A Distributed Cooperative Maneuvering Multi-Target Encirclement and Control Method for Multiple Unmanned Surface Vessels

Abstract

This invention discloses a distributed cooperative maneuvering multi-target encirclement control method for multiple unmanned surface vessels (USVs), belonging to the field of USV cooperative control. To address the shortcomings of existing technologies that cannot achieve real-time multi-USV cooperative encirclement control in communication-constrained and dynamic aquatic environments, the technical solution provided by this invention includes: defining the position, velocity, and heading state of each USV; classifying leaders and followers and generating affine transformation relationships for their configurations; receiving target state information and outputting estimated positions and velocities of the target center; constructing a distributed state observer to generate the desired states of each follower; using the desired states as input to construct a preset time sliding mode controller to achieve real-time tracking of the desired states by each USV; and outputting control signals to drive the USV swarm to perform multi-target encirclement tasks. This method is applicable to multi-USV cooperative encirclement and maneuvering control tasks in communication-constrained and complex aquatic environments.

Description

Technical Field

[0001] Belonging to the field of unmanned surface vessel (USV) collaborative control, a method and system for collaborative encirclement and capture of USVs have been specially designed. Background Technology

[0002] With the increasing complexity of marine missions, unmanned surface vehicle (USV) systems have been widely applied in various fields such as marine environmental monitoring, resource exploration, search and rescue, military reconnaissance, formation warfare, and maritime construction due to their advantages of strong collaborative operation capabilities, flexible deployment, and high mission efficiency. Among the many application scenarios of USV systems, collaborative target encirclement technology, as a key component, is increasingly demonstrating its research value. This technology, through the coordinated cooperation of multiple vessels, encircles, tracks, and captures dynamic targets, showcasing a high degree of system synergy and intelligent control capabilities.

[0003] Currently, research on the cooperative target encirclement problem mainly focuses on single-target encirclement strategies and the construction of fixed formation structures, such as using fixed formations like "circles," "regular polygons," and "ellipses," to achieve target encirclement through center guidance or surrounding methods. For example, some literature proposes encirclement based on artificial potential fields or virtual structures, but these methods mostly rely on static scenario assumptions and ideal communication conditions, and cannot cope with dynamic interference and constraints in real environments. Furthermore, when the number of targets expands to multiple, the computational complexity of target state estimation and formation dynamic response increases significantly, directly leading to a decrease in system control accuracy and response efficiency.

[0004] On the other hand, multi-unmanned surface vessel (USV) systems typically operate in complex aquatic environments, such as narrow channels, winding waterways, or areas fraught with obstacles. In such environments, fixed formation structures become rigid, lacking sufficient scaling, rotation, and deformation capabilities, making it difficult to guarantee the system's environmental adaptability and obstacle avoidance capabilities. Furthermore, most existing multi-target capture schemes rely on centralized control frameworks or global information sharing mechanisms, which are prone to failure under communication constraints and also result in a lack of maneuverability in responding to emergencies.

[0005] In summary, existing technologies have limitations in achieving real-time, autonomous, and multi-target adaptive collaborative capture and control of multiple unmanned surface vessels in communication-constrained and dynamic aquatic environments. Summary of the Invention

[0006] To address the shortcomings of existing technologies in achieving real-time, autonomous, and multi-target adaptive collaborative encirclement and control of multiple unmanned surface vessels (USVs) in communication-constrained and dynamic aquatic environments, the technical solution provided by this invention is as follows:

[0007] A distributed cooperative maneuvering multi-target encirclement and control method for multiple unmanned surface vessels includes:

[0008] The steps for establishing a two-dimensional motion mathematical model of multiple unmanned surface vessels (USVs) and defining the position, velocity, and heading state of each USV are used to construct the state expression of the USV system.

[0009] Leaders and followers are divided according to the preset communication topology, and the affine transformation relationship of the configuration is generated based on the stress matrix, which is used for subsequent steps to control the scaling, rotation and translation of the formation.

[0010] The steps are: receiving target status information, constructing a distributed target center estimator based on local communication neighbor relationships, and outputting the estimated position and velocity values ​​of the target center;

[0011] Based on the leader's state information and the estimated position and velocity of the target center, a distributed state observer is constructed, and combined with historical observation data and event triggering mechanisms, the steps for generating the expected state of each follower are generated.

