A method for interference encirclement control of a UAV cluster in a non-ideal communication environment

Abstract

This invention discloses a method for controlling UAV swarm interference encirclement under non-ideal communication environments. First, a three-dimensional spatial system model of a single UAV is established. The UAV swarm is divided into three categories: reference leader UAV, formation leader UAV, and follower UAV. The impact of external interference on the formation leader UAV and follower UAV is considered separately, and second-order dynamic equations are established. Graph theory is introduced to describe the information interaction between UAVs in the swarm and to complete formation. A time-delay-dependent dynamic event triggering mechanism is provided for each formation leader UAV and follower UAV. An error system equation for the UAV swarm under non-ideal communication environments is established, and external interference is suppressed. Through the dynamic event triggering mechanism, redundant data transmitted by UAVs is effectively reduced, and the network transmission burden is lowered. Even with delays and packet loss caused by non-ideal communication environments, effective data transmission between UAVs in the swarm can be guaranteed.

Description

Technical Field

[0001] This invention relates to unmanned aerial vehicle (UAV) control, specifically to a method for controlling UAV swarm interference and encirclement under non-ideal communication environments. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields due to their high controllability, ease of operation, ability to carry various mission equipment, and low power consumption. Compared to a single UAV, UAV swarms, through mutual cooperation, improve mission completion efficiency, increase UAV survivability in combat, and reduce the risk of personnel casualties. Therefore, UAV swarms play an important role in modern warfare. In civilian applications, large numbers of UAVs can be used for holiday performances, agricultural irrigation to save labor, express delivery, geological surveys in dangerous areas, photography, and more. In short, collaborative control of UAV swarms can fully utilize the limited resources of individual UAVs to accomplish more complex tasks.

[0003] Encirclement control of drone swarms is a special form of collaborative control of drone formations, typically applied in the following scenarios. As is well known, drones carrying specialized equipment are usually expensive, and if they encounter obstacles or enemy military attacks during flight, the losses can be enormous. Therefore, using a swarm of drones with detection capabilities to protect a group of drones carrying specialized equipment within a safe zone can effectively avoid these problems. Even if obstacles or enemy attacks are encountered, it can buy valuable escape time for the expensive drone swarm; this is the significance of encirclement control methods.

[0004] However, drone swarms inevitably encounter external interference during formation flight, such as wind and airflow interference. This interference not only makes it more difficult for drones to form formations but also damages the drones themselves, leading to economic losses.

[0005] Communication technology is one of the decisive factors for the success of drone swarm formation. It is well known that numerous drones, whether in flight or performing missions, need to communicate with other drone members to better coordinate and cooperate. If the communication environment is unfavorable, it will directly affect the mission execution of the drone swarm. Therefore, how to improve data transmission efficiency using limited network resources, while considering network latency encountered during data transmission by the drone swarm, and how to design drone controllers to avoid affecting information exchange between drones, are issues worthy of research. Summary of the Invention

[0006] Purpose of the invention: To address the above-mentioned shortcomings, this invention provides a method for controlling UAV swarm interference encirclement in the event of latency and packet loss in an undesirable communication environment.

[0007] Technical Solution: To solve the above problems, this invention employs a method for controlling UAV swarm interference and encirclement under non-ideal communication environments, comprising the following steps:

[0008] (1) Establish a three-dimensional spatial system model of a single UAV;

[0009] (2) The drone swarm is divided into three categories: the first is the reference leader drone, which is used to provide the reference trajectory for the drone swarm flight; the second is the formation leader drone, which is used to realize the encirclement formation; and the third is the follower drone. The impact of external interference on the formation leader drone and the follower drone is considered respectively, and the second-order dynamic equations of the formation leader drone and the follower drone are established respectively.

[0010] (3) Introduce graph theory to describe the information interaction between drone swarms, so that the follower drones can interact with each other to complete the formation and achieve consistency, and the formation leader drones can interact with each other to complete the formation.

[0011] (4) Provide a time-delay-dependent dynamic event triggering mechanism for each formation leader drone and follower drone to solve the network latency and data redundancy problems in non-ideal communication environments; use graph theory to describe the information interaction between drone clusters and provide a controller with anti-interference performance to track the flight trajectory of the reference leader drone and achieve encirclement formation consistency.

[0012] (5) Establish the error system equations for UAV swarms under non-ideal communication environments, obtain the consistency conditions and stability conditions of the error system for UAV swarms to achieve encirclement formation, and utilize H ∞ The method suppresses external interference and ultimately obtains the controller gain expression for the encirclement control of the UAV swarm.

