surface and constrains the system state on the sliding surface, thereby making the closed-loop system stably converge along the sliding surface. The sliding mode control method has certain robustness to external disturbances, but has a chattering phenomenon.

[0004] It can be seen that there is an urgent need for a passive sliding mode method for UAV formation distributed control with high stability and disturbance rejection. SUMMARY

[0005] Therefore, embodiments of the present disclosure provide a passive sliding mode method for UAV formation distributed control, at least partially solving the problems of stability and disturbance rejection in the prior art.

[0006] Embodiments of the present disclosure provide a passive sliding mode method for UAV formation distributed control, comprising:

[0007] Step 1, according to the UAV motion model and the preset initial state quantity of the lead aircraft, an intermediate state quantity of the lead aircraft is obtained under the action of a preset control quantity, an initial state quantity of the wing aircraft is set, and the initial state quantity of the wing aircraft is taken as the intermediate state quantity of the wing aircraft at the initial time;

[0008] Step 2, according to the preset communication model and topology structure, based on a consistency estimation algorithm, obtaining the state quantity estimation of the wingman to the leader;

[0009] Step 3, according to the intermediate state quantity of the leader, the intermediate state quantity of the wingman and the estimated state quantity of the wingman to the leader, based on a distributed formation control error model, obtaining the distributed formation error quantity of the wingman;

[0010] Step 4, according to the port Hamilton system theory, based on the distributed formation error quantity, defining a Hamilton energy function, thereby converting the unmanned aerial vehicle motion model into a port Hamilton system form, and further deducing the port Hamilton system form of the unmanned aerial vehicle formation system;

[0011] Step 5, according to the port Hamilton system form of the unmanned aerial vehicle formation system, introducing a sliding mode quantity into the Hamilton energy function of the system, and further deducing the passive sliding mode control quantity of the wingman and inputting the unmanned aerial vehicle motion model to obtain the intermediate state quantity of the wingman at the next moment.

[0012] According to a specific implementation manner of the embodiment of the present disclosure, the step 1 specifically comprises:

[0013] Step 1.1, setting the expression of the unmanned aerial vehicle motion model as ; ; wherein, indicates the wingman serial number; respectively indicate the three-dimensional position coordinates of the first wingman in the northeast celestial coordinate system; respectively indicate the velocity scalar, the heading angle and the track angle of the first wingman; respectively indicate the velocity command, the heading angle command and the track angle command of the first wingman; respectively indicate the time inertia constants of the autopilot velocity, the heading angle and the track angle channels, and the units are all ; respectively indicate the minimum flight speed and the maximum flight speed; indicates the maximum acceleration; respectively indicate the minimum track angle and the maximum track angle; indicates the maximum angular velocity;

[0014] Step 1.2, based on the preset initial state quantity of the leader, inputting the preset control quantity into the unmanned aerial vehicle motion model to obtain the intermediate state quantity of the leader;

[0015] Step 1.3, set the initial state of the wingman, and use the initial state of the wingman as the intermediate state of the wingman at the initial moment, and set the current moment The intermediate state of the wingman is Calculation of the intermediate state quantity of the wingman.

[0016] According to a specific implementation of the embodiment of the present disclosure, the expression of the consistency estimation algorithm is: ; in, Indicates the The state vector of the virtual leader estimated by the wingman according to the consistency estimation algorithm; Represents the state vector of the virtual leader itself; Indicates the The communication relationship between the wingman and the Indicates that the two wingmen are communicating with each other. It means there is no communication between the two; Indicates the The degree of dependency between the wingmen; Indicates A collection of wingmen that communicate with each other.

[0017] According to a specific implementation of the embodiment of the present disclosure, the expression of the distributed formation control error model is: ; in, Indicates the The formation error vector of the wingman; Indicates the The desired relative state vector between the wingman and the virtual leader; Indicates the The desired relative state vector between the two wingmen; Indicates the state error between the wingman itself and the virtual leader. Represents the sum of the intermediate state errors between the wingman itself and all its neighbor wingmen.

