Marine vehicle control method, apparatus, device, and medium

Abstract

The application provides a marine vehicle control method, device, equipment and medium, the method comprises: obtaining the state information of a plurality of marine vehicles; the plurality of marine vehicles comprise a leader vehicle and a follower vehicle; based on the state information of each marine vehicle, a corresponding dynamic model of the marine vehicle is constructed, and a corresponding leader disturbance observation model of the leader vehicle and a corresponding follower disturbance observation model of the follower vehicle are constructed; based on the corresponding dynamic model and the leader disturbance observation model of the leader vehicle, the driving strategy of the leader vehicle is determined, and based on the corresponding dynamic model and the follower disturbance observation model of the follower vehicle, the driving strategy of the follower vehicle is determined. In this way, in a complex environment where the communication network topology randomly changes and external unknown disturbances exist at the same time, the hierarchical cooperative trajectory tracking control of the distributed marine vehicle system is effectively realized.

Description

Technical Field

[0001] This application relates to the field of ship control technology, and more specifically, to a method, apparatus, equipment, and medium for controlling marine vehicles. Background Technology

[0002] With the advancement of marine resource development and marine informatization, distributed autonomous marine vehicles (AMs) have been widely used in tasks such as marine environmental monitoring, maritime patrol and search and rescue, and marine surveys due to their advantages such as mobility, low deployment cost, and strong mission adaptability.

[0003] However, the actual marine environment contains complex dynamic factors, such as unknown external disturbances, unstructured marine environments, and dynamic changes in communication networks. These factors pose a severe challenge to the collaborative control performance of distributed autonomous marine vehicle systems. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, device and medium for controlling marine vehicles, which effectively realizes cooperative trajectory tracking control of distributed marine vehicle systems under the dual influence of dynamic random topology of communication networks and unknown interference.

[0005] Specifically, this application is implemented through the following technical solution: According to a first aspect of this application, a method for controlling a marine vehicle is provided, the method comprising: The status information of multiple ocean vehicles is acquired; the multiple ocean vehicles include a leader vehicle and a follower vehicle, wherein the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the trajectory of the leader vehicle as a reference trajectory. Based on the state information of each of the aforementioned marine vehicles, a dynamic model corresponding to each marine vehicle, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle are constructed. The dynamic model is used to characterize the correlation between the actual driving control input of the marine vehicle, the actual sea state interference experienced by the marine vehicle during driving, and the dynamic state of the marine vehicle. The leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving. Based on the dynamics model and the leader interference observation model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined, and based on the dynamics model and the follower interference observation model corresponding to the follower vehicle, the driving strategy of the follower vehicle is determined.

[0006] In one optional implementation, a dynamic model corresponding to each of the marine vehicles is constructed based on the state information of each marine vehicle, including: Based on the state information of the marine vehicle, determine the transformation relationship of the marine vehicle's velocity between the body coordinate system and the Earth coordinate system; Based on the actual driving control input of the marine vehicle and the actual sea condition disturbances encountered by the marine vehicle during its operation, the speed change information of the marine vehicle is determined. Based on the transformation relationship and the velocity change information, the dynamic model is constructed.

[0007] In one optional implementation, determining the speed change information of the marine vehicle based on its actual navigation control input and the actual sea state disturbances experienced by the marine vehicle during navigation includes: The first fusion result is determined between the actual driving control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during driving, and the second fusion result is determined between the inertial effect, centrifugal effect and hydrodynamic damping of the marine vehicle. The velocity change information is determined based on the deviation between the first fusion result and the second fusion result.

[0008] In one optional implementation, constructing a leader interference observation model corresponding to the leader vehicle includes: Based on the state information of the leader vehicle, the correlation between the estimated sea state disturbance and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state disturbance approximate the actual sea state disturbance. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the leader disturbance observation model is constructed.

[0009] In one optional implementation, constructing a leader interference observation model corresponding to the leader vehicle includes: Based on the state information of the leader vehicle, the correlation between the estimated sea state disturbance and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state disturbance approximate the actual sea state disturbance. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the leader disturbance observation model is constructed.

