Unmanned aerial vehicle cluster cooperative tracking method based on fixed time sliding surface

Abstract

The application provides a UAV cluster cooperative tracking method based on a fixed time sliding mode surface, comprising the following steps: S100, determining expected positions and expected speeds of each UAV in the UAV cluster at a preset time according to a preset formation of the UAV cluster; S200, defining variables for each UAV in the UAV cluster based on the expected positions and the expected speeds; S300, designing a fixed time sliding mode surface for each UAV based on the defined variables; S400, calculating a control law of each UAV and determining parameters of the control law based on the fixed time sliding mode surface and requirements of the UAV; and S500, controlling the corresponding UAV to reach the preset expected positions and the expected speeds within a preset time through the control law. The application can guarantee that the cluster completes formation change and reconstruction within a specified time, and meets the demand of the UAV for adapting to complex terrains and performing complex tasks; and the control law is in an analytical form, so the control is easy to implement.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm technology, and in particular to a UAV swarm cooperative tracking method based on a fixed-time sliding surface. Background Technology

[0002] For drone swarms, forming a formation shortly after takeoff can enhance combat effectiveness and extend the swarm's operational time. Simultaneously, when performing missions such as attack, obstacle avoidance, and canyon traversal, the swarm needs to change and reconfigure its formation to adapt to terrain changes and mission requirements. Because the battlefield situation is constantly changing, and the positions of enemy targets and obstacles are also constantly shifting, the ability of the swarm to change formation before colliding with obstacles after detecting them and making decisions becomes a critical issue. Therefore, the technology for coordinated tracking of desired flight paths by individual drones within the swarm within a specified timeframe is a key technology that urgently needs to be mastered.

[0003] Unmanned aerial vehicle (UAV) cooperative tracking and control involves each UAV in a formation designing a formation control strategy based on its own and neighboring UAV states within a specified time, thereby tracking the desired flight path and ultimately completing the formation change and reconstruction of the cluster.

[0004] The main issues involved include:

[0005] 1) Design of a fixed-time sliding surface for a drone model;

[0006] 2) Design of distributed fixed-time formation control strategy for UAV swarm system. Summary of the Invention

[0007] The technical problem to be solved by this invention is how to design a distributed cooperative tracking control law for UAV swarms so that the swarm can form or reconstruct its formation within a specified time. This invention proposes a UAV swarm cooperative tracking method based on a fixed-time sliding surface.

[0008] The UAV swarm cooperative tracking method based on a fixed-time sliding mode surface according to embodiments of the present invention includes:

[0009] Based on the preset formation of the drone swarm, determine the desired position and speed of each drone in the swarm at a preset time.

[0010] Based on the desired position and desired speed, variables are defined for each drone in the drone swarm;

[0011] Based on the defined variables, design the fixed-time sliding mode surface for each UAV;

[0012] Based on the fixed-time sliding surface and the requirements of the mission for the UAV, the control law for each UAV is calculated and the parameters of the control law are determined.

[0013] The control law controls the corresponding UAV to reach the preset desired position and speed within a preset time.

[0014] According to some embodiments of the present invention, variables are defined for each drone in a drone swarm, including:

[0015] Define variables p for the i-th and j-th drones in the cluster. i (t) and q i (t) is as follows:

[0016]

[0017] Where Γ is a finite set of points containing all drone IDs in the drone swarm, and ai j This represents the communication relationship between the i-th drone and the j-th drone, b i This represents the communication relationship between the i-th drone and the virtual navigator; x i and x j Indicates the positions of the i-th and j-th drones, v i and v j x represents the speed of the i-th and j-th drones; di Let v represent the desired location of the i-th drone. di Let N represent the expected speed of the i-th drone, and N represent the total number of drones.

[0018] In some embodiments of the present invention, when the i-th UAV is able to receive the information sent by the j-th UAV, a ij =1, otherwise a ij =0; when the i-th drone is able to receive the information from the virtual navigator, b i =1, otherwise b i =0.

[0019] According to some embodiments of the present invention, the fixed-time sliding surface designed for the i-th UAV in the cluster is as follows:

[0020]

[0021] Where α > 0, β > 0, 1 < a2 < 2, a2 ​​= m / n, m and n are positive odd numbers, and a1 > a2.

[0022] In some embodiments of the present invention, the control law of the i-th UAV is calculated according to the following formula:

[0023]

[0024] Among them, s i Indicates a sliding surface at a fixed time. It is a cluster of drones The maximum absolute value, satisfy:

[0025]

[0026] Where τ is a positive constant.

