Unmanned aerial vehicle-unmanned vehicle heterogeneous system formation control method based on protopigeon fuzziness

Abstract

The invention discloses an unmanned aerial vehicle-unmanned vehicle heterogeneous system formation control method based on protopigeon fuzzy. The method comprises the following steps: 1, modeling an unmanned aerial vehicle and unmanned vehicle heterogeneous system and initializing system state information; 2, combining the kinetic models of the unmanned aerial vehicle and the unmanned vehicle, and establishing a unified unmanned aerial vehicle and unmanned vehicle heterogeneous system model; 3, designing a dynamic formation consistency control protocol and a dynamic formation keeping control protocol; 4, introducing the consistency error of each unmanned aerial vehicle and the unmanned vehicle, and establishing a target constraint equation; 5, introducing a protopigeon fuzzy mechanism, selecting model parameters of the heterogeneous system, and solving a target constraint equation; and 6, substituting the optimal parameter into the heterogeneous system equation to obtain a final heterogeneous system formation result. According to the method, the problem of unified modeling of heterogeneous models in different dimensions is solved; the requirements of formation consistent movement, formation keeping movement and the like between heterogeneous unmanned aerial vehicles and unmanned vehicles can be met; errors can be reduced and the convergence speed can be accelerated; and better system parameters can be obtained.

Description

Technical Field

[0001] This invention relates to a formation control method for heterogeneous unmanned aerial vehicle-drone systems based on pigeon fuzzy logic, belonging to the field of heterogeneous unmanned system cluster cooperative control technology. Background Technology

[0002] With the rapid development of unmanned systems technology, the collaborative operation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has shown great potential in disaster relief, smart agriculture, and military reconnaissance. Compared to a single platform, heterogeneous unmanned system formations can significantly improve mission adaptability in complex environments by integrating the wide-area perception capabilities of UAVs with the long-duration operation advantages of UGVs. For example, in collaborative search and rescue missions, UAVs can quickly locate targets, while UGVs are responsible for transporting supplies; their collaboration can significantly shorten response time. However, the differences in dynamic characteristics of heterogeneous platforms, cross-media communication constraints, and environmental uncertainties pose multi-dimensional collaborative challenges to their formation control, necessitating a breakthrough from the theoretical framework of traditional homogeneous formation control.

[0003] Current research on formation control mainly focuses on the cooperative control of homogeneous systems (such as multiple UAVs or multiple UGVs). Research on heterogeneous cooperative systems based on UAVs and unmanned vehicles still has significant potential and importance, and achieving cooperative consistency control between UGVs and UAVs remains a challenge. Consensus control, as a core theory for achieving formation maintenance and cooperative behavior in multi-agent systems, provides an effective theoretical foundation for cooperation in heterogeneous systems. By rationally designing consensus protocols, each agent can gradually approach the desired formation or target state through distributed information interaction.

[0004] For heterogeneous UAV-UGV systems, the design of consensus protocols must not only consider the dynamic constraints of different types of agents, but also fully account for communication topology changes and system disturbances in practical applications. Regarding parameter selection in formation control of heterogeneous systems, the original pigeon mechanism is an effective mechanism extracted from pigeon flock optimization algorithms, which can select better relevant parameters.

[0005] This invention proposes to introduce a formation consistency-related control method based on the modeling of heterogeneous cluster systems, and to introduce a primitive pigeon fuzzy mechanism for parameter selection, thereby achieving better heterogeneous formation control of UAVs and unmanned vehicles. Summary of the Invention

[0006] This invention provides a formation control method for heterogeneous UAV-Vehicle systems based on primitive dovetail fuzzy logic. Its purpose is to offer a formation control method for heterogeneous swarm systems, aiming to solve the problems of heterogeneous cooperative formation and system parameter selection between UAVs and Vehicles. This method constructs a dynamic model of the heterogeneous UAV-Vehicle system, designs a consistent formation control protocol considering inter-UAV distance, introduces primitive dovetail fuzzy logic for parameter selection, and achieves formation control of the system.