[0010] In one optional implementation, determining the driving strategy of the leader vehicle based on the dynamics model corresponding to the leader vehicle and the leader interference observation model includes: Based on the leader vehicle's trajectory and the desired trajectory, a leader tracking error model is constructed for the leader vehicle; the leader tracking error model is used to determine the tracking error of the leader vehicle while it is following the desired trajectory. Based on the dynamics model, the interference observation model, and the tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined.

[0011] In one optional implementation, constructing a leader tracking error model corresponding to the leader vehicle based on the leader vehicle's trajectory and the desired trajectory includes: Based on the leader vehicle's trajectory and the desired trajectory, determine the leader tracking error between the leader vehicle's current state and the current state corresponding to the desired trajectory; Determine the leader control input error between the virtual driving control input and the stabilization function set for the leader vehicle; the stabilization function corresponding to the leader vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the leader vehicle. The leader tracking error model is constructed based on the leader tracking error and the leader control input error.

[0012] In an optional implementation, before determining the driving strategy of the leader vehicle based on the dynamics model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the method further includes: Based on the leader tracking error and leader control input error obtained through the leader tracking error model, and the sea state disturbance estimate obtained through the leader disturbance observation model, a leader tracking error function is determined; the leader tracking error function is used to verify the stability of the leader tracking error model. The leader tracking error model is verified by the leader tracking error function, and it is determined that the leader tracking error is less than the second threshold.

[0013] According to a second aspect of this application, a marine vehicle control device is provided, the device comprising: An information acquisition module is used to acquire the status information of multiple ocean vehicles; the multiple ocean vehicles include a leader vehicle and a follower vehicle, wherein the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the driving trajectory of the leader vehicle as a reference trajectory. The model building module is used to construct a dynamic model corresponding to each of the ocean vehicles, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle, based on the state information of each ocean vehicle. The dynamic model is used to characterize the correlation between the actual driving control input of the ocean vehicle, the actual sea state interference experienced by the ocean vehicle during driving, and the dynamic state of the ocean vehicle. The leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving. The strategy determination module is used to determine the driving strategy of the leader vehicle based on the dynamic model corresponding to the leader vehicle and the leader interference observation model, and to determine the driving strategy of the follower vehicle based on the dynamic model corresponding to the follower vehicle and the follower interference observation model.

[0014] According to a third aspect of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the marine vehicle control method described in the first aspect above.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the marine vehicle control method described in the first aspect.

[0016] The marine vehicle control method, apparatus, equipment, and medium provided in this application hierarchically divide a distributed marine vehicle system, comprising multiple marine vehicles, into leader vehicles and follower vehicles, and perform hierarchical interference observation. Based on the leader interference observation model and the follower interference observation model, corresponding driving strategies are determined for the leader vehicle and the follower vehicle, respectively. This effectively solves the problem of cross-layer information transmission of marine vehicles, thereby enabling hierarchical collaborative trajectory tracking control of the distributed marine vehicle system in complex environments where communication network topology changes randomly and external unknown interference exists simultaneously. This helps to improve the stability and anti-interference capability of the distributed marine vehicle system.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this disclosure.

[0018] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an exemplary embodiment of a marine vehicle control method according to this application; Figure 2 This is a schematic diagram of a reference coordinate system for a marine vehicle, as illustrated in an exemplary embodiment of this application. Figure 3 This is a schematic diagram of a communication topology for a marine vehicle system, as illustrated in an exemplary embodiment of this application. Figure 4 This is a schematic diagram illustrating the control process of a marine vehicle according to an exemplary embodiment of this application; Figure 5 This is a schematic diagram of a marine vehicle control device shown in an exemplary embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device shown in an exemplary embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0023] Research has revealed that the communication network topology of multiple marine vehicles is often uncertain, causing communication between vehicles to disconnect or reconnect at any time. This results in a random switching characteristic of the system topology, making it difficult to adapt to traditional control methods based on fixed topologies. Furthermore, since unmanned marine vehicles typically operate for extended periods in the marine environment, they are often subjected to unknown disturbances from multiple sources, significantly impacting their dynamic behavior and further increasing the complexity of cooperative controller design. How to achieve cooperative trajectory tracking control of a distributed marine vehicle system under the dual influence of dynamic random topology and unknown disturbances is a pressing technical problem that needs to be solved.