[0018] According to a specific implementation of the embodiment of the present disclosure, step 4 specifically includes:

[0019] Step 4.1, describe the UAV motion model as the following general nonlinear system expression: ; in, , is the state quantity of the wingman; , is a unit column vector; , is the control amount of the wingman; ; ; Further, for any deputy , it is converted into the following port Hamilton form: ; wherein, is the state variable of the deputy; is the control variable of the deputy; is the interconnection matrix, satisfying the anti-symmetry property; is the damping matrix, satisfying the positive definite property; is the Hamilton energy function The partial derivative of the state , ; ; ; ; ; Step 4.2, according to the distributed formation error variable, the Hamilton energy function is defined as: ; Step 4.3, let , according to the port Hamilton system theory, the port Hamilton system form of the UAV formation system is derived: ; ; .

[0020] According to a specific implementation manner of the embodiment of the present disclosure, the step 5 specifically comprises:

[0021] Step 5.1, according to the port Hamilton system form of the UAV formation system, the sliding mode variable is introduced into the Hamilton energy function: ; ; Step 5.2, according to the port Hamilton system theory, the passive sliding mode control variable of the deputy is obtained: ; Step 5.3, the passive sliding mode control variable of the deputy is input into the UAV motion model to obtain the intermediate state variable of the deputy at the next moment.

[0022] The passive sliding mode scheme for the unmanned aerial vehicle formation distributed control in the embodiment of the present disclosure comprises: step 1, according to the unmanned aerial vehicle motion model and the preset initial state quantity of the long machine, the intermediate state quantity of the long machine is obtained under the action of the preset control quantity, the initial state quantity of the deputy machine is set, and the initial state quantity of the deputy machine is taken as the intermediate state quantity of the deputy machine at the initial time; step 2, according to the preset communication model and the topological structure, the state quantity estimation of the deputy machine to the long machine is obtained based on the consistency estimation algorithm; step 3, according to the intermediate state quantity of the long machine, the intermediate state quantity of the deputy machine and the estimated state quantity of the deputy machine to the long machine, the distributed formation error quantity of the deputy machine is obtained based on the distributed formation control error model; step 4, according to the port Hamilton system theory, the Hamilton energy function is defined based on the distributed formation error quantity, thereby the unmanned aerial vehicle motion model is converted into the form of the port Hamilton system, and the port Hamilton system form of the unmanned aerial vehicle formation system is further derived; step 5, according to the port Hamilton system form of the unmanned aerial vehicle formation system, the sliding mode quantity is introduced into the Hamilton energy function of the system, and then the passive sliding mode control quantity of the deputy machine is derived and input into the unmanned aerial vehicle motion model to obtain the intermediate state quantity of the deputy machine at the next time.

[0023] The beneficial effects of the embodiment of the present disclosure are: through the scheme of the present disclosure, the unmanned aerial vehicle formation system is directly modeled under the port Hamilton framework for the unmanned aerial vehicle motion model, which can fully reflect the nonlinear characteristics of the unmanned aerial vehicle formation model; the control quantity of the deputy machine in the unmanned aerial vehicle formation system is directly designed from the energy angle under the port Hamilton framework, which is helpful to use the existing energy shaping technology; the expected Hamilton energy function is defined based on the distributed formation error model, and the sliding mode quantity is introduced into the Hamilton energy function, so that the designed control quantity of the deputy machine can not only ensure the stability of the unmanned aerial vehicle formation system, but also retain a certain robustness to the disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without creative labor.