[0007] This invention addresses the formation and cooperative control problem of heterogeneous UAV-UAV swarms considering communication topology. It proposes a formation control method for heterogeneous UAV-UAV systems based on primitive pigeon fuzzy logic, and its algorithm implementation flowchart is shown below. Figure 1 As shown, the implementation includes several parts, such as the initialization of the UAV and unmanned vehicle system cluster state, the establishment of the UAV and unmanned vehicle system models and the establishment of the joint unified model of the two, the design of the dynamic formation consistency control protocol and the dynamic formation maintenance protocol, the design of the target constraint equation based on the consistency error, and the implementation of the original pigeon fuzzy mechanism. The specific implementation steps are as follows:

[0008] Step 1: Modeling and Initializing System State Information for the Heterogeneous System of UAVs and Unmanned Vehicles

[0009] In step one, the initial parameters of the model, the initial positions of the UAV and the unmanned vehicle, and the communication topology between the UAV and the unmanned vehicle need to be initialized. In this invention, a high-order rotorcraft UAV dynamic model is considered as follows:

[0010]

[0011] Where g represents gravitational acceleration; x a ,y a ,z a Represents the three-axis position coordinates. β, α, These are roll, pitch, and yaw angles, respectively. z It is the lift force in the vertical direction, M β M α , Indicates the moments of the three coordinate axes in the body reference frame; I x ,I y ,I z These represent the moments of inertia of the body's reference frame, respectively.

[0012] Consider the dynamic model of the autonomous vehicle as follows:

[0013]

[0014] Where, x g ,y gThe vector represents the position information of the autonomous vehicle, ζ and w are the linear velocity and angular velocity of the autonomous vehicle, respectively, χ represents the yaw angle of the autonomous vehicle; F is the thrust, m g τ is the mass of the autonomous vehicle, τ is the torque of the autonomous vehicle, and J is the mass of the autonomous vehicle. g It is the moment of inertia; This indicates the coordinate transformation of the autonomous vehicle in the local coordinate system.

[0015] Step 2: Combine the dynamic models of UAVs and unmanned vehicles to establish a unified heterogeneous system model.

[0016] The dynamics model of the unmanned vehicle in step one above can be simplified as follows:

[0017]

[0018] in, This represents the position of the autonomous vehicle in two-dimensional space. Indicates speed; Let represent the control input of driverless vehicle i. A driverless vehicle system with l vehicles can be transformed into the following state-space equations:

[0019]

[0020] Where X G =[R G V G ] T , R G V G U G These represent vectors consisting of the position, velocity, and control input of all autonomous vehicles.

[0021] Similarly, the unmanned aerial vehicle system with m drones can be transformed into a state-space equation as follows:

[0022]

[0023] in R A V A U A ,Ξ A This represents a vector consisting of the position, velocity, and control inputs of all UAVs.

[0024] This invention focuses on the coordinated motion of the drone and the unmanned vehicle. Assuming the drone flies at a fixed altitude and the control input on the z-axis is assumed to be 0, we can obtain:

[0025]

[0026] Based on the state-space equation (4) of the unmanned vehicle and the state-space equation (5) of the unmanned aerial vehicle, the heterogeneous system model of the unmanned aerial vehicle and the unmanned aerial vehicle can be obtained as follows:

[0027]

[0028] in In this invention, the UAV and unmanned vehicle system corresponding to equation (7) are used as heterogeneous systems.

[0029] Step 3: Considering the non-fully connected communication topology, design a dynamic formation consistency control protocol and a formation maintenance protocol respectively.