[0024] For the cooperative control of distributed marine vehicles, several approaches are commonly used: distributed control based on fixed network topology, robust tracking based on model predictive control, and anti-interference based on disturbance observers. However, control based on fixed network topology assumes a constant network structure, which does not match the randomly changing network characteristics in reality, leading to a significant decrease in control performance. Model predictive control is highly dependent on the accuracy of the system model, making it difficult to effectively cope with complex and ever-changing marine environments. Traditional disturbance observer designs struggle to address multi-source disturbances while also meeting the real-time requirements of dynamic topology changes, limiting their effectiveness in practical applications.

[0025] Based on the above research, this application provides a method, apparatus, device, and medium for controlling marine vehicles. It hierarchically divides a distributed marine vehicle system, which includes multiple marine vehicles, into leader vehicles and follower vehicles, and performs hierarchical interference observation and navigation strategy formulation. This enables effective hierarchical collaborative trajectory tracking control of the distributed marine vehicle system in complex environments where communication network topology changes randomly and external unknown interferences coexist. This helps to improve the stability and anti-interference capability of the distributed marine vehicle system.

[0026] To facilitate understanding of this embodiment, a detailed description of the marine vehicle control method disclosed in this application is provided first. The execution entity of the marine vehicle control method provided in this application is generally an electronic device with a certain computing power. This electronic device can be a server, which can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. In some possible implementations, this marine vehicle control method can be implemented by a processor calling computer-readable instructions stored in memory.

[0027] The following description, in conjunction with the accompanying drawings, illustrates a marine vehicle control method provided in an embodiment of this application.

[0028] See Figure 1 The diagram shown is a flowchart illustrating a marine vehicle control method according to an exemplary embodiment of this application. Figure 1 As shown in the embodiments of this disclosure, the marine vehicle control method includes steps S101 to S103, wherein: S101: Obtain the status information of multiple ocean vehicles; the multiple ocean vehicles include a leader vehicle and a follower vehicle, wherein the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the driving trajectory of the leader vehicle as a reference trajectory.

[0029] The marine vehicle can be a ship or the like; in this embodiment, an unmanned vessel is used as an example.

[0030] For a better understanding of this embodiment, please refer to Figure 2 This is a schematic diagram of a reference coordinate system for a marine vehicle, illustrating an exemplary embodiment of this application. Figure 2 As shown, This represents a fixed Earth coordinate system, with point O representing the starting point of the desired trajectory. Let i represent the body coordinate system of the marine vehicle, and let i represent the i-th marine vehicle. [1, N], where N represents the number of the plurality of marine vehicles. The point represents the geometric center of the ocean-going vehicle. The axis points to true north. The axis points due east geographically. This indicates the total speed of a marine vehicle. Indicates the longitudinal speed of a marine vehicle. Indicates the lateral speed of a marine vehicle. Indicates the yaw speed of a marine vehicle. Indicates the yaw angle of a marine vehicle. Indicates the center of gravity of a marine vehicle. This represents the distance between the origin of the marine vehicle's body coordinate system and the center of gravity of the marine vehicle.

[0031] Optionally, the status information of each of the marine vehicles includes, but is not limited to, the position, speed, acceleration, yaw angle, etc. of the marine vehicle.

[0032] In this embodiment of the disclosure, the plurality of marine vehicles constitute a marine vehicle system with a random communication network topology. The marine vehicle system consists of a plurality of leader vehicles and a plurality of follower layer vehicles. The topology is random under conditions of limited communication links or differences in sensor perception capabilities.

[0033] Please also see Figure 3 This is a schematic diagram illustrating a communication topology of a marine vehicle system, as shown in an exemplary embodiment of this application. Figure 3 As shown, Representing a fixed Earth coordinate system, the ocean vehicle system comprises seven ocean vehicles. Vehicles numbered 1 to 3 are the leader vehicles, forming the leader layer, while vehicles numbered 4 to 7 are the follower vehicles, forming the follower layer. Taking leader vehicle number 1 as an example... The body coordinate system is for the No. 1 leader spacecraft.