[0012] Using the desired state as input, a preset time sliding mode controller is constructed. The control input is dynamically generated by combining the current state and the error state, so as to realize the real-time tracking of the desired state by each unmanned surface vessel.

[0013] The output control signal is used to drive the unmanned surface vessel swarm to perform multi-target encirclement missions and continuously adjust the affine formation.

[0014] Furthermore, in a preferred embodiment, the kinematic mathematical model includes the longitudinal velocity, lateral velocity, and bow roll rate of the unmanned surface vessel, and the kinematic system and dynamic system are modeled in a consistent manner by constructing additional dummy variables.

[0015] Furthermore, in a preferred embodiment, the affine transformation relationship is generated driven by the leader positions, and the rotation, scaling, and translation transformation parameters of the overall formation are determined by controlling the positions of at least three leaders.

[0016] Furthermore, in a preferred embodiment, the distributed target centroid estimator constructs an estimation model based on multiple target location information received by the leader, and uses bivariate intermediate states to achieve finite-time convergence of the target centroid estimation.

[0017] Furthermore, in a preferred embodiment, the distributed state observer employs an event-triggered mechanism with memory and is configured with a state buffer of finite length.

[0018] Furthermore, in a preferred embodiment, the sliding mode controller dynamically adjusts by constructing a sliding surface and combining it with the target state error, and a preset time convergence term is introduced into the control law.

[0019] A distributed cooperative maneuvering multi-target encirclement and control device for multiple unmanned surface vessels includes:

[0020] A two-dimensional motion mathematical model of multiple unmanned surface vessels (USVs) is established, defining the position, velocity, and heading state of each USV, which is used to construct a module for the state expression of the USV system.

[0021] Leaders and followers are divided according to the preset communication topology, and the affine transformation relationship of the configuration is generated based on the stress matrix, which is used for subsequent modules to control the scaling, rotation and translation of the formation.

[0022] This module receives target status information, constructs a distributed target center estimator based on local communication neighbor relationships, and outputs the estimated position and velocity values ​​of the target center.

[0023] Based on the leader's state information and the estimated position and velocity of the target center, a distributed state observer is constructed, and a module for generating the expected state of each follower is generated by combining historical observation data and event triggering mechanisms.

[0024] The module uses the desired state as input to construct a preset time sliding mode controller, and dynamically generates control input by combining the current state and error state, so as to realize the real-time tracking of the desired state by each unmanned surface vessel.

[0025] The module outputs control signals to drive the unmanned surface vessel swarm to perform multi-target encirclement missions and continuously adjust the affine formation.

[0026] A computer storage medium for storing a computer program, which, when read by a computer, is used by the computer to execute the method described thereon.

[0027] A computer, including a processor and a storage medium, executes the method when the processor reads a computer program stored in the storage medium.

[0028] A computer program product, which, as a computer program, implements the method when the computer program is executed.

[0029] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows:

[0030] The distributed target center estimator proposed at the communication layer enables each leader UAV to estimate the target center position and velocity based on its own received local target information, without relying on a globally unified information source or centralized computing unit. This approach effectively solves the problem of high dependence on global information in traditional centralized encirclement strategies, and improves the system's practicality and robustness in environments with limited communication or incomplete local perception.

[0031] The distributed state observer designed in the guidance layer, combined with a memory event triggering mechanism, enables followers to accurately observe the desired pose based solely on limited leader information and their own historical states. Compared to existing state feedback mechanisms that require continuous communication or high-frequency updates, this approach improves observation accuracy and update stability by introducing a buffer and fusing historical data, while significantly reducing communication frequency and minimizing system load and latency risks.

[0032] The pre-set time dynamics controller in the control layer design, combining sliding mode control with pre-set time convergence characteristics, enables rapid tracking of the desired state within a specified time. Compared to existing asymptotically stable or finite-time stable strategies, this approach does not depend on the magnitude of the initial error, ensuring the controllability and consistency of the encirclement response speed, and significantly improving the time determinism and control reliability of multi-vessel cooperative tracking in dynamic tasks.