[0030] For the i-th (i = 1, 2, ..., l) autonomous vehicle, the designed dynamic formation consensus control protocol is as follows:

[0031]

[0032] The formation maintenance protocol designed is as follows:

[0033]

[0034] For the i-th (i = 1, 2, ..., m) drone, the designed dynamic formation consensus control protocol is as follows:

[0035]

[0036] The formation maintenance protocol designed is as follows:

[0037]

[0038] Among them, a ij The communication topology between the drone and the unmanned vehicle determines that when the i-th drone and the j-th drone have a communication connection, a ij =1, otherwise a ij =0; L ij The distance matrix L between the UAV and the unmanned vehicle determines the distance between them in the formation; c1, c2, ..., c8, γ1, γ2 are the model parameters in the control protocol designed in the heterogeneous system of UAV and unmanned vehicle, and are referred to as the model parameters of the heterogeneous system.

[0039] Step 4: Introduce the consistency error between each UAV and unmanned vehicle, and establish the target constraint equation as follows:

[0040] The average consistency error is calculated as follows:

[0041]

[0042] Among them, when using the dynamic formation consistency control protocol (equations (8) and (10)), e ij =‖‖r j -r i ‖; When using formation preservation protocols (equations (9) and (11)), e ij =||r j -r i ||-R ij ,

[0043] The established objective constraint equation is as follows

[0044]

[0045] Step 5: Introduce the original pigeon fuzzy mechanism, select the model parameters of the heterogeneous system, and solve the objective constraint equation (13) established in Step 4. The original pigeon fuzzy mechanism is described as follows: the search in the first stage is...

[0046]

[0047] Among them, P i (t) represents the current position of the original pigeon population, P gbest It is the optimal population location, O i (t) represents the population's movement speed, Rp is the step control parameter, and rand is a random number in [0,1]. The fuzzy membership function established for Rp is:

[0048]

[0049] in, t max This is the maximum number of iterations. The second-stage search method of the original pigeon fuzzy mechanism is...

[0050]

[0051] Where N pio (t) represents the number of the original pigeon population, and f(·) represents the objective constraint equation to be solved. Equations (14) and (16) are used to solve the objective constraint equation in equation (13) to obtain better system model parameters.

[0052] Step 6: Substitute the optimal parameter variables c1, c2, ..., c8, γ1, γ2 obtained in Step 5 into the heterogeneous system equations (7) and (8)-(11) to obtain the final formation result of the UAV and unmanned vehicle heterogeneous system.

[0053] Advantages and beneficial effects of the present invention:

[0054] This invention proposes a dovetail fuzzy-based formation control method for heterogeneous UAV-Vehicle systems. The main advantages of this visual navigation system and method are reflected in four aspects: First, this invention provides a complete modeling method and a unified model method for heterogeneous UAV and Vehicle systems, solving the problem of unified modeling across different dimensions in heterogeneous models; second, this invention designs formation consistency control protocols and formation control protocols, which can meet the requirements of formation consistency motion and formation maintenance motion between heterogeneous UAVs and Vehicles; third, this invention designs an objective constraint function to address the selection of heterogeneous system model parameters and the reduction of formation errors, which can reduce errors and accelerate convergence speed; finally, this invention provides a dovetail fuzzy mechanism to solve the objective constraint equation and obtain better system parameters; the obtained optimal parameters are then substituted into the system to obtain formation experiment results. Attached Figure Description

[0055] Figure 1 This is a flowchart of a heterogeneous UAV-unmanned vehicle formation control method based on pigeon fuzzy logic.

[0056] Figure 2 It is the curve of the consistent motion state variables of the heterogeneous system of drones and unmanned vehicles.

[0057] Figure 3 It is the process of coordinated movement of a heterogeneous system of drones and unmanned vehicles.

[0058] Figure 4 It is the curve of the motion state variable of the formation of the heterogeneous system of drones and unmanned vehicles.

[0059] Figure 5 It is the process of maintaining the formation and movement of a heterogeneous system of drones and unmanned vehicles.

[0060] Figure 6 It is the process of maintaining the formation and movement of a heterogeneous system of drones and unmanned vehicles.