[0034] To reduce communication power consumption, only one leader vehicle can receive the desired trajectory and forward it to the other leader vehicles. In this example, only leader vehicle number 2 can directly receive the desired trajectory sent by the trajectory generator. Leader vehicle number 2 then sends the received desired trajectory to leader vehicles number 1 and 3. Each leader vehicle uses the received desired trajectory as a reference trajectory for its navigation, while each follower vehicle uses the leader vehicle's trajectory as a reference trajectory for its own pose adjustment.

[0035] Figure 3 The diagram also illustrates the communication connection status between the leader and follower layers. This marine vehicle system exhibits an uncertain communication connection structure due to differences in sensor capabilities. In the communication topology, all communication links have a weight of 1, representing a communication probability of 1, meaning that information sent by the sender will definitely be received by the receiver when a connection is established. Thus, considering topology changes caused by differences in sensor performance or limited communication links, a network structure is designed that satisfies connectivity maintenance conditions. This ensures that each marine vehicle in the system can establish a communication path with other marine vehicles at any given time using a finite number of hops, avoiding the creation of isolated nodes.

[0036] Here, the leader vehicle is positioned as a relatively stable anchor node for information transmission, capable of reliably transmitting its own information to follower vehicles or other leader vehicles. Its communication links are relatively fixed, represented by solid blue lines. Follower vehicles, on the other hand, participate in completely random communication links, with connection states changing over time. Their communication links are represented by dashed red lines, indicating that the connection may be interrupted or restored over time. Solid blue lines indicate a valid communication link, while dashed red lines indicate a temporary communication interruption. This communication topology is designed to prevent follower vehicles from completely losing contact due to random topology switching, while preserving the randomness of communication characteristics in the marine vehicle system, thereby enhancing the system's adaptability and robustness in dynamic communication environments.

[0037] This helps to solve the problem of randomness in information transmission between marine vehicles and fully considers the dynamic topology changes that marine vehicles may experience during movement. This is different from the distributed systems in related technologies that only focus on random switching between fixed topologies. The completely random topology that this disclosure focuses on is closer to the complex situations such as sensor failure and channel reconnection in the real environment.

[0038] S102: Based on the state information of each of the marine vehicles, construct a dynamic model corresponding to the marine vehicle, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle; the dynamic model is used to characterize the correlation between the actual driving control input of the marine vehicle, the actual sea state interference experienced by the marine vehicle during driving, and the dynamic state of the marine vehicle; the leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving.

[0039] In this step, considering unknown external interference, a dynamic model corresponding to the ocean vehicle is established, and the interference is layered to construct a leader interference observation model corresponding to the leader vehicle and a follower interference observation model corresponding to the follower vehicle.

[0040] In some possible implementations, a dynamic model corresponding to each of the marine vehicles is constructed based on the state information of each vehicle, including: Based on the state information of the marine vehicle, determine the transformation relationship of the marine vehicle's velocity between the body coordinate system and the Earth coordinate system; Based on the actual driving control input of the marine vehicle and the actual sea condition disturbances encountered by the marine vehicle during its operation, the speed change information of the marine vehicle is determined. Based on the transformation relationship and the velocity change information, the dynamic model is constructed.

[0041] Here, the speed of the marine vehicle includes three degrees of freedom: longitudinal speed, lateral speed, and yaw speed. The sea state disturbances include external unknown disturbances caused by sea winds, waves, and ocean currents.

[0042] In this way, by considering the impact of external disturbances on ocean-going vehicles, a dynamic model is established, providing a solid foundation for the formulation of subsequent navigation strategies.

[0043] In some possible implementations, determining the speed change information of the marine vehicle based on its actual navigation control input and the actual sea state disturbances it experiences during navigation includes: The first fusion result is determined between the actual driving control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during driving, and the second fusion result is determined between the inertial effect, centrifugal effect and hydrodynamic damping of the marine vehicle. The velocity change information is determined based on the deviation between the first fusion result and the second fusion result.