[0025] Figure 1 A flowchart of a passive sliding mode method for unmanned aerial vehicle formation distributed control provided by the embodiment of the present disclosure; Figure 2An error amount diagram of a wingman in a UAV formation system is provided for the embodiments of the present disclosure, wherein (a) is a north distance error of the wingman in the UAV formation system, (b) is an east distance error of the wingman in the UAV formation system, (c) is a height distance error of the wingman in the UAV formation system, (d) is a speed error of the wingman in the UAV formation system, (e) is a heading angle error of the wingman in the UAV formation system, and (f) is a track angle error of the wingman in the UAV formation system; Figure 3 A sliding mode amount component variation diagram of a passive sliding mode method is provided for the embodiments of the present disclosure, wherein (a) is a sliding mode amount component 1 variation curve of the passive sliding mode method, (b) is a sliding mode amount component 2 variation curve of the passive sliding mode method, and (c) is a sliding mode amount component 3 variation curve of the passive sliding mode method; Figure 4 A state bounded variation curve diagram of a passive sliding mode method under disturbance is provided for the embodiments of the present disclosure, wherein (a) to (e) are state bounded variation curves of different wingmen under disturbance. DETAILED DESCRIPTION

[0026] The embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0027] The embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0028] It should be noted that various aspects of the embodiments described below are within the scope of the appended claims. It should be apparent that the aspects described herein can be implemented in a wide variety of forms and that any specific structure and / or function described herein is merely illustrative. Based on the teachings provided herein one skilled in the art will be able to devise alternative implementations without departing from the scope of the present disclosure. For example, the various aspects described herein can be implemented across many disparate software and hardware systems. Any process descriptions or blocks

[0029] It is also necessary to note that the diagrams provided in the following embodiments only illustrate the basic concepts of the present disclosure in a schematic manner, and only show the components related to the present disclosure, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be a random change in type, number and proportion, and the component layout may also be more complex.

[0030] In addition, in the following description, specific details are provided in order to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the aspects can be practiced without these specific details.

[0031] The embodiments of the present disclosure provide a passive sliding mode method for unmanned aerial vehicle formation distributed control, which can be applied to unmanned aerial vehicle formation control process in scenarios such as logistics transportation, agricultural irrigation, and remote reconnaissance.

[0032] Referring to Figure 1 A flowchart of a passive sliding mode method for unmanned aerial vehicle formation distributed control provided by the embodiments of the present disclosure is shown. As Figure 1 shown, the method mainly includes the following steps:

[0033] Step 1, according to the unmanned aerial vehicle motion model and the preset initial state quantity of the long machine, the intermediate state quantity of the long machine is obtained under the action of the preset control quantity, the initial state quantity of the deputy machine is set, and the initial state quantity of the deputy machine is taken as the initial state quantity of the deputy machine at the initial time;

[0034] In actual implementation, the unmanned aerial vehicle formation system includes one virtual long machine and multiple deputy machines. In some embodiments of the present disclosure, the initial state quantity of the virtual long machine and the deputy machine can be given an initial value according to human experience when determining the flight formation motion model.

[0035] In the present embodiment, a fixed-wing unmanned aerial vehicle nonlinear motion model in a three-dimensional space is adopted, and its specific expression is as follows: wherein, indicates the deputy machine number; respectively indicate the three-dimensional position coordinates of the first deputy machine in the northeast celestial coordinate system; respectively indicate the speed scalar, heading angle and track angle of the first deputy machine; respectively indicate the speed command, heading angle command and track angle command of the first deputy machine; respectively indicate the time inertia constants of the autopilot speed, heading angle and track angle channels, and the units are all . ​

[0036] In actual UAV systems, due to factors such as aerodynamic performance and power system, the UAV needs to meet certain constraints. In this embodiment, five state constraints are considered, including speed, acceleration, track angle, track angle velocity, and heading angle velocity. The specific constraints are: ; in, Respectively represent the minimum flight speed and the maximum flight speed; Indicates the maximum acceleration; Represent the minimum track angle and the maximum track angle respectively; Indicates the maximum angular velocity.

[0037] According to the above motion model, the control amount is set in advance. , we can get the intermediate state quantity of the long machine .