[0061] The labels and symbols in the diagram are explained as follows:

[0062] t max —The maximum number of iterations for the original pigeon fuzzy mechanism;

[0063] (x a ,y a ,z a — The three-dimensional position coordinates of the drone;

[0064] (x g ,y g — The two-dimensional position coordinates of the driverless vehicle;

[0065] β,α, —The roll, pitch, and yaw angles of the drone;

[0066] f z —Lift of drones;

[0067] M β M α , —The radius of the drone;

[0068] I x ,I y ,I z —Moment of inertia of the unmanned aerial vehicle system;

[0069] ζ, w — the linear velocity and angular velocity of the autonomous vehicle;

[0070] F – The thrust of the driverless car;

[0071] m g —The quality of driverless cars;

[0072] τ,J g —The torque and moment of inertia of the autonomous vehicle;

[0073] X G —The state space of autonomous vehicles;

[0074] —The state-space system matrix of the autonomous vehicle;

[0075] —The control input system matrix for autonomous vehicles;

[0076] U G —Control inputs for driverless vehicles;

[0077] X A —The state space of the drone;

[0078] —The state-space system matrix of the unmanned aerial vehicle;

[0079] —The control input system matrix for unmanned aerial vehicles;

[0080] U A —Control inputs for the drone;

[0081] c1,c2,...,c8,γ1,γ2——Model parameters of the heterogeneous system of UAV and unmanned vehicle;

[0082] r i V i —Location and speed information of drones / unmanned vehicles;

[0083] a ij ,L ij—Communication topology and formation distance between drones and unmanned vehicles;

[0084] P i (t),O i (t) — Location and speed of the rock pigeon population;

[0085] Rp—the stepping parameter for the movement of the wild pigeon population;

[0086] N pio (t),P gbest —Population size and optimal location of individual wild pigeons;

[0087] f(·)——The objective constraint equation to be solved for the original pigeon population; Detailed Implementation

[0088] A formation control method for a heterogeneous UAV-Vehicle system based on pigeon fuzzy logic is illustrated in the flowchart below. Figure 1 As shown, the implementation includes several parts, such as the initialization of the UAV and unmanned vehicle system cluster state, the establishment of the UAV and unmanned vehicle system models and the establishment of the joint unified model of the two, the design of the dynamic formation consistency control protocol and the dynamic formation maintenance protocol, the design of the target constraint equation based on the consistency error, and the implementation of the original pigeon fuzzy mechanism. The specific implementation steps are as follows:

[0089] Step 1: Modeling and Initializing System State Information for the Heterogeneous System of UAVs and Unmanned Vehicles

[0090] In step one, the initial parameters of the model, the initial positions of the UAV and the unmanned vehicle, and the communication topology between the UAV and the unmanned vehicle need to be initialized. In this invention, a high-order rotorcraft UAV dynamic model is considered as follows:

[0091]

[0092] Where g represents gravitational acceleration; x a ,y a ,z a Represents the three-axis position coordinates. β, α, These are roll, pitch, and yaw angles, respectively. z It is the lift force in the vertical direction, M β M α , Indicates the moments of the three coordinate axes in the body reference frame; I x ,I y ,I z These represent the moments of inertia of the body's reference frame, respectively.

[0093] Consider the dynamic model of the autonomous vehicle as follows:

[0094]

[0095] Where, x g ,y g The vector represents the position information of the autonomous vehicle, ζ and w are the linear velocity and angular velocity of the autonomous vehicle, respectively, χ represents the yaw angle of the autonomous vehicle; F is the thrust, m g τ is the mass of the autonomous vehicle, τ is the torque of the autonomous vehicle, and J is the mass of the autonomous vehicle. g It is the moment of inertia; This indicates the coordinate transformation of the autonomous vehicle in the local coordinate system.

[0096] Step 2: Combine the dynamic models of UAVs and unmanned vehicles to establish a unified heterogeneous system model.