[0033] The introduced affine transformation formation control structure, by controlling the positional changes of a few leader vessels, drives the scaling, rotation, and deformation of the entire formation, adapting to different water terrains. Compared with traditional fixed formation methods, this approach not only preserves the stability of the encirclement but also enhances the adaptability and maneuverability of the multi-unmanned surface vessel system in complex environments such as narrow waters and obstacle zones, thereby improving the overall system flexibility.

[0034] The adopted memory-based dynamic event-triggered communication mechanism only triggers communication actions when error conditions are met, and combines historical state data to correct observations. This allows the system to maintain high observation accuracy while significantly reducing redundant communication. Compared to periodic synchronous communication, this mechanism significantly reduces communication resource consumption and node energy consumption, enhancing the long-term stability and reliability of the system.

[0035] It is suitable for multi-target coordinated capture and maneuver control missions in communication-restricted and complex water environments. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of a distributed cooperative maneuvering multi-target encirclement and control method for multiple unmanned surface vessels;

[0037] Figure 2 A trajectory diagram for the maneuvering and multi-target encirclement of a multi-unmanned surface vessel system;

[0038] Figure 3 A graph showing the variation of the consistency error at the target center position;

[0039] Figure 4 A graph showing the variation of the error in estimating the target center position;

[0040] Figure 5 The graph shows the change in the velocity estimation error at the target center. Detailed Implementation

[0041] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically:

[0042] Implementation Method 1: This implementation method provides a distributed cooperative maneuvering multi-target encirclement and control method for multiple unmanned surface vessels, including:

[0043] The steps for establishing a two-dimensional motion mathematical model of multiple unmanned surface vessels (USVs) and defining the position, velocity, and heading state of each USV are used to construct the state expression of the USV system.

[0044] Leaders and followers are divided according to the preset communication topology, and the affine transformation relationship of the configuration is generated based on the stress matrix, which is used for subsequent steps to control the scaling, rotation and translation of the formation.

[0045] The steps are: receiving target status information, constructing a distributed target center estimator based on local communication neighbor relationships, and outputting the estimated position and velocity values ​​of the target center;

[0046] Based on the leader's state information and the estimated position and velocity of the target center, a distributed state observer is constructed, and combined with historical observation data and event triggering mechanisms, the steps for generating the expected state of each follower are generated.

[0047] Using the desired state as input, a preset time sliding mode controller is constructed. The control input is dynamically generated by combining the current state and the error state, so as to realize the real-time tracking of the desired state by each unmanned surface vessel.

[0048] The output control signal is used to drive the unmanned surface vessel swarm to perform multi-target encirclement missions and continuously adjust the affine formation.

[0049] The kinematic mathematical model includes the longitudinal velocity, lateral velocity, and bow roll rate of the unmanned surface vessel, and achieves consistent modeling of the kinematic and dynamic systems by constructing additional dummy variables.

[0050] The affine transformation relationship is generated driven by the leader position, and the rotation, scaling and translation transformation parameters of the overall formation are determined by controlling the positions of at least three leaders.

[0051] The distributed target centroid estimator constructs an estimation model based on multiple target location information received by the leader and uses bivariate intermediate states to achieve finite-time convergence of target centroid estimation.

[0052] The distributed state observer employs an event-triggered mechanism with memory and is configured with a state buffer of finite length.

[0053] The sliding mode controller dynamically adjusts by constructing a sliding surface and combining it with the target state error, and a preset time convergence term is introduced into the control law.

[0054] Implementation Method Two: This implementation method provides a further detailed description of the technical solution provided in Implementation Method One. Specifically:

[0055] A distributed, cooperative, multi-target encirclement and control method for multiple unmanned surface vessels (USVs) is proposed. Its core lies in dividing the control architecture into a communication layer, a guidance layer, and a control layer. Through distributed estimation and control mechanisms, it achieves real-time encirclement and capture of multiple targets and possesses good adaptability to complex aquatic environments. This includes:

[0056] The first step is to establish a mathematical model of the unmanned surface vessel's motion and define the system state variables.