[0027] According to some embodiments of the present invention, it includes:

[0028] Each drone in the drone swarm is equipped with an IMU, a magnetic compass, and GPS to measure and obtain the position and speed information of each drone.

[0029] In some embodiments of the present invention, the drones in the drone cluster exchange position and speed information with each other via a data transmission radio.

[0030] According to some embodiments of the present invention, each of the drones in the drone swarm is equipped with an actuator that responds to control inputs.

[0031] In some embodiments of the present invention, there is full-duplex communication between the drones in the drone cluster.

[0032] According to some embodiments of the present invention, each drone in the drone swarm tracks its own virtual navigator during formation.

[0033] The UAV swarm cooperative tracking method based on a fixed-time sliding mode surface proposed in this invention has the following beneficial effects:

[0034] The method proposed in this invention can ensure that the cluster completes formation change and reconstruction within a specified time. Since the UAV can complete formation change within a fixed time, it better meets the needs of UAV to adapt to complex terrain and perform complex tasks. Since the control law is in analytical form, the control is easy to implement. Attached Figure Description

[0035] Figure 1 This is a flowchart of a UAV swarm cooperative tracking method based on a fixed-time sliding surface according to an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of the drone swarm formation process according to an embodiment of the present invention;

[0037] Figure 3 This is a diagram showing the location changes of a drone swarm according to an embodiment of the present invention.

[0038] Figure 4This is a graph showing the speed variation of a drone swarm according to an embodiment of the present invention.

[0039] Figure 5 This is a diagram illustrating the tracking error of a drone swarm according to an embodiment of the present invention. Detailed Implementation

[0040] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0041] The steps described in the specification and the flowcharts in the accompanying drawings of this invention are not necessarily to be strictly followed according to the step numbers; the execution order of the steps can be changed. Furthermore, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be broken down into multiple steps.

[0042] Most existing cooperative tracking technologies are based on asymptotic stability theory, a few are based on finite time theory, and most cooperative modes are based on the lead-wingman mode.

[0043] The main drawbacks of existing technological inventions are as follows:

[0044] Most existing swarm cooperative tracking technologies are based on asymptotic convergence theory. The drawback of this method is that it cannot provide a clear time limit for tracking the ideal trajectory, which limits the ability to perform urgent tasks.

[0045] This invention provides an feasible solution to the "cooperative tracking and control problem of UAV swarms with time constraints" based on a fixed-time sliding surface and multi-agent consensus. This solution enables UAV swarms to complete cooperative tracking of an ideal trajectory within a specified time.

[0046] It is worth emphasizing that although sliding mode control has applications in other fields, it is usually applied to a single object and does not involve the interaction and collaborative control between objects. Therefore, it cannot be directly transferred to the collaborative tracking of drone swarms.

[0047] To address the shortcomings of existing technologies, this invention proposes a collaborative tracking method for UAV swarms based on a fixed-time sliding mode surface.

[0048] like Figure 1 As shown, the UAV swarm cooperative tracking method based on a fixed-time sliding mode surface according to an embodiment of the present invention includes:

[0049] S100, based on the preset formation of the drone swarm, determines the desired position and speed of each drone in the drone swarm at a preset time.

[0050] S200 defines variables for each drone in a drone swarm based on the desired position and desired speed.

[0051] S300, based on defined variables, designs a fixed-time sliding mode surface for each UAV;

[0052] S400, based on a fixed-time sliding mode surface and the requirements of the mission for the UAV, calculates the control law for each UAV and determines the parameters of the control law;

[0053] The S500 uses a control law to control the corresponding drone to reach the preset desired position and speed within a preset time.

[0054] According to some embodiments of the present invention, variables are defined for each drone in a drone swarm, including:

[0055] Define variables p for the i-th and j-th drones in the cluster. i (t) and q i (t) is as follows:

[0056]

[0057] Where Γ is a finite set of points containing all drone IDs in the drone swarm, and a ij This represents the communication relationship between the i-th drone and the j-th drone, b i This represents the communication relationship between the i-th drone and the virtual navigator; x i and x j Indicates the positions of the i-th and j-th drones, v i and v j x represents the speed of the i-th and j-th drones; di Let v represent the desired location of the i-th drone. di Let N represent the expected speed of the i-th drone, and N represent the total number of drones.

