A passive sliding mode method for unmanned aerial vehicle formation distributed control
By designing UAV formation control variables in a port Hamiltonian system using a passive sliding mode method, the stability and disturbance resistance issues of distributed control of UAV formations are solved, thereby improving the stability and robustness of the UAV formation system. This approach is applicable to scenarios such as logistics transportation, agricultural irrigation, and remote reconnaissance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CENT SOUTH UNIV
- Filing Date
- 2025-07-30
- Publication Date
- 2026-07-21
AI Technical Summary
Existing distributed control methods for UAV formations have shortcomings in terms of stability and disturbance resistance. In particular, the backstepping control method requires an accurate system model and is complex to design, the model predictive control method has a large optimization computational burden and poor real-time performance, and the sliding mode control method suffers from jitter.
By adopting the passive sliding mode method, and by setting the UAV motion model and the preset initial state of the lead aircraft, combined with the consensus estimation algorithm and port Hamiltonian system theory, the Hamiltonian energy function is defined and the sliding mode quantity is introduced. The distributed formation control quantity of the wingmen is designed, and it is transformed into the form of a port Hamiltonian system to derive the control quantity of the UAV formation system.
It improves the stability and anti-disturbance of the drone formation system, and enables the direct design of wingman control variables under the port Hamiltonian framework, maintaining system stability and robustness. It is suitable for scenarios such as logistics transportation, agricultural irrigation and long-range reconnaissance of drone formation control.
Smart Images
Figure CN120803050B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of control technology, and in particular to a passive sliding mode method for distributed control of UAV formations. Background Technology
[0002] In recent years, unmanned aerial vehicles (UAVs) have played an increasingly important role in both military 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 widely used in civilian applications such as agricultural irrigation and logistics transportation. UAV swarm systems have advantages such as high mission execution efficiency, high economic value, and good fault tolerance and robustness, thus becoming an important development trend in UAV system applications. Distributed control of UAV swarm systems is an important topic in the field of UAV swarm systems and also a research hotspot.
[0003] For distributed control of UAV formations, commonly used control methods include backstepping control, model predictive control, and sliding mode control. Backstepping control introduces virtual control inputs and asymptotically approximates the system state, progressively designing the control law for each stage to achieve global system stability. However, backstepping control requires an accurate system model, and its design process is highly complex. Model predictive control, based on the system model, can predict the future behavior of the system within a given time window, thus making optimal decisions. Model predictive control can well consider system constraints, but its optimization computation burden is high and its real-time performance is poor. Sliding mode control introduces a sliding surface and constrains the system state to this surface, enabling the closed-loop system to converge stably along the sliding surface. Sliding mode control has some robustness to external disturbances, but it suffers from chattering.
[0004] It is evident that there is an urgent need for a passive sliding mode method with high stability and disturbance resistance for distributed control of UAV formations. Summary of the Invention
[0005] In view of this, the present disclosure provides a passive sliding mode method for distributed control of UAV formations, which at least partially solves the problems of stability and disturbance resistance in the prior art.
[0006] This disclosure provides a passive sliding mode method for distributed control of UAV formations, including:
[0007] Step 1: Based on the UAV motion model and the preset initial state of the lead aircraft, and under the action of the preset control variables, obtain the intermediate state of the lead aircraft, 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.
[0008] Step 2: Based on the preset communication model and topology, and using the consensus estimation algorithm, obtain the wingman's state variables for the alpha aircraft.
[0009] Step 3: Based on the intermediate state quantities of the lead aircraft, the intermediate state quantities of the wingman, and the estimated state quantities of the wingman relative to the lead aircraft, the distributed formation error quantity of the wingman is obtained based on the distributed formation control error model.
[0010] Step 4: Based on the port Hamiltonian system theory, the Hamiltonian energy function is defined based on the distributed formation error, thereby transforming the UAV motion model into a port Hamiltonian system form, and further deriving the port Hamiltonian system form of the UAV formation system.