[0013] Beneficial effects: Compared with the prior art, the significant advantage of this invention is that it can automatically adjust the trigger threshold according to the current state of the system through a dynamic event triggering mechanism, effectively reducing redundant data transmitted by drones and lowering the network transmission burden. Even with latency and packet loss caused by imperfect communication environments, it can still ensure the effective transmission of data between drone clusters. Furthermore, by introducing H... ∞ Control methods are used to suppress the negative impact of external interference on drone swarms. Attached Figure Description

[0014] Figure 1 The diagram shown is a flowchart of the encirclement control design in this invention.

[0015] Figure 2 The diagram shown is a schematic of the UAV formation encirclement control flight of the present invention. Detailed Implementation

[0016] like Figure 1 As shown in this embodiment, a method for controlling UAV swarm interference and encirclement under non-ideal communication conditions includes the following steps:

[0017] Step 1: First, based on UAV flight theory, establish a three-dimensional spatial system model of a single UAV;

[0018] Step 2: Divide the drone swarm into three categories: first, the reference leader drone, which provides a reference trajectory for the drone swarm flight; second, the formation leader drone, which is used to achieve encirclement formation; and third, the follower drone swarm. Consider the impact of external interference on the formation leader drone and the follower drones separately, and establish the second-order dynamic equations for the formation leader and the follower drones respectively.

[0019] Step 3: Introduce graph theory to describe the information interaction between drone swarms. Not only do follower drones need to interact to form up and achieve consistency, but the navigator drone swarm can also interact to form up.

[0020] Step 4: Design a latency-dependent dynamic event triggering mechanism for each leader and follower drone in the formation to reduce the burden on network transmission, solve the problems of network latency and data redundancy in non-ideal communication environments, use graph theory to describe the information interaction between drone clusters, and design a controller with anti-interference performance that can not only track the flight trajectory of the reference leader drone, but also achieve the consistency of the encirclement formation.

[0021] Step 5: Establish the error system equations for UAV swarms under non-ideal communication environments, obtain the consistency conditions and stability conditions of the error system for UAV swarms to achieve encirclement formation, and utilize H... ∞ The method suppresses external interference and ultimately obtains the controller gain expression for the encirclement control of the UAV swarm.

[0022] Establish a three-dimensional dynamic model of the UAV:

[0023]

[0024] In the formula, x i (t),y i (t),z i (t) represent the coordinates of the i-th UAV in three-dimensional space, V i (t)>0 indicates the speed of the drone, α i (t) and β i(t) represent the azimuth and yaw angles of the UAV's trajectory, respectively, and cosα i (t)≠0. δ xi (t),δ yi (t),δ zi (t) represents the acceleration of the drone, and g is the neutral acceleration of the drone.

[0025] For x respectively i (t),y i (t),z i Taking the second derivative of (t), we can obtain the second-order dynamic model of the UAV:

[0026]

[0027] definition New control input And consider external interference d lk (t) and d fi (t), the models of the formation leader and follower drones can be represented by the following equations:

[0028]

[0029] In the formula, This represents the state vector of the formation leader drone. U represents the state vector of the follower drone. lk (t) and u fi (t) represent the control inputs for the leader and follower drones, respectively. The trajectory equation of the reference leader can be obtained through Description, in which By introducing the distance vector S lk S represents the ideal position offset of the formation leader. fi This represents the expected position offset of the follower drone. Define the error vector e. lk (t)=l k (t)-S lk (t)-l0(t), Indicates the ideal position of the formation leader, c k It is a constant, satisfying Based on the above analysis and equation (3), the error system equations for the formation leader and follower UAVs can be obtained as follows:

[0030]

[0031] Of the N+1 drones, one is the reference leader drone, M are the leader drone swarm, and NM are the follower drone swarm. Introducing a directed graph G, [b ij The adjacency matrix represents the connection between the i-th drone and the j-th drone, where i = 0, 1, ..., N, j = 1, ..., N. If b ij =1 indicates that the j-th drone can receive information from the i-th drone, b ij =0 means that the j-th drone cannot receive information from the i-th drone. Note b ii =0. Definition D r =diag{b 10 ,...,b M0}, Laplace matrix Based on the above description, the controllers for the formation leader and followers can be represented as follows:

[0032]

[0033] In the formula, K l1 K l2 K f1 and K f2 This indicates the gain of the controller in the design.