[0057] The purpose of this step is to provide a dynamic foundation for subsequent control design. Using a two-dimensional plane as the working scenario, the position coordinates, heading angle, longitudinal velocity, lateral velocity, and yaw rate of each unmanned surface vessel (USV) are defined. Furthermore, configuration vectors are introduced to describe the layout state of the entire cluster, and additional variables are defined to unify the kinematic and dynamic systems, providing a computable state representation for the subsequent implementation of affine control strategies.

[0058] The second step is to construct the communication topology between unmanned surface vessels and introduce an affine transformation control mechanism.

[0059] This step outputs the information interaction methods and structural organization of the unmanned surface vessel (USV) swarm. A directed graph is used to represent the communication relationships between the USVs, and they are divided into leader and follower classes. Based on this, an affine tension and stress matrix representation of the swarm configuration is proposed. Using adjacency matrices and Laplace matrices from graph theory, dynamic position mapping between multiple USVs is achieved. The leader's transformation control guides the entire system's formation through rotation, scaling, and translation, providing structural flexibility to adapt to different operating waters.

[0060] The third step is to design a distributed target center estimator at the communication layer.

[0061] This step uses the communication topology as input to achieve local estimation of the centroid positions and velocities of multiple targets by the leader. Each leader UAV only needs to perceive the state information of a portion of the targets. By introducing intermediate variable states, a nonlinear estimation of the target centers is performed, and the estimated values ​​are adaptively corrected using the state errors between adjacent nodes. This estimator can converge in a finite time without centralized information fusion, effectively improving the system's practicality in communication-constrained scenarios.

[0062] The fourth step is to build a distributed state observer based on memory event triggering at the guidance layer.

[0063] After obtaining the target center location, this step is used to construct the desired trajectory for the followers. By using the leader's movement trajectory, the followers are guided to observe their own state through event-triggered observations, avoiding unnecessary communication overhead. Each follower UAV utilizes historical state data and neighbor information to determine whether to update its desired state based on event triggering conditions, thus achieving state observation with low communication costs. A buffer is used to store historical observation data, and by incorporating importance weights, the accuracy and stability of the observations are improved.

[0064] The fifth step is to design a preset time sliding mode controller at the control layer to achieve tracking control of the desired state.

[0065] After obtaining the desired target state, a dynamic controller is designed using sliding mode control to ensure that each follower vessel can track the desired state within a preset time. The controller incorporates a sliding surface function, calculates the control input based on the state error and observation error, and combines this with a hypersurface approximation strategy to achieve fast and stable dynamic convergence. Compared to traditional asymptotically stable control, this method has the advantages of being insensitive to initial errors and having a controllable response speed.

[0066] The sixth step is to verify the effectiveness and robustness of the proposed method in typical multi-target encirclement tasks through simulation.

[0067] The proposed distributed control method was applied to a typical aquatic environment and a multi-target dynamic encirclement task. Different initial target positions and velocities were set to verify that the system can quickly construct an encirclement formation and continuously adjust its configuration based on the target's movement. Simulation results show that the system can complete target capture within a finite time, with rapid convergence of centroid error and velocity estimation error, and stable control input, verifying the engineering feasibility and stability of the proposed method.

[0068] Implementation Method 3: Combination Figure 1-5 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically:

[0069] (1) Establish a mathematical model of unmanned surface vessel motion. Definition Indicates the position coordinates of the unmanned surface vessel. Indicates the heading angle of the unmanned surface vessel. This indicates the longitudinal and lateral speeds of the unmanned surface vessel. This represents the bow roll rate of the unmanned surface vessel. Define a new variable. ,in, , These are design parameters, representing the distance from the center of the unmanned surface vessel to the reference control point. The vector represents the nonlinear function of the unmanned surface vessel's dynamics, determined by factors such as velocity, heading angle, and control point location. To control the input matrix, thrust and torque are mapped to the unmanned surface vessel's acceleration response. The kinematics and dynamics of the unmanned surface vessel can then be expressed as:

[0070]

[0071]

[0072] In the formula, and The nonlinear coupling term is represented in the following form:

[0073]

[0074] This indicates a control input. , , ; , and This represents the hydrodynamic coefficient. This indicates the mass of the unmanned surface vessel. This represents the inertia matrix.