[0011] Step 5: Based on the port Hamiltonian system form of the UAV formation system, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, thereby deriving the passive sliding mode control quantity of the wingman and inputting it into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.
[0012] According to a specific implementation of an embodiment of this disclosure, step 1 specifically includes:
[0013] Step 1.1, define the expression for the UAV motion model as follows: ; ; in, Indicates the wingman's serial number; They represent the first The three-dimensional position coordinates of the wingman in the northeast-central coordinate system; They represent the first The wingman's speed scalar, heading angle, and track angle; They represent the first Speed, heading, and track angle commands for wingmen; These represent the time inertia constants for the autopilot's speed, heading angle, and track angle channels, respectively, with units of 1. ; These represent the minimum and maximum flight speeds, respectively. Indicates the maximum acceleration; These represent the minimum and maximum track angles, respectively. Indicates the maximum angular velocity;
[0014] Step 1.2: Based on the preset initial state of the servo machine, set the preset control quantity... Input the UAV motion model to obtain the intermediate state variables of the lead UAV;
[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 quantity of the wingman changes from the previous moment. Calculation of intermediate state quantities of the wingman.
[0016] According to a specific implementation of this disclosure, the expression of the consensus estimation algorithm is as follows: ; in, Indicates the first The wingman estimates the virtual alpha state vector based on the consensus estimation algorithm; This represents the state vector of the virtual primary machine itself; Indicates the first Communication relationships between wingmen This indicates communication between the two wingmen. This indicates that the two do not communicate with each other; Indicates the first The degree of interdependence between wingmen; Indicates the relationship with the first A group of wingmen who communicate with each other.
[0017] According to a specific implementation of this disclosure, the expression for the distributed formation control error model is as follows: ; in, Indicates the first Formation error vector of wingman aircraft; Indicates the first The expected relative state vector between the wingman and the virtual alpha; Indicates the first The expected relative state vector between wingmen; This indicates the state error between the wingman and the virtual alpha aircraft. This represents the sum of intermediate state errors between the wingman itself and all its neighboring wingmen.
[0018] According to a specific implementation of an embodiment of this disclosure, step 4 specifically includes:
[0019] Step 4.1, the UAV motion model is described as the following general nonlinear system expression: ; in, , is the status variable of the wingman; , It is a unit column vector; This refers to the control amount of the wingman; ; ; Furthermore, for any wingman Convert it to the following port Hamiltonian form: ; in, For the wingman's status variables; For the control of wingmen; The interconnection matrix satisfies the antisymmetric property; The damping matrix satisfies the positive definite property; Hamiltonian energy function State The partial derivative, ; ; ; ; ; Step 4.2, based on the distributed formation error, define the Hamiltonian energy function as follows: ; Step 4.3, let Based on the port Hamiltonian system theory, the port Hamiltonian system form of the UAV formation system is derived as follows: ; ; .
[0020] According to a specific implementation of an embodiment of this disclosure, step 5 specifically includes:
[0021] Step 5.1, based on the port Hamiltonian system form of the UAV formation system, introduce a sliding mode into the Hamiltonian energy function: ; ; Step 5.2, based on the port Hamiltonian system theory, obtain the passive sliding mode control quantity of the wingman: ; Step 5.3: Input the passive sliding mode control quantity of the wingman into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.
[0022] The passive sliding mode scheme for distributed control of UAV formations in this embodiment includes: Step 1, obtaining the intermediate state of the wingman based on the UAV motion model and the preset initial state of the wingman, under the action of a preset control variable, setting the initial state of the wingman, and using the initial state of the wingman as the intermediate state of the wingman at the initial moment; Step 2, obtaining the state value estimate of the wingman to the wingman based on a preset communication model and topology, using a consensus estimation algorithm; Step 3, based on the intermediate state of the wingman, the intermediate state of the wingman, and the estimated state value of the wingman to the wingman, using a consensus estimation algorithm... Step 4: Based on the distributed formation control error model, the distributed formation error of the wingman is obtained; Step 5: Based on the port Hamiltonian system theory, the Hamiltonian energy function is defined based on the distributed formation error, thereby transforming the UAV motion model into a port Hamiltonian system form, and further deriving the port Hamiltonian system form of the UAV formation system; Step 6: Based on the port Hamiltonian system form of the UAV formation system, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, thereby deriving the passive sliding mode control quantity of the wingman and inputting it into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.