[0044] In the above steps, the speed change information is determined by the deviation between the first fusion result of the actual driving control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during driving, and the second fusion result of the inertial effect, centrifugal effect and hydrodynamic damping of the marine vehicle. This allows the marine vehicle to be controlled to accelerate or decelerate accordingly, fully considering the dynamic state of the marine vehicle during its movement, which helps to improve the accuracy of marine vehicle control.

[0045] Specifically, the dynamic model corresponding to each ocean-going vehicle can be represented by the following formula (1): (1) in, This represents the state of the i-th marine vehicle; Including the x-coordinate of the i-th ocean vehicle y-axis Yaw angle ; This represents the rotation matrix between the Earth's fixed coordinate system and the volume coordinate system of the i-th ocean vehicle. ; This represents the total speed of the i-th ocean vehicle. Including the longitudinal velocity of the i-th ocean vehicle The lateral velocity of the i-th ocean vehicle yaw speed of the i-th ocean vehicle ; Let represent the non-singular symmetric positive definite inertial matrix of the i-th marine vehicle. ; Let the Coriolis force matrix and centripetal force matrix of the i-th marine vehicle be represented. ; This represents the damping matrix of the i-th marine vehicle. ; This represents the actual driving control input for the i-th marine vehicle. , Let represent the control inputs in the three degrees of freedom of the total velocity of the i-th marine vehicle, for example, Represents the longitudinal velocity of the i-th ocean vehicle. On the control input, Represents the lateral velocity of the i-th ocean vehicle On the control input, Represents the yaw speed of the i-th ocean vehicle Control inputs on; This represents the actual sea state disturbance of the i-th ocean vehicle. , These represent the real, unknown external disturbances caused by sea winds, waves, and currents to the i-th marine vehicle, respectively, and N represents the number of the multiple marine vehicles.

[0046] in, , , , , , , , , , ; This represents the mass of the i-th marine vehicle; Let represent the moment of inertia of the yaw rotation of the i-th marine vehicle; This represents the longitudinal resistance of the i-th marine vehicle. This includes linear terms, as well as nonlinear drag that is proportional to the square of the velocity; This represents the lateral resistance of the i-th marine vehicle. Both lateral velocity It is related to, and also to, yaw rate. coupling; This represents the contribution of the lateral motion of the i-th marine vehicle to the yaw moment (sway-yaw coupling); This represents the contribution of the yaw motion of the i-th marine vehicle to the lateral force (yaw-sway coupling); The yaw damping of the i-th marine vehicle is represented, including linear and nonlinear rotational drag. , , , , Let and represent the linear damping term of the i-th marine vehicle, corresponding to the linear viscous drag at low speeds; , , , , , , , , This represents the nonlinear damping coefficient of the i-th marine vehicle, used to describe the contribution of the square of velocity or the product of velocity to drag / torque.

[0047] Among the above parameters, quality parameters (including) , , ) is used to describe the inertial characteristics of the i-th marine vehicle itself, with an additional mass coefficient (including , , The damping coefficient (linear and nonlinear) is used to describe the inertial effect produced by the interaction between the i-th marine vehicle and the surrounding fluid during acceleration. , The term is used to describe the fluid resistance experienced by the i-th marine vehicle when it moves in water, including velocity-dependent and velocity-square-dependent resistance. Coupling terms (including sway-yaw, surge-yaw, etc.) are used to reflect the coupling effects of the i-th marine vehicle under multi-degree-of-freedom motion.

[0048] The parameters in the above formula (1) reflect the hydrodynamic resistance and coupling effects when the marine vehicle moves in the water, including: viscous drag (linear part), eddy drag / turbulence effect (nonlinear part) and motion coupling effect (e.g., when turning, both lateral force and yaw moment are generated).

[0049] It can be understood that the first expression in the above formula (1) represents the transformation relationship, the second expression in the above formula (1) represents the speed change information, the left side of the second expression represents the first fusion result, and the right side of the second expression represents the second fusion result.

[0050] For ease of representation, a distributed hierarchical system of ocean vehicles containing N ocean vehicles can be defined as follows: ; ; ; ; ; ; ; .

[0051] Wherein, the subscript is Indicates the leader's aircraft, subscript . This refers to a follower aircraft.