[0038] The control quantity of the wingman needs to be determined by the passive sliding mode method provided by the present invention. Once the control quantity is determined, according to the above-mentioned UAV motion model, the intermediate state quantity of the wingman can be obtained as ,in, is the wingman serial number. During initialization, you can first set the wingman initial state quantity, and use the wingman initial state quantity as the intermediate state quantity of the wingman at the initial moment, and set the current moment The intermediate state of the wingman is Calculation of the intermediate state quantity of the wingman.

[0039] Step 2: Based on the preset communication model and topology, and using a consistency estimation algorithm, the wingman's state estimation of the leader is obtained.

[0040] In specific implementations, a distributed control approach is employed. In this distributed control approach, only some wingmen in the cluster maintain communication with the virtual leader, while others only communicate with their neighboring drones. To achieve distributed formation control under this incomplete communication scenario, this embodiment employs a consistency estimation algorithm, allowing drones unable to communicate with the virtual leader to indirectly estimate the virtual leader's state information based on their neighbors' state information. The specific expression of the consistency estimation algorithm is as follows: ; in, Indicates the The state vector of the virtual leader estimated by the wingman according to the consistency estimation algorithm; Represents the state vector of the virtual leader itself; Indicates the The communication relationship between the wingman and the Indicates that the two wingmen are communicating with each other. indicates that there is no communication between them; indicates the degree of dependence between the aircraft and the virtual leader; indicates the set of wingmen that are in communication with the aircraft.

[0041] Then, a reasonable communication topology is set in the embodiment, and the intermediate state quantity of the wingmen to the leader is obtained according to the above algorithm. It should be noted that the reasonable communication topology means that there is a reachable communication topology from any wingman to the virtual leader, so that the estimation of the wingmen to the leader can be ensured to converge to the .

[0042] In step 3, the distributed formation error quantity of the wingmen is obtained based on the distributed formation control error model according to the intermediate state quantity of the leader, the intermediate state quantity of the wingmen and the estimation state quantity of the wingmen to the leader.

[0043] In the distributed control, the unmanned aerial vehicle that cannot directly communicate with the virtual leader solves the control quantity according to the state error between itself and the neighbor unmanned aerial vehicle. Therefore, the distributed formation error model in the embodiment is divided into two parts: the first part is the state error between the wingman itself and the virtual leader . For the wingman that can directly communicate with the virtual leader, the intermediate state quantity of the virtual leader can be directly obtained. For the wingman that cannot directly communicate with the virtual leader, the intermediate state quantity of the virtual leader is estimated according to the consensus algorithm. The second part is the intermediate state quantity error and the .

[0044] The specific expression of the distributed formation error model is as follows: ; wherein, indicates the formation error vector of the aircraft; indicates the expected relative state vector between the aircraft and the virtual leader; indicates the expected relative state vector between the aircraft.

[0045] According to the intermediate state quantity of the leader, the intermediate state quantity of the wingmen and the estimation intermediate state quantity of the wingmen to the leader, the distributed formation error quantity of the wingmen in the embodiment can be obtained based on the above distributed formation error model.

[0046] Step 4, according to the port Hamilton system theory, a Hamilton energy function is defined based on the distributed formation error quantity, thus the UAV motion model is converted into the form of the port Hamilton system, and the port Hamilton system form of the UAV formation system is further derived;

[0047] For example, the UAV 3-DOF nonlinear motion model can be described as the following general nonlinear system expression: ; wherein, is the state quantity of the wingman; , is a unit column vector; is the control quantity of the wingman; ; ; Further, for any wingman , it can be converted into the following port Hamilton form: ; wherein, is the state quantity of the wingman; is the control quantity of the wingman; is the interconnection matrix, satisfying the anti-symmetry property; is the damping matrix, satisfying the positive definite property; is the Hamilton energy function , which is the partial derivative of the state . ; ; ; ; ; According to the distributed formation error quantity, the Hamilton energy function is defined as follows in the embodiment: ; In particular, let , according to the port Hamilton system theory, the UAV formation system in the following form can be further derived: ; ; .