[0075] (2) Define the topological relationship and affine transformation relationship of information interaction between multiple unmanned surface vessels.

[0076] The information exchange topology among unmanned surface vessel (USV) swarms uses a directed graph. It means that, among them, Represents a set of nodes. Represents an edge set. For an edge set... There exists a directed edge ,node Nodes can be received The information sent may not necessarily be true, and the node... Defined as a node Out-neighbor, node Defined as a node The neighboring nodes. Additionally, directed graphs... There are no loops in it, that is .definition For nodes The set of inbound neighbors. From node To the node A directed path is defined as a set of ordered edges, i.e. .definition For directed graphs A non-negative adjacency matrix, where if ,So ,otherwise .definition For directed graphs The Laplace matrix can be expressed as:

[0077]

[0078] For edge set Define the index vector as Directed graph Correlation matrix use - A matrix description, where rows are indexed by edges and columns by nodes, specifically expressed as follows:

[0079]

[0080] Directed graph Correlation matrix It possesses the following properties: , .

[0081] definition One unmanned surface vessel took the lead, and the rest... An unmanned surface vessel is defined as a follower. Under this premise, and Used to represent the sets of leaders and followers respectively; configuration It can be broken down into leader configuration and follower configuration, namely: and Let each represent the set of positions for leaders and followers, respectively. Assume each leader... Neither can access information from other unmanned surface vessels, and it can base its trajectory on a desired path. Navigation, defined as the set of expected trajectories for the leader: .

[0082]

[0083] Multiple unmanned surface vessels Affine Zhang Cheng Defined as:

[0084] Given any affine span, it can always be translated to a position containing the origin, thus obtaining a linear space whose dimension is defined as the dimension of the affine span. The affine span of a multi-UAV swarm has a dimension of 2, so we say that the affine span of these points is 2. If there exists a set of scalars that are not all zero. Make the point set satisfy: and So, point set It is affine dependent; otherwise, it is a point set. It is affine independent. If Affine Zhang Cheng There must exist 3 affine independent points.

[0085] for Stress is a set of scalars ,in With edge Related, when node With nodes When there is attraction between them, When node With nodes When there is a repulsive force between them, In other cases, If the stress satisfies the following equation, then it is called equilibrium stress:

[0086]

[0087] The above formula means that the node set Apply to node The forces on it are in equilibrium. It is worth noting that equilibrium stresses can only be determined down to a scalar factor. This means that if... If it is balanced stress, then for any , It is also a balanced stress.

[0088] The above formula can be further expressed in matrix form, as follows:

[0089]

[0090] In the formula, For the framework The stress matrix satisfies:

[0091]

[0092] The stress matrix has a similar structure to the graph Laplacian matrix. The difference is that the edge weights in the stress matrix can be positive, negative, or zero, while the edge weights in the graph Laplacian operator are usually positive.

[0093] Consider in space A configuration The affine image of this configuration is defined as:

[0094]

[0095] In the formula, It represents affine transformation. It is a matrix that can be used to implement a configuration relative to the nominal configuration. Rotation, scaling, and shearing; It is a vector used to perform translation.

[0096] Therefore, the target framework for a time-varying affine formation is as follows:

[0097]

[0098] In the formula, and It's about time. Continuous, and may be constant or time-varying. Node The desired position within the target affine formation is: .

[0099] It is worth noting that if each unmanned surface vessel needs to know... , and It is impractical to expect them all to track their respective desired trajectories; this strategy requires specifying all time periods. of , and This information is stored on each unmanned surface vessel (USV), so that the multi-USV system cannot dynamically respond to unexpected situations such as popping out obstacles.

[0100] To achieve target formation in a distributed manner, a leader-follower strategy is employed, where the required formation maneuvers are known only to a few agents called leaders, while the other unmanned surface vessels (USVs) called followers simply need to follow the leaders' actions. As previously shown, the leader's position will be determined by an affine transformation. One-to-one correspondence. Therefore, the affine transformation of a multi-unmanned surface vessel (USV) system configuration is achieved by controlling the position of the leaders. Since the number of leaders is typically small, it is generally assumed that they can be properly controlled. In real-world mission scenarios, leaders may be controlled by human operators or intelligent decision-making programs. It is assumed that the position of each leader is equal to the desired value in the target formation, i.e. Then, the control target becomes guiding the followers, making... when .