[0023] The beneficial effects of the embodiments of this disclosure are as follows: By using the solution of this disclosure, the UAV formation system is directly modeled under the port Hamiltonian framework for the UAV motion model, which can fully reflect the nonlinear characteristics of the UAV formation model; under the port Hamiltonian framework, the control quantity of the wingman in the UAV formation system is directly designed from the energy perspective, which helps to use existing energy shaping technology; the expected Hamiltonian energy function is defined based on the distributed formation error model, and a sliding mode is introduced into the Hamiltonian energy function, so that the designed wingman control quantity can not only ensure the stability of the UAV formation system, but also retain a certain robustness to disturbances. Attached Figure Description
[0024] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 A flowchart illustrating a passive sliding mode method for distributed control of UAV formations provided in this disclosure embodiment; Figure 2This is a schematic diagram of the error of a wingman in an unmanned aerial vehicle (UAV) formation system provided in an embodiment of the present disclosure, wherein (a) is the northward distance error of the wingman in the UAV formation system, (b) is the eastward distance error of the wingman in the UAV formation system, (c) is the altitude distance error of the wingman in the UAV formation system, (d) is the speed error of the wingman in the UAV formation system, (e) is the heading angle error of the wingman in the UAV formation system, and (f) is the track angle error of the wingman in the UAV formation system. Figure 3 This is a schematic diagram of the sliding mode component variation of a passive sliding mode method provided in an embodiment of the present disclosure, wherein (a) is the variation curve of sliding mode component 1 of the passive sliding mode method, (b) is the variation curve of sliding mode component 2 of the passive sliding mode method, and (c) is the variation curve of sliding mode component 3 of the passive sliding mode method. Figure 4 This is a schematic diagram of the bounded state change curve of the passive sliding mode method provided in the embodiments of this disclosure under disturbance, wherein (a) to (e) are the bounded state change curves of different wingmen under disturbance. Detailed Implementation
[0026] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.
[0027] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0028] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.
[0029] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0030] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.
[0031] This disclosure provides a passive sliding mode method for distributed control of UAV formations, which can be applied to UAV formation control processes in scenarios such as logistics transportation, agricultural irrigation, and remote reconnaissance.
[0032] See Figure 1 This is a flowchart illustrating a passive sliding mode method for distributed control of UAV formations provided in an embodiment of this disclosure. Figure 1 As shown, the method mainly includes the following steps:
[0033] Step 1: Based on the UAV motion model and the preset initial state of the lead aircraft, and under the action of the preset control variables, obtain the intermediate state of the lead aircraft, 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.
[0034] In practice, the drone formation system includes a virtual lead drone and multiple wingmen. In some embodiments of the present invention, the initial state variables of the virtual lead drone and wingmen can be assigned initial values based on human experience when determining the flight formation motion model.
[0035] In this embodiment, a nonlinear motion model of a fixed-wing UAV in three-dimensional space is adopted, and its specific expression is as follows: ; in, Indicates the wingman's serial number; They represent the first The three-dimensional position coordinates of the wingman in the northeast-central coordinate system; They represent the first The wingman's speed scalar, heading angle, and track angle; They represent the first Speed, heading, and track angle commands for wingmen; These represent the time inertia constants for the autopilot's speed, heading angle, and track angle channels, respectively, with units of 1. .