[0058] In some embodiments of the present invention, when the i-th UAV is able to receive the information sent by the j-th UAV, a ij =1, otherwise a ij =0; when the i-th drone is able to receive the information from the virtual navigator, b i =1, otherwise b i =0.

[0059] According to some embodiments of the present invention, the fixed-time sliding surface designed for the i-th UAV in the cluster is as follows:

[0060]

[0061] Where α > 0, β > 0, 1 < a2 < 2, a2 ​​= m / n, m and n are positive odd numbers, and a1 > a2.

[0062] In some embodiments of the present invention, the control law of the i-th UAV is calculated according to the following formula:

[0063]

[0064] Among them, s i Indicates a sliding surface at a fixed time. It is a cluster of drones The maximum absolute value, satisfy:

[0065]

[0066] Where τ is a positive constant.

[0067] According to some embodiments of the present invention, it includes:

[0068] Each drone in the drone swarm is equipped with an IMU, a magnetic compass, and GPS to measure and obtain the position and speed information of each drone.

[0069] In some embodiments of the present invention, the drones in the drone swarm exchange position and speed information with each other via a data transmission radio.

[0070] According to some embodiments of the present invention, each drone in a drone swarm is equipped with an actuator that responds to control inputs.

[0071] In some embodiments of the present invention, there is full-duplex communication between the drones in the drone swarm.

[0072] According to some embodiments of the present invention, each drone in a drone swarm tracks its own virtual navigator during formation.

[0073] The UAV swarm cooperative tracking method based on a fixed-time sliding mode surface proposed in this invention has the following beneficial effects:

[0074] The method proposed in this invention can ensure that the cluster completes formation change and reconstruction within a specified time. Since the UAV can complete formation change within a fixed time, it better meets the needs of UAV to adapt to complex terrain and perform complex tasks. Since the control law is in analytical form, the control is easy to implement.

[0075] The UAV swarm cooperative tracking method based on a fixed-time sliding mode surface according to the present invention is described in detail below with reference to the accompanying drawings. It is to be understood that the following description is merely exemplary and should not be construed as a specific limitation of the present invention.

[0076] This invention is based on the following assumptions:

[0077] 1. Each drone in the drone swarm is equipped with an IMU, magnetic compass and GPS, which can measure its own position and speed information and transmit its own position and speed information to other drones in the swarm via data radio; it is also equipped with actuators that can quickly respond to control inputs.

[0078] 2. There is full-duplex communication between drones in the cluster. The communication relationship can be abstracted as a topology graph, and it is assumed that the communication topology graph is an undirected connected graph.

[0079] 3. During formation, each drone in the cluster tracks its own virtual navigator. The position and velocity of the navigator at time t are x and x, respectively. di (t) and v di (t).

[0080] Consider a swarm system consisting of N drones, with communication links between them transmitting state information of neighboring drones. The communication topology between drones is described by an undirected graph G, where the i-th drone is considered a node in the graph. The i-th drone is treated as a point mass, and its dynamics model uses a second-order integrator model.

[0081]

[0082] Where i = 1, 2, ..., N, x i (t) and v i (t) represent the position and velocity of the i-th UAV at time t, respectively. i (t) represents the control input for the i-th UAV. Assuming the three-dimensional motions of the UAVs are decoupled, x here... i (t) and v i (t) only represents the motion of the UAV in one dimension of the three-dimensional coordinate system, but the conclusion still applies to two-dimensional plane and three-dimensional space.

[0083] Let the i-th drone and the j-th drone in the cluster be at time... t The expected position is x di (t) and x dj (t), desired speed is v di (t) and v dj (t).

[0084] The problem of cooperative tracking and control of drone swarms involves designing a controller for each drone in the swarm. i (t) enables each drone to track its ideal position trajectory and maintain an ideal speed, forming a designated formation.

[0085] Step 1: Design of a fixed-time sliding surface;

[0086] First, define variables p for the i-th and j-th drones in the cluster. i (t) and q i (t), defined as follows:

[0087]

[0088] Where Γ is a finite set of points containing the IDs of all drones in the cluster, and a ij This represents the communication relationship between the i-th drone and the j-th drone, b i This represents the communication relationship between the i-th drone and the virtual navigator. Specifically, when the i-th drone is able to receive the information sent by the j-th drone, a ij =1, otherwise a ij =0; similarly, when the i-th drone is able to receive the information from the virtual navigator, b i =1, otherwise b i =0.

[0089] Secondly, a fixed-time sliding mode surface is designed for the i-th drone in the cluster as follows:

[0090]

[0091] Where α > 0, β > 0, 1 < a2 < 2, a2 ​​= m / n, m and n are positive odd numbers, and a1 > a2.