[0097] The dynamics model of the unmanned vehicle in step one above can be simplified as follows:

[0098]

[0099] in, This represents the position of the autonomous vehicle in two-dimensional space. Indicates speed; Let represent the control input of driverless vehicle i. A driverless vehicle system with l vehicles can be transformed into the following state-space equations:

[0100]

[0101] Where X G =[R G V G ] T , R G V G U G These represent vectors consisting of the position, velocity, and control input of all autonomous vehicles.

[0102] Similarly, the unmanned aerial vehicle system with m drones can be transformed into a state-space equation as follows:

[0103]

[0104] in R A V A U A ,Ξ A This represents a vector consisting of the position, velocity, and control inputs of all UAVs.

[0105] This invention focuses on the coordinated motion of the drone and the unmanned vehicle. Assuming the drone flies at a fixed altitude and the control input on the z-axis is assumed to be 0, we can obtain:

[0106]

[0107] Based on the state-space equation (4) of the unmanned vehicle and the state-space equation (5) of the unmanned aerial vehicle, the heterogeneous system model of the unmanned aerial vehicle and the unmanned aerial vehicle can be obtained as follows:

[0108]

[0109] in In this invention, the UAV and unmanned vehicle system corresponding to equation (7) are used as heterogeneous systems.

[0110] Step 3: Considering the non-fully connected communication topology, design a dynamic formation consistency control protocol and a formation maintenance protocol respectively.

[0111] For the i-th (i = 1, 2, ..., l) autonomous vehicle, the designed dynamic formation consensus control protocol is as follows:

[0112]

[0113] The formation maintenance protocol designed is as follows:

[0114]

[0115] For the i-th (i = 1, 2, ..., m) drone, the designed dynamic formation consensus control protocol is as follows:

[0116]

[0117] The formation maintenance protocol designed is as follows:

[0118]

[0119] Among them, a ij The communication topology between the drone and the unmanned vehicle determines that when the i-th drone and the j-th drone have a communication connection, a ij =1, otherwise a ij =0; L ij The distance matrix L between the UAV and the unmanned vehicle determines the distance between them in the formation; c1, c2, ..., c8, γ1, γ2 are the model parameters in the control protocol designed in the heterogeneous system of UAV and unmanned vehicle, and are referred to as the model parameters of the heterogeneous system.

[0120] Step 4: Introduce the consistency error between each UAV and unmanned vehicle, and establish the target constraint equation as follows:

[0121] The average consistency error is calculated as follows:

[0122]

[0123] Among them, when using the dynamic formation consistency control protocol (equations (8) and (10)), e ij =||r j -r i ||;When using formation maintenance protocols (equations (9) and (11)), e ij =||r j -r i ||-R ij ,

[0124] The established objective constraint equation is as follows

[0125]

[0126] Step 5: Introduce the original pigeon fuzzy mechanism, select the model parameters of the heterogeneous system, and solve the objective constraint equation (13) established in Step 4. The original pigeon fuzzy mechanism is described as follows: the search in the first stage is...

[0127]

[0128] Among them, P i (t) represents the current position of the original pigeon population, P gbest It is the optimal population location, O i (t) represents the population's movement speed, Rp is the step control parameter, and rand is a random number in [0,1]. The fuzzy membership function established for Rp is:

[0129]

[0130] in, t max This is the maximum number of iterations. The second-stage search method of the original pigeon fuzzy mechanism is...

[0131]

[0132] Where N pio (t) represents the number of the original pigeon population, and f(·) represents the objective constraint equation to be solved. Equations (14) and (16) are used to solve the objective constraint equation in equation (13) to obtain better system model parameters.

[0133] Step 6: Substitute the optimal parameter variables c1, c2, ..., c8, γ1, γ2 obtained in Step 5 into the heterogeneous system equations (7) and (8)-(11) to obtain the final formation result of the UAV and unmanned vehicle heterogeneous system.