[0036] In practical unmanned aerial vehicle (UAV) systems, due to factors such as aerodynamic performance and propulsion systems, the UAV needs to meet certain constraints. This embodiment considers five state constraints, including velocity, acceleration, track angle, track angle angular velocity, and heading angle angular velocity. Specifically, the constraints are as follows: ; in, These represent the minimum and maximum flight speeds, respectively. Indicates the maximum acceleration; These represent the minimum and maximum track angles, respectively. This indicates the maximum angular velocity.
[0037] Based on the above motion model, through a pre-set control quantity This allows us to obtain the intermediate state variables of the lead machine. .
[0038] The control variables of the wingman need to be determined using the passive sliding mode method provided by this invention. Once the control variables are determined, the intermediate state variables of the wingman can be obtained based on the aforementioned UAV motion model. ,in, This refers to the wingman's sequence number. During initialization, the initial state of the wingman can be set first, and then used as the intermediate state of the wingman at the initial moment, setting the current state. The intermediate state quantity of the wingman changes from the previous moment. Calculation of intermediate state quantities of the wingman.
[0039] Step 2: Based on the preset communication model and topology, and using the consensus estimation algorithm, obtain the wingman's state variables for the alpha aircraft.
[0040] In practice, a distributed control approach is adopted. In this approach, only a portion of the wingmen in the cluster communicate with the virtual leader, while the other portion communicates only with their neighboring drones. To achieve distributed formation control under this incomplete communication scenario, this embodiment employs a consensus estimation algorithm, allowing drones unable to communicate with the virtual leader to indirectly estimate its state information based on the state information of their neighbors. The specific expression of the consensus estimation algorithm is as follows: ; in, Indicates the first The wingman estimates the virtual alpha state vector based on the consensus estimation algorithm; This represents the state vector of the virtual primary machine itself; Indicates the first Communication relationships between wingmen This indicates communication between the two wingmen. This indicates that the two do not communicate with each other; Indicates the first The degree of interdependence between wingmen; Indicates the relationship with the first A group of wingmen who communicate with each other.
[0041] Next, this embodiment establishes a reasonable communication topology and obtains the intermediate state estimates of the wingman to the alpha machine based on the algorithm described above. It should be noted that a reasonable communication topology means that every wingman has a reachable communication topology to the virtual alpha machine, thus ensuring the estimation... Eventually converges to .
[0042] Step 3: Based on the intermediate state quantities of the lead aircraft, the intermediate state quantities of the wingman, and the estimated state quantities of the wingman relative to the lead aircraft, the distributed formation error quantity of the wingman is obtained based on the distributed formation control error model.
[0043] In practical implementation, in distributed control, drones that cannot communicate directly with the virtual alpha drone solve for control variables based on the state error between themselves and their neighboring drones. Therefore, the distributed formation error model in this embodiment is divided into two parts: the first part is the state error between the wingman and the virtual alpha drone. For wingmen that can communicate directly with the virtual alpha, the intermediate state of the virtual alpha can be obtained directly. For wingmen that cannot communicate directly with the virtual alpha, the intermediate state of the virtual alpha is estimated using a consensus estimation algorithm. The second part is the sum of the intermediate state errors between the wingman itself and all its neighboring wingmen. .
[0044] The specific expression for the distributed formation error model is as follows: ; in, Indicates the first Formation error vector of wingman aircraft; Indicates the first The expected relative state vector between the wingman and the virtual alpha; Indicates the first The expected relative state vector between wingmen.
[0045] Based on the intermediate state quantities of the lead aircraft, the intermediate state quantities of the wingman, and the estimated intermediate state quantities of the wingman relative to the lead aircraft, the distributed formation error quantity of the wingman in this embodiment can be obtained based on the above-mentioned distributed formation error model.
[0046] Step 4: Based on the port Hamiltonian system theory, the Hamiltonian energy function is defined based on the distributed formation error, thereby transforming the UAV motion model into a port Hamiltonian system form, and further deriving the port Hamiltonian system form of the UAV formation system.