[0101] In order to manipulate the entire system through leaders, we must select sufficient and suitable leaders. Within the standard framework, any affine tension... Unmanned surface vessels can all be selected as leaders to ensure the affines can be located. Because An affine span requires at least 3 points, therefore the minimum number of leaders is 3. Given the positions of any leader... The position of the leader and affine transformation There is a one-to-one correspondence between them, meaning there is always a solution. Get the position of the follower .

[0102] Set time The target's location is For multi-objective systems, use The set of nodes representing the target. Define a directed graph. To describe the relationship between leaders and goals. Accordingly, the diagram... Laplace matrix Defined as ,in This represents the connection matrix between leaders and goals. If the first... The leader can from the first The target receiving information is sufficient. Otherwise, satisfy .

[0103] (3) Design a distributed target center estimator. Define the target center as... A distributed target center estimator is designed for each unmanned surface vessel to estimate the target center, denoted as:

[0104]

[0105] In the formula, , Represents intermediate state variables. and These represent the leader's estimates of the location and velocity of the target center, respectively.

[0106] Based on the aforementioned distributed target centroid estimator, each unmanned surface vessel (USV) can estimate the target's centroid, and the estimation error converges to zero within a finite time.

[0107] (4) Design a distributed state observer. For the first... A single unmanned surface vessel and The definition is as follows:

[0108]

[0109] In the formula, Indicates the first The triggering moment of the unmanned surface vessel.

[0110] To improve the system's stability, each unmanned surface vessel was equipped with a device of a size of [missing information]. The buffer. Based on the above statements, the distributed observer based on memory-triggered events is designed as follows:

[0111]

[0112] In the formula, , This indicates the preset time design parameters. Indicates positive design parameters. This represents the positive design parameters, indicating the importance of the data within the buffer, and ensuring that the conditions are met. and .

[0113] Rewrite the above expression in a compact form:

[0114]

[0115] In the formula, , , , , , .

[0116] Then, the measurement error is defined as follows:

[0117]

[0118] Define auxiliary variables and To represent the error dynamics, the specific form is as follows:

[0119]

[0120] Therefore, we can obtain:

[0121]

[0122] Alternatively, the following compact form can be obtained:

[0123]

[0124] Subsequently, based on the traditional event-triggered mechanism, a novel distributed memory-based event-triggered mechanism is introduced, specifically as follows:

[0125]

[0126]

[0127] and In the formula, , , and Indicates positive design parameters.

[0128] Based on the above analysis, the triggering time sequence of the unmanned surface vessel can be determined as follows:

[0129]

[0130] (5) Design a preset time dynamics controller.

[0131] A dynamic controller is designed for each unmanned surface vessel to track the desired state. The tracking error is defined as follows:

[0132]

[0133] Then, a sliding surface is defined as:

[0134]

[0135] Based on the sliding mode design framework, the following preset time-dynamic controllers can be designed:

[0136]

[0137] In the formula, , and Indicates positive design parameters. ,in Indicates a predefined time. .

[0138] Consider the first The control signals for the unmanned surface vessel are generated by the aforementioned controller and satisfy the following relation. , and Then the local tracking error of the control system and It has a globally preset time stability.

[0139] The technical effect is as follows:

[0140] (1) The multi-unmanned surface vessel cooperative encirclement and control method is decoupled into a communication layer, a guidance layer and a control layer, which reduces the complexity of system design and realizes efficient encirclement and capture of multiple dynamic targets under complex environmental conditions.

[0141] (2) A distributed memory event-triggered observer based on the motion layer was designed. It can realize real-time observation of its own desired state under communication-limited conditions with only leadership information, eliminating the dependence on global information and enhancing practicality. At the same time, affine transformation endows the multi-unmanned surface vessel system with the maneuverability to respond to dynamic changes in the environment and ensures that the affine transformation design is independent of the complex dynamic system, thus enhancing the feasibility of the control scheme.