[0134] Example 1:

[0135] See Figures 1 to 6The effectiveness of the system and method proposed in this invention will be verified through two sets of experiments: "formation consistency motion of heterogeneous systems of UAVs and unmanned vehicles" and "formation maintenance motion of heterogeneous systems of UAVs and unmanned vehicles". The specific steps of the system and method are as follows:

[0136] Based on the unified model established by the above formula (7), the positions of the UAV and unmanned vehicle cluster are initialized. The positions (in m) of unmanned vehicles 1, 2, and 3 are (100, 80), (70, 20), and (45, 35), respectively, and their speeds (in m / s) are (5, 10), (15, 5), and (18, 8), respectively. The positions (in m) of UAVs 1 and 2 are (35, 60, 55) and (85, 38, 60), respectively, and their speeds (in m / s) are (2, 12, 1) and (4, 14, 3), respectively. Their attitude angles (in °) are (12, 25, 2) and (25, 10, 1), respectively, and their attitude angular rates (° / s) are (2, 3, 1) and (1, 2, 2), respectively. The initial topology communication structure of the UAV and unmanned vehicle cluster is as follows: Figure 2 As shown. Set the formation control matrix L = [L ij ]for

[0137]

[0138] In the formation consistency motion experiment of the heterogeneous system of UAV and unmanned vehicle, control protocols (8) and (10) are adopted. In the formation maintenance motion experiment of the heterogeneous system of UAV and unmanned vehicle, control protocols (9) and (11) are adopted. Then, the original pigeon fuzzy mechanism (14)-(16) is used to solve the target constraint equation (13) and optimize the parameters in the heterogeneous system of UAV and unmanned vehicle to obtain the final result. t in the original pigeon fuzzy mechanism max =100,N pio =30, the resulting parameter selection is:

[0139] c1, c3, c5, c7 = 0.2, c2, c4, c6, c9 = 0.9, γ1 = 4, γ2 = 1.5, and the simulation results of the control protocol are as follows: Figure 3-6 As shown. In the formation consistency motion experiment of the heterogeneous system of UAV and unmanned vehicle, Figure 3 and Figure 4 The effectiveness of the designed formation consistency control protocol and the original dovetail fuzzy mechanism optimization was verified, and both the UAV and the unmanned vehicle achieved position and velocity consistency. In the formation-keeping motion experiment of the heterogeneous UAV and unmanned vehicle system, Figure 5 and Figure 6 The effectiveness of the designed formation maintenance control protocol and the original dove fuzzy mechanism optimization was verified. Both the UAV and the unmanned vehicle achieved position and speed consistency while maintaining a certain distance between the UAV and the vehicle to form a formation.

Claims

1. A method for formation control of a heterogeneous UAV-unmanned vehicle system based on primitive pigeon fuzzy logic, characterized in that, The specific steps of this method are as follows: Step 1: Modeling and initializing the system state information of the UAV-Vehicle heterogeneous system, including initializing the position and communication topology of the UAV and Vehicle cluster, and establishing the dynamic models of the UAV and Vehicle. Step 2: Combine the dynamic models of UAVs and unmanned vehicles to establish a unified heterogeneous system model of UAVs and unmanned vehicles; Step 3: Based on the principle of dynamic consistency, and considering the non-fully connected graph of the communication topology, a dynamic formation consistency control protocol and a dynamic formation maintenance control protocol were designed respectively. Step 4: Introduce the consistency error of each UAV-unmanned vehicle and establish the target constraint equation; Step 5: Introduce the original pigeon fuzzy mechanism, select the model parameters of the heterogeneous system, and solve the target constraint equations established in Step 4; Step 6: Substitute the optimal parameters into the heterogeneous system equations to obtain the final heterogeneous system formation results.