[0047] For example, the 3-DOF nonlinear motion model of a UAV can be described by the following general nonlinear system expression: ; in, , is the status variable of the wingman; , It is a unit column vector; This refers to the control amount of the wingman; ; ; Furthermore, for any wingman All of these can be converted to the following port Hamiltonian form: ; in, For the wingman's status variables; For the control of wingmen; The interconnection matrix satisfies the antisymmetric property; The damping matrix satisfies the positive definite property; Hamiltonian energy function State The partial differential of . ; ; ; ; ; Based on the distributed formation error, the Hamiltonian energy function is defined as follows in this embodiment: ; In particular, Based on the port Hamiltonian system theory, the following form of UAV formation system can be further derived: ; ; .
[0048] Step 5: Based on the port Hamiltonian system form of the UAV formation system, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, thereby deriving the passive sliding mode control quantity of the wingman and inputting it into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.
[0049] In practical implementation, based on the port Hamiltonian system form of the UAV formation system, the following glide modulus is introduced into the Hamiltonian energy function: ; ; Furthermore, based on the port Hamiltonian system theory, the passive sliding mode control quantity of the wingman is obtained: ; To verify the effectiveness of the passive sliding mode method for UAV formation control provided by this invention, simulation experiments were conducted on the method in the embodiments of this invention, as follows: This embodiment uses the motion of a drone formation along a straight trajectory as an example for simulation experiments. Consider a drone formation system consisting of 5 wingmen and 1 virtual leader. In the motion model of each drone... Take respectively For the state constraints of each UAV, select... , , , , , The following incomplete communication topology exists between the five wingmen. ; Two of the wingmen, UAV1 and UAV5, communicated directly with the virtual alpha aircraft.
[0050] In this embodiment, the initial state of the virtual leader is: The initial status of the other 5 wingmen is shown in Table 1.
[0051] Table 1
[0052] In this embodiment, the selection of some parameters is as follows: for the matrix Select Smooth functions Select . Select Selection of sliding surface parameters , Simulation step size selection Total simulation duration selected .
[0053] Figure 2 (a), (b), and (c) respectively show the error variation curves between the relative distance and the expected relative distance between the five wingmen and the virtual lead aircraft in the north, east, and altitude directions; Figure 2Figures (d), (e), and (f) show the error curves of speed, heading angle, and track angle between the five wingmen and the virtual lead aircraft. It can be seen that the six state errors of the five wingmen all converge to zero, indicating that the passive sliding mode method of this invention can enable the UAV's state trajectory to stably converge to the desired state value.
[0054] Figure 3 Images (a), (b), and (c) show the time-varying curves of the three components of the sliding surface in the passive sliding mode method. It can be seen that all three components of the sliding mode quantity converge to near zero within a finite time, indicating that the passive sliding mode method of this invention can make the system state converge to the sliding surface and converge along the sliding surface to the desired value.
[0055] Specifically, to verify the robustness of the passive sliding mode method of the present invention, this embodiment also considers the effect of disturbance. The disturbance is selected as... ; It should be noted that the disturbance is applied at the point shown in the following formula in this embodiment. ; Figure 4 (a), (b), (c), (d), and (e) show five wingmen. The value change curve. It can be seen that there are 5 wingmen. The fact that all values converge to an upper bound indicates that the states of all wingmen converge to the set. The robustness of the passive sliding mode method of the present invention is illustrated in the figure.
[0056] The passive sliding mode method for distributed control of UAV formations provided in this embodiment directly models the UAV formation system within a port Hamiltonian framework, based on the UAV motion model, which fully reflects the nonlinear characteristics of the UAV formation model. Within the port Hamiltonian framework, the control quantities of the wingmen in the UAV formation system are directly designed from an energy perspective, which facilitates the use of existing energy shaping techniques. Based on the distributed formation error model, the desired Hamiltonian energy function is defined, and a sliding mode quantity is introduced into the Hamiltonian energy function, so that the designed wingman control quantities can not only ensure the stability of the UAV formation system, but also retain a certain degree of robustness to disturbances.
[0057] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.