[0034] The following design incorporates a latency-dependent dynamic event triggering mechanism. It compares the currently collected data with the latest triggered data. If the condition in equation (6) is met, the currently collected data is discarded; otherwise, the currently collected data is triggered. A dynamic triggering threshold function μ is also designed. lk exp(-σ lk ||e lk (t)||),μ fi exp(-σ fi ||e fi (t)||) can dynamically adjust the threshold range based on the current system state, improving the flexibility of event triggering conditions and also increasing network data transmission efficiency. The designed dynamic event triggering mechanism is as follows:

[0035]

[0036] Where ε lk (t)=e lk (t s )-e lk (t) represents the current sampled signal e in the k-th formation leader drone. lk(t) and the most recently triggered signal e lk (t s The error ε fi (t)=e fi (t n )-e fi (t) represents the current sampled signal e in the i-th follower drone. fi (t) and the most recently triggered signal e fi (t n The error is denoted as μ, where s,n=1,2,... lk ,μ fi ∈[0,1],σ lk >0,σ fi Values ​​greater than 0 all represent constants. Φ lk ,Φ fi Let represent the positive definite symmetric matrix to be determined. Assume the network delays experienced by the leader drone and the follower drones in the formation are τ and τ, respectively. l (t)∈[0,τ lM ],τ f (t)∈[0,τ fM ], τ lM >0,τ fM >0 indicates the maximum value of the time delay. t s =t-τ l (t),t n =t-τ f (t). Combining the time-delay-dependent dynamic event triggering mechanism and the controller in equation (5), the formation leader and follower UAV error system in equation (4) can be represented as follows:

[0037]

[0038] By combining the Kronecker product technique, the following system equations for the UAV swarm error can be obtained:

[0039]

[0040] Where C is a known matrix of suitable dimension, and the other matrices are described in detail below:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] The stability conditions for the UAV swarm error system that achieves consistent encirclement control are as follows:

[0055] For a given adaptive event triggering mechanism parameter μ lk ,μ fi ∈[0,1],σ lk >0,σ fi >0, delay over the previous τ lM ,τ fM With the interference suppression parameter λ > 0, combined with the designed time-delay-dependent adaptive event-triggered anti-interference control gain K... l1 ,K l2 ,K f1, K f2 If there exist positive definite symmetric matrices P1, P2, R l R f Q l Q f Φ lk and Φ fi The free weight matrices F1, F2, F3, and F4 of suitable dimension can satisfy the following inequality:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

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[0068]

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[0070]

[0071]

[0072]

[0073]

[0074] The Lyapunov function is constructed as follows:

[0075]

[0076] in By differentiating V(t), we obtain the following:

[0077]

[0078] By using the free weight matrix, we can obtain:

[0079]

[0080] Where, ξ T (t=)[e T (t-) T τe(-t M T (τt)e)ε],

[0081] F1 = [F1 1 0 F1 3 0 0 0 0 0 0 0],

[0082]

[0083] F3 = [0 F3] 2 0 F3 4 0 0 0 0 0 0],

[0084]

[0085] Considering a latency-dependent adaptive event triggering mechanism, the following inequality can be obtained:

[0086]

[0087] in,

[0088] Φ l =diag{Φ l1 ,...,Φ lM},Φ f =diag{Φ f(M+1) ,...,Φ fN},μ l =diag{μ l1 ,...,μ lM},μ f =diag{μ f(M+1) ,...,μ fN}

[0089] Combining equations (9)-(13), we can obtain the following inequalities:

[0090]

[0091] Equation (14) shows that the analyzed UAV swarm error system is asymptotically stable. Below, we define X... l1 =P l BK l1 ,X l2 =P l BK l2 ,X f1 =P f BK f1 ,X f2 =P f BK f2 And for the given parameters κ1>0, κ2>0, we have Based on the above, inequality (10) can be rewritten as:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Therefore, the specific expression of the designed controller gain can be obtained as follows:

[0101]

[0102] in, The element (*) in the diagram represents the element B that is symmetric to element B. T .

[0103] A diagram illustrating the formation and encirclement control of drones is shown below. Figure 2 As shown.