[0142] (3) A positive minimum interval dynamic event-triggered communication mechanism was designed, which enables unmanned surface vessels to transmit status information only when the triggering conditions are met, effectively avoiding continuous communication and significantly reducing communication complexity and the possibility of system failure. At the same time, the use of a buffer to store historical data improves the reliability of control signals under the event-triggered mechanism, provides more reasonable signals, and enhances the robustness and stability of the system.

[0143] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for distributed cooperative maneuvering and multi-target encirclement and control of multiple unmanned surface vessels, characterized in that, include: The steps for establishing a two-dimensional motion mathematical model of multiple unmanned surface vessels (USVs) and defining the position, velocity, and heading state of each USV are used to construct the state expression of the USV system. Leaders and followers are divided according to the preset communication topology, and the affine transformation relationship of the configuration is generated based on the stress matrix, which is used for subsequent steps to control the scaling, rotation and translation of the formation. The steps are: receiving target status information, constructing a distributed target center estimator based on local communication neighbor relationships, and outputting the estimated position and velocity values ​​of the target center; Based on the leader's state information and the estimated position and velocity of the target center, a distributed state observer is constructed, and combined with historical observation data and event triggering mechanisms, the steps for generating the expected state of each follower are generated. Using the desired state as input, a preset time sliding mode controller is constructed. The control input is dynamically generated by combining the current state and the error state, so as to realize the real-time tracking of the desired state by each unmanned surface vessel. The output control signal is used to drive the unmanned surface vessel swarm to perform multi-target encirclement missions and continuously adjust the affine formation.

2. The multi-unmanned surface vessel distributed cooperative maneuvering multi-target encirclement and control method according to claim 1, characterized in that, The kinematic mathematical model includes the longitudinal velocity, lateral velocity, and bow roll rate of the unmanned surface vessel, and achieves consistent modeling of the kinematic and dynamic systems by constructing additional dummy variables.

3. The multi-unmanned surface vessel distributed cooperative maneuvering multi-target encirclement and control method according to claim 1, characterized in that, The affine transformation relationship is generated driven by the leader position, and the rotation, scaling and translation transformation parameters of the overall formation are determined by controlling the positions of at least three leaders.

4. The method for distributed cooperative maneuvering and multi-target encirclement and control of multiple unmanned surface vessels according to claim 1, characterized in that, The distributed target centroid estimator constructs an estimation model based on multiple target location information received by the leader and uses bivariate intermediate states to achieve finite-time convergence of target centroid estimation.

5. The multi-unmanned surface vessel distributed cooperative maneuvering multi-target encirclement and control method according to claim 1, characterized in that, The distributed state observer employs an event-triggered mechanism with memory and is configured with a state buffer of finite length.

6. The multi-unmanned surface vessel distributed cooperative maneuvering multi-target encirclement and control method according to claim 1, characterized in that, The sliding mode controller dynamically adjusts by constructing a sliding surface and combining it with the target state error, and a preset time convergence term is introduced into the control law.

7. A multi-unmanned surface vessel distributed cooperative maneuvering multi-target encirclement and control device, characterized in that, include: A two-dimensional motion mathematical model of multiple unmanned surface vessels (USVs) is established, defining the position, velocity, and heading state of each USV, which is used to construct a module for the state expression of the USV system. Leaders and followers are divided according to the preset communication topology, and the affine transformation relationship of the configuration is generated based on the stress matrix, which is used for subsequent modules to control the scaling, rotation and translation of the formation. This module receives target status information, constructs a distributed target center estimator based on local communication neighbor relationships, and outputs the estimated position and velocity values ​​of the target center. Based on the leader's state information and the estimated position and velocity of the target center, a distributed state observer is constructed, and a module for generating the expected state of each follower is generated by combining historical observation data and event triggering mechanisms. The module uses the desired state as input to construct a preset time sliding mode controller, and dynamically generates control input by combining the current state and error state, so as to realize the real-time tracking of the desired state by each unmanned surface vessel. The module outputs control signals to drive the unmanned surface vessel swarm to perform multi-target encirclement missions and continuously adjust the affine formation.

8. A computer storage medium for storing computer programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 1.

9. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.

10. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 1.

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