[0058] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A passive sliding mode method for distributed control of UAV formations, characterized in that, include: Step 1: Based on the UAV motion model and the preset initial state of the lead aircraft, and under the action of the preset control variables, obtain the intermediate state of the lead aircraft, 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. Step 2: Based on the preset communication model and topology, and using the consensus estimation algorithm, obtain the wingman's state variables for the alpha aircraft. Step 3: Based on the intermediate state quantities of the lead aircraft, the intermediate state quantities of the wingman, and the estimated state quantities of the wingman relative to the lead aircraft, the distributed formation error quantity of the wingman is obtained based on the distributed formation control error model. Step 4: Based on the port Hamiltonian system theory, the Hamiltonian energy function is defined based on the distributed formation error, thereby transforming the UAV motion model into a port Hamiltonian system form, and further deriving the port Hamiltonian system form of the UAV formation system. Step 4 specifically includes: Step 4.1, the UAV motion model is described as the following general nonlinear system expression: in, , is the status variable of the wingman; , It is a unit column vector; This refers to the control amount of the wingman; Furthermore, for any wingman Convert it to the following port Hamiltonian form: in, For the wingman's status variables; For the control of wingmen; The interconnection matrix satisfies the antisymmetric property; The damping matrix satisfies the positive definite property; Hamiltonian energy function State The partial derivative, ; ; ; ; ； Step 4.2, based on the distributed formation error, define the Hamiltonian energy function as follows: ； Step 4.3, let Based on the port Hamiltonian system theory, the port Hamiltonian system form of the UAV formation system is derived as follows: ； Step 5: Based on the port Hamiltonian system form of the UAV formation system, the sliding mode quantity is introduced into the Hamiltonian energy function of the system, thereby deriving the passive sliding mode control quantity of the wingman and inputting it 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, Step 1 specifically includes: Step 1.1, define the expression for the UAV motion model as follows: in, Indicates the wingman's serial number; They represent the first The three-dimensional position coordinates of the wingman in the northeast-central coordinate system; They represent the first The wingman's speed scalar, heading angle, and track angle; They represent the first Speed, heading, and track angle commands for wingmen; These represent the time inertia constants for the autopilot's speed, heading angle, and track angle channels, respectively, with units of 1. ; These represent the minimum and maximum flight speeds, respectively. Indicates the maximum acceleration; These represent the minimum and maximum track angles, respectively. Indicates the maximum angular velocity; Step 1.2: Based on the preset initial state of the servo machine, set the preset control quantity... Input the UAV motion model to obtain the intermediate state variables of the lead UAV; 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 quantity of the wingman changes from the previous moment. Calculation of intermediate state quantities of the wingman.
3. The method according to claim 2, characterized in that, The expression for the consensus estimation algorithm is: in, Indicates the first The wingman estimates the virtual alpha state vector based on the consensus estimation algorithm; This represents the state vector of the virtual primary machine itself; Indicates the first Communication relationships between wingmen This indicates communication between the two wingmen. This indicates that the two do not communicate with each other; Indicates the first The degree of interdependence between wingmen; Indicates the relationship with the first A group of wingmen who communicate with each other.
4. The method according to claim 3, characterized in that, The expression for the distributed formation control error model is as follows: in, Indicates the first Formation error vector of wingman aircraft; Indicates the first The expected relative state vector between the wingman and the virtual alpha; Indicates the first The expected relative state vector between wingmen; This indicates the state error between the wingman and the virtual alpha aircraft. This represents the sum of intermediate state errors between the wingman itself and all its neighboring wingmen.
5. The method according to claim 4, characterized in that, Step 5 specifically includes: Step 5.1, based on the port Hamiltonian system form of the UAV formation system, introduce a sliding mode into the Hamiltonian energy function: ； Step 5.2, based on the port Hamiltonian system theory, obtain the passive sliding mode control quantity of the wingman: ； Step 5.3: Input the passive sliding mode control quantity of the wingman into the UAV motion model to obtain the intermediate state quantity of the wingman at the next moment.