Claims

1. A method for controlling interference and encirclement of a UAV cluster in a non-ideal communication environment, characterized in that, Includes the following steps: (1) Establish a three-dimensional spatial system model of a single UAV; (2) The drone swarm is divided into three categories: the first is the reference leader drone, which is used to provide the reference trajectory for the drone swarm flight; the second is the formation leader drone, which is used to realize the encirclement formation; and the third is the follower drone. The influence of external interference on the formation leader drone and the follower drone is considered respectively, and the second-order dynamic equations of the formation leader drone and the follower drone are established respectively. (3) Introduce graph theory to describe the information interaction between drone swarms, so that the follower drones can interact with each other to complete the formation and achieve consistency, and the formation leader drones can interact with each other to complete the formation. (4) Provide a time-delay-dependent dynamic event triggering mechanism for each leader and follower UAV in the formation to solve the network latency and data redundancy problems in non-ideal communication environments; use graph theory to describe the information interaction between UAV clusters, and provide a controller with anti-interference performance to track the flight trajectory of the reference leader UAV and achieve encirclement formation consistency; design a time-delay-dependent dynamic event triggering mechanism, and also design a dynamic triggering threshold function. This is used to dynamically adjust the threshold range based on the current system state value; the designed dynamic event triggering mechanism is as follows: (6) in, This represents the current sampled signal in the k-th formation leader drone. With the most recent trigger signal The error, This represents the current sampled signal in the i-th follower drone. With the most recent trigger signal The error, of which ; , , Both represent constants; , Denotes an undetermined positive definite symmetric matrix; the network latency experienced by the leader drone and follower drones in the formation is respectively... , , , These represent the maximum latency experienced by the leader drone and the follower drones in the formation, respectively. , ; The currently collected data is compared with the latest triggered data. If the conditions in equation (6) are met, the data currently collected by the UAV is discarded. If the conditions in equation (6) are not met, the data currently collected is triggered. (5) Establish the error system equations of the UAV swarm under non-ideal communication environment, obtain the consistency conditions and stability conditions of the error system for the UAV swarm to achieve encirclement formation, and utilize... The method suppresses external interference and ultimately obtains the controller gain expression for the encirclement control of the UAV swarm.

2. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 1, characterized in that, In step (1), the three-dimensional spatial system model of a single UAV is as follows: (1) in, , , They represent the first The coordinates of the drone in three-dimensional space. Indicates the first The speed of the drone, and , , They represent the first The azimuth and yaw angles of the drone's flight path. ; , , They represent the first The acceleration of the drone in three directions in three-dimensional space. For the neutral acceleration of the drone.

3. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 2, characterized in that, To each , , Taking the second derivative, we can obtain the second-order dynamic model of a single UAV: (2)。 4. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 3, characterized in that, In step (2), define new control inputs. The second-order dynamic equations for the leader and follower drones in the formation are: (3) in, This represents the state vector of the formation leader drone. This represents the state vector of the follower drone. , , , , and These represent the control inputs for the leader drone and the follower drones in the formation, respectively. and These represent external interference from the leader drone and the follower drones in the formation, respectively. , ; The error system equations for the leader and follower drones in the formation are as follows: (4) in, and Let represent the error vectors of the leader drone and the follower drones in the formation, respectively. , ; This indicates the ideal position offset of the formation leader drone. This indicates the expected position offset of the follower drone; This represents the trajectory equation of the reference leader, and ; Indicates the ideal position for the formation leader drone; It is a constant, satisfying .

5. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 4, characterized in that, In step (3), the drone swarm includes N+1 drones, one of which is a reference leader drone, M drones are formation leader drones, and NM drones are follower drones; a directed graph is introduced. , The adjacency matrix represents the relationship between the i-th drone and the j-th drone, where... , ,if This means that the j-th drone can receive information from the i-th drone. This means that the j-th drone cannot receive information from the i-th drone. Note that... ;definition , , , , Laplace matrix Based on the above description, the controllers for the leader and follower drones in the formation can be represented as follows: (5) In the formula, , This indicates the controller gain of the formation leader drone. , This indicates the gain of the controller of the follower drone.

6. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 5, characterized in that, In step (4), combining the time-delay-dependent dynamic event triggering mechanism and the controller in equation (5), the error system of the formation leader UAV and follower UAV in equation (4) is as follows: (7)。 7. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 6, characterized in that, In step (5), the system equations for the UAV swarm error are established as follows: (8) in, For known matrices of suitable dimension, other matrices are described below:

8. The method for controlling UAV swarm interference and encirclement under non-ideal communication environments according to claim 7, characterized in that, In step (5), the stability conditions for the UAV swarm error system to achieve consistent encirclement control are as follows: For a given adaptive event triggering mechanism parameter , , , The previous session was delayed. , Interference suppression parameters Combined with the designed time-delay dependent adaptive event-triggered anti-interference control gain , , , If a positive definite symmetric matrix exists , , , , , , and A free weight matrix of suitable dimension , , and This makes the following inequality satisfy: (9) The Lyapunov function is constructed as follows: (10) in, , , ; Through the Taking the derivative, we get the following: (11) By using the free weight matrix, we obtain: (12) in, , Considering a latency-dependent adaptive event triggering mechanism, the following inequality is obtained: (13) in, , , , ; Combining equations (9) to (13), we obtain the following inequalities: (14) The UAV swarm error system analyzed by equation (14) is asymptotically stable; by defining And for a given parameter There will be Based on the above, inequality (10) can be rewritten as: (15) Therefore, the specific expression of the designed controller gain is as follows: . (16) in, elements in Representation and element Symmetrical elements .

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