2. The method according to claim 1, characterized in that: Step one is described in detail as follows: The established dynamic model of the UAV is as follows: Where g represents gravitational acceleration; x a ,y a ,z a Represents the three-axis position coordinates; β, α, These are roll, pitch, and yaw; f z It is the lift force in the vertical direction, M β M α , Indicates the moments of the three coordinate axes in the body reference frame; I x ,I y ,I z These represent the moments of inertia of the body's reference frame, respectively. The established dynamic model of the autonomous vehicle is as follows: Where, x g ,y g The vector represents the position information of the autonomous vehicle, ζ and w are the linear velocity and angular velocity of the autonomous vehicle, respectively, χ represents the yaw angle of the autonomous vehicle; F is the thrust, m g τ is the mass of the autonomous vehicle, τ is the torque of the autonomous vehicle, and J is the mass of the autonomous vehicle. g It is the moment of inertia; This indicates the coordinate transformation of the autonomous vehicle in the local coordinate system.

3. The method according to claim 2, characterized in that: Step two is described in detail below: A simplified model of an autonomous vehicle is as follows: in, This represents the position of the autonomous vehicle in two-dimensional space. Indicates speed; Let i represent the control input of driverless vehicle i; the driverless vehicle system with l driverless vehicles can be transformed into the state-space equation as follows: Among them, X G =[R G V G ] T , R G V G U G These represent vectors consisting of the position, velocity, and control input of all autonomous vehicles; Consider a drone system with m drones, which can be transformed into a state-space equation as follows: in, Considering the cooperative motion of the UAV and the unmanned vehicle, assuming the UAV flies at a fixed altitude and the control input on the z-axis is set to 0, we get: The state-space models of the combined unmanned vehicle and drone (4) and (5) form a unified heterogeneous system model for drones and unmanned vehicles: in, 4. The method according to claim 1, characterized in that: Step three is described in detail below: Based on the non-fully connected graph of the communication topology, for the i-th autonomous vehicle, the designed dynamic formation consensus control protocol is: where i = 1, 2, ..., l; The formation maintenance protocol designed is as follows: For the i-th individual drone, the designed dynamic formation consensus control protocol is as follows: The formation maintenance protocol designed is as follows: Among them, a ij The communication topology between the UAV and the unmanned vehicle determines that when the i-th UAV has a communication connection with the j-th UAV, a ij =1, otherwise a ij =0; L ij The distance matrix L between the UAV and the unmanned vehicle determines the distance between them in the formation; c1, c2, ..., c8, γ1, γ2 are the model parameters in the control protocol designed in the UAV-unmanned vehicle heterogeneous system, and are referred to as the model parameters of the heterogeneous system.

5. The method according to claim 1, characterized in that: Step four is described in detail below: The consistency error introduced for each UAV-unmanned vehicle is: Among them, when using the dynamic formation consistency control protocol, e ij =||r j -r i ‖; When using formation maintenance protocols, e ij =||r j -r i ||-R ij , The established objective constraint function is:

6. The method according to claim 1, characterized in that: Step five is described in detail below: Introducing the original pigeon fuzzy mechanism, selecting the model parameters of the heterogeneous system, and solving the above-established objective constraint equation (13); the original pigeon fuzzy mechanism is described as follows, the first stage of the search is: Among them, P i (t) represents the current position of the original pigeon population, P gbest It is the optimal population location, O i (t) represents the population's movement speed, Rp is the step control parameter, and rand is a random number in [0,1]. The fuzzy membership function established for Rp is: in, t max This is the maximum number of iterations; the second-stage search method of the original pigeon fuzzy mechanism is: Where, N pio (t) represents the number of the original pigeon population, and f(·) represents the objective constraint equation to be solved. Equations (14) and (16) are used to solve the objective constraint equation in equation (13) to obtain better system model parameters.

7. The method according to claim 1, characterized in that: Step six is ​​described in detail below: Substitute the optimal parameter variables c1, c2, ..., c8, γ1, γ2 obtained in step five into the unified UAV-unmanned vehicle heterogeneous system model established in step two and the dynamic formation consistency control protocol and dynamic formation maintenance control protocol designed in step three to obtain the final formation result of the UAV-unmanned vehicle heterogeneous system.

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