[0092] Step two, calculate the control law;

[0093] Calculate the control law for the i-th UAV using the following formula:

[0094]

[0095] Among them, s i Indicates a sliding surface at a fixed time. It is a cluster of drones The maximum absolute value. satisfy:

[0096]

[0097] Where τ is a positive constant.

[0098] The following is a specific example of the UAV swarm cooperative tracking method based on a fixed-time sliding mode surface according to the present invention:

[0099] In the example, the drone swarm consists of 5 fixed-wing drones. Since the method in this invention is designed for the movement of drones in one dimension, the 5 drones are confined to a flight path with a spacing of 6 meters. The initial formation is a "\" shaped formation, and the desired formation is a " / " shaped formation.

[0100] The drone swarm formation process is as follows Figure 2 As shown, the swarm initially formed a "\" shaped formation, and after speed adjustments, eventually formed the desired " / " shaped formation. The curves showing the position and speed of the drone swarm over time are as follows: Figure 3 and Figure 4 As shown, the drone furthest from the desired position has the highest speed in the initial stage, while the speed of all drones stabilizes at 20 m / s after tracking the desired position. The tracking error curve of the drone swarm is shown in the figure. Figure 5 As shown, the tracking error between the initial position and the initial desired position of each drone is within the range of 6 meters to 194 meters. After speed adjustment, the tracking error quickly converges to zero.

[0101] In summary, the method in this invention can ensure that the cluster completes formation changes and reconstruction within a specified time. Since UAVs can complete formation changes within a fixed time, it better meets the needs of UAVs to adapt to complex terrain and perform complex tasks. Since the control law is in analytical form, control is easy to implement.

[0102] In addition to employing a fixed-time sliding mode strategy, this invention can also utilize homogeneity methods to achieve fixed-time convergence.

[0103] Through the description of specific embodiments, a more in-depth and specific understanding should be gained of the technical means and effects adopted by the present invention to achieve the intended purpose. However, the accompanying drawings are only provided for reference and illustration and are not intended to limit the present invention.

Claims

1. A method for cooperative tracking of UAV swarm based on fixed-time sliding mode surface, characterized in that, Comprise: According to the preset formation shape of the UAV cluster, determine the expected position and expected speed of each UAV in the UAV cluster at the preset time; Based on the expected position and expected speed, define variables for each UAV in the UAV cluster; Based on the defined variables, design a fixed-time sliding mode surface for each UAV; Based on the fixed-time sliding mode surface and the requirements of the task on the UAV, calculate the control law of each UAV and determine the parameters of the control law; Control the corresponding UAV to reach the preset expected position and expected speed within the preset time through the control law; Define variables for each UAV in the UAV cluster, comprising: Define variables for the first i and second j drones in the swarm and as follows: ； wherein, is a finite set of points numbered for all UAVs in the UAV swarm, denotes the communication relationship between the i th UAV and the j th UAV, denotes the communication relationship between the i th UAV and the virtual leader; x i and x j denotes the position of the th and the j th UAV, v i and v j denotes the velocity of the th and the j th UAV; x di denotes the desired position of the th UAV, v di denotes the desired velocity of the i th UAV, N denotes the total amount of UAVs; When the i The drone can receive the first j When a drone sends information ,on the contrary When the first i When a drone is able to receive information from a virtual navigator ,on the contrary ; The fixed time sliding mode surface designed for the i-th UAV in the cluster is as follows: i ​ ； wherein , , , , m , n is a positive odd integer, ; The control law of the i-th UAV is calculated according to the following formula: i = 1, 2,..., N ； wherein, represents a fixed-time sliding mode surface, is a UAV maximum value of absolute value, satisfies: ； wherein is a normal number.

2. The method of claim 1, wherein, Comprise: Each UAV in the UAV cluster is equipped with an IMU element, a magnetic compass and a GPS to measure and obtain the position and speed information of each UAV.

3. The method of claim 1, wherein, Each of the UAVs in the UAV cluster interacts with each other through a data radio station to exchange position and speed information.

4. The method of claim 1, wherein, Each of the UAVs in the UAV cluster is equipped with an actuator that responds to control input.

5. The method of claim 1, wherein, There is duplex communication between each of the UAVs in the UAV cluster.

6. The method of claim 1, wherein, Each of the UAVs in the UAV cluster follows a virtual leader during formation.

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