[0048] Step 5, according to the port Hamilton system form of the UAV formation system, the sliding mode quantity is introduced into the Hamilton energy function of the system, and the passive sliding mode control quantity of the wingman is derived and input into the UAV motion model to obtain the intermediate state quantity of the wingman at the next time.

[0049] In the specific implementation, according to the port Hamiltonian system form of the UAV formation system, the following sliding modulus is introduced into the Hamiltonian energy function: ; ; Furthermore, according to the port Hamiltonian system theory, the passive sliding mode control quantity of the wingman is obtained: ; In order to verify the effectiveness of the passive sliding mode method for UAV formation control provided by the present invention, a simulation experiment was conducted on the method in an embodiment of the present invention, and the process is as follows: In this embodiment, the simulation experiment is carried out by taking the UAV formation moving along a straight trajectory as an example. Consider a UAV formation system consisting of 5 wingmen and 1 virtual leader. Take separately For each UAV’s state constraint, select , , , , , The five wingmen satisfy the following incomplete communication topology: ; Two of the wingmen communicated directly with the virtual leader, namely UAV1 and UAV5.

[0050] In this embodiment, the initial state of the virtual leader is ,The initial states of the other five wingmen are shown in Table 1.

[0051] Table 1

[0052] In this embodiment, some parameters are selected as follows: , select . Smooth function Select . Select . Sliding surface parameter selection , . Simulation step selection , total simulation time selection .

[0053] Figure 2 (a), (b), and (c) show the error curves between the relative distances of the five wingmen and the virtual leader in the north, east, and altitude directions and the expected relative distances; Figure 2The (d), (e), (f) of the figure show the error variation curves of the speed, the heading angle and the track angle between the 5 wingmen and the virtual leader. It can be seen that the six state errors of the 5 wingmen converge to zero, which shows that the passive sliding mode method of the application can make the unmanned aerial vehicle state trajectory stably converge to the expected state value.

[0054] Figure 3 The (a), (b), (c) of the figure show the variation curves of the three components of the sliding mode surface along the time of the passive sliding mode method. It can be seen that the three components of the sliding mode quantity converge to the vicinity of zero in a limited time, which shows that the passive sliding mode method of the application can make the system state converge to the sliding mode surface and converge to the expected value along the sliding mode surface.

[0055] In particular, in order to verify the robustness of the passive sliding mode method of the application, the influence of the disturbance is also considered in this embodiment. The disturbance is selected as ; It should be particularly pointed out that the disturbance is applied as shown in the following formula in this embodiment ; Figure 4 The (a), (b), (c), (d), (e) of the figure show the variation curves of the values of the 5 wingmen It can be seen that the values of the 5 wingmen can converge to within an upper bound, which shows that the states of all wingmen can converge to the set , which shows the robustness of the passive sliding mode method of the application.

[0056] The passive sliding mode method for unmanned aerial vehicle formation distributed control provided by the embodiment can fully reflect the nonlinear characteristics of the unmanned aerial vehicle formation model by directly modeling the unmanned aerial vehicle formation system in the port Hamilton framework for the unmanned aerial vehicle motion model; in the port Hamilton framework, the control quantity of the wingman in the unmanned aerial vehicle formation system is directly designed from the energy angle, which is helpful to use the existing energy shaping technology; the expected Hamilton energy function is defined based on the distributed formation error model, and the sliding mode quantity is introduced in the Hamilton energy function, so that the designed wingman control quantity can not only ensure the stability of the unmanned aerial vehicle formation system, but also retain a certain robustness to the disturbance.

[0057] It should be understood that parts of the present disclosure can be realized by hardware, software, firmware or a combination thereof.

[0058] The above merely provides the specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present disclosure, which should be covered in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A passive sliding mode method for distributed control of UAV formations, characterized by: include: Step 1: Based on the UAV motion model and the preset initial state of the leader, under the action of the preset control variable, the intermediate state of the leader is obtained, the initial state of the wingman is set, and the initial state of the wingman is used as the intermediate state of the wingman at the initial moment; Step 2: Based on the preset communication model and topology, and using a consistency estimation algorithm, the wingman's state estimation of the leader is obtained. Step 3: Based on the distributed formation control error model, the distributed formation error of the wingman is obtained according to the intermediate state of the leader, the intermediate state of the wingman, and the estimated state of the wingman to the leader. Step 4: According to the port Hamiltonian system theory, the Hamiltonian energy function is defined based on the distributed formation error, thereby converting the UAV motion model into a port Hamiltonian system form, and further deriving the port Hamiltonian system form of the UAV formation system; In step 5, according to the port Hamiltonian system form of the UAV formation system, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, and then the passive sliding mode control quantity of the wingman is derived and input into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1, set the expression of the UAV motion model as ； ； in, Indicates the wingman number; Respectively represent The three-dimensional position coordinates of the wingman in the northeast celestial coordinate system; Respectively represent The speed scalar, heading angle and track angle of the wingman; Respectively represent The speed command, heading angle command and track angle command of the wingman; They represent the time inertia constants of the autopilot speed, heading angle, and track angle channels, respectively, and the units are ; Respectively represent the minimum flight speed and the maximum flight speed; Indicates the maximum acceleration; Represent the minimum track angle and the maximum track angle respectively; represents the maximum angular velocity; Step 1.2: Based on the preset initial state of the long engine, the preset control quantity Input the UAV motion model to obtain the intermediate state of the lead aircraft; Step 1.3, set the initial state of the wingman, and use the initial state of the wingman as the intermediate state of the wingman at the initial moment, and set the current moment The intermediate state of the wingman is Calculation of the intermediate state quantity of the wingman.

3. The method according to claim 2, characterized in that The expression of the consistency estimation algorithm is: ； in, Indicates the The state vector of the virtual leader estimated by the wingman according to the consistency estimation algorithm; Represents the state vector of the virtual leader itself; Indicates the The communication relationship between the wingman and the Indicates that the two wingmen are communicating with each other. It means there is no communication between the two; Indicates the The degree of dependency between the wingmen; Indicates the A collection of wingmen that communicate with each other.

4. The method according to claim 3, characterized in that The expression of the distributed formation control error model is: ； in, Indicates the The formation error vector of the wingman; Indicates the The desired relative state vector between the wingman and the virtual leader; Indicates the The desired relative state vector between the two wingmen; Indicates the state error between the wingman itself and the virtual leader. Represents the sum of the intermediate state errors between the wingman itself and all its neighbor wingmen.

5. The method according to claim 4, characterized in that The step 4 specifically includes: Step 4.1, describe the UAV motion model as the following general nonlinear system expression: ； in, , is the state quantity of the wingman; , is a unit column vector; , is the control amount of the wingman; ； ； Furthermore, for any wingman , converting it into the following port Hamiltonian form: ； in, is the state quantity of the wingman; is the control amount of the wingman; is an interconnected matrix that satisfies the antisymmetric property; is the damping matrix, which satisfies the positive definite property; is the Hamiltonian energy function Status The partial differential of ; ; ; ; ； Step 4.2: Based on the distributed formation error, define the Hamiltonian energy function as: ； Step 4.3, let , According to the port Hamiltonian system theory, the port Hamiltonian system form of the UAV formation system is derived: ； ； 。 6. The method according to claim 5, characterized in that The step 5 specifically includes: Step 5.1, based on the port Hamiltonian system form of the UAV formation system, introduce the sliding modulus into the Hamiltonian energy function: ； ； In step 5.2, according to the port Hamiltonian system theory, the passive sliding mode control quantity of the wingman is obtained: ； In step 5.3, the passive sliding mode control variable of the wingman is input into the UAV motion model to obtain the intermediate state variable of the wingman at the next moment.

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