[0052] In some possible implementations, a leader interference observation model corresponding to the leader vehicle is constructed, including: Based on the state information of the leader vehicle, the correlation between the estimated sea state disturbance and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state disturbance approximate the actual sea state disturbance. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the leader disturbance observation model is constructed.

[0053] Therefore, considering the unknown interferences that marine vehicles may experience during operation, designing a hierarchical interference observation model that distinguishes between leader interference observation models and follower interference observation models can help improve the accuracy of marine vehicle control.

[0054] The construction method of the follower interference observation model corresponding to the follower vehicle is similar to that of the leader interference observation model. Specifically, based on the state information of the follower vehicle, the correlation between the estimated sea state interference and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state interference approximate the actual sea state interference. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the follower disturbance observation model is constructed.

[0055] Specifically, the interference observation model corresponding to the marine vehicle system can be represented by the following formula (2): (2) in, This indicates an estimate of sea state disturbances. , These represent estimated unknown external disturbances caused by sea winds, waves, and currents, respectively. Indicates auxiliary variables; Represents the gain matrix; This represents the overall inertia matrix, including the inertia matrices of multiple marine vehicles; This indicates the total speed, including the total speed of multiple ocean-going vehicles. This represents the total Coriolis force matrix and centripetal force matrix, including the Coriolis force matrix and centripetal force matrix of multiple marine vehicles; This represents the total damping matrix, including the damping matrices of multiple marine vehicles; This represents the total actual driving control input, including the actual driving control input of multiple marine vehicles.

[0056] Considering the hierarchical structure of the marine vehicle system, the parameters shown in formula (2) above can be set as follows: ; ; .

[0057] Wherein, the subscript is Indicates the leader's aircraft, subscript . This refers to a follower aircraft.

[0058] Optionally, corresponding auxiliary variables and gain matrices can be set according to the communication and dynamic characteristics of the leader and follower layers.

[0059] In some possible implementations, interference errors can be constructed. ,in Therefore, the observation error of an ocean vehicle system can be defined as: .

[0060] S103: Based on the dynamic model corresponding to the leader vehicle and the leader interference observation model, determine the driving strategy of the leader vehicle, and based on the dynamic model corresponding to the follower vehicle and the follower interference observation model, determine the driving strategy of the follower vehicle.

[0061] In this step, the driving strategy of the leader vehicle can be determined by combining the dynamic model corresponding to the leader vehicle and the leader interference observation model, and the driving strategy of the follower vehicle can be determined by combining the dynamic model corresponding to the follower vehicle and the follower interference observation model, so as to realize the cooperative tracking control of the desired trajectory under the hierarchical architecture.

[0062] In some possible implementations, before determining the driving strategy of the leader vehicle based on the dynamics model corresponding to the leader vehicle and the leader interference observation model, the method further includes: Based on the estimated sea state disturbance obtained from the leader disturbance observation model, the leader disturbance error function is determined; the leader disturbance error function is used to verify the stability of the leader disturbance observation model. The leader interference observation model is validated by the leader interference error function, and it is determined that the target interference error between the estimated sea state interference obtained by the leader interference observation model and the corresponding real sea state interference is less than a first threshold.

[0063] In this step, a leader interference error function, such as the Lyapunov function, can be designed to verify the stability of the leader interference observation model.

[0064] Specifically, the leader interference error function can be represented by the following formula (3): (3) in, This represents the leader interference error function. This represents the target interference error between the estimated sea state interference obtained from the leader interference observation model and the corresponding actual sea state interference.

[0065] When the target interference error is less than the first threshold, it indicates that the target interference error tends to stabilize, meaning that the estimated sea state interference obtained by the leader interference observation model tends to be close to the corresponding real sea state interference.

[0066] In this way, the stability of the leader interference observation model is verified by using the leader interference error function, ensuring that the interference estimation has convergence and stability under disturbance changes.

[0067] The determination and verification methods for the follower interference error function corresponding to the follower vehicle are similar to those for the leader interference error function corresponding to the leader vehicle, and will not be repeated here.

[0068] In some possible implementations, determining the driving strategy of the leader vehicle based on the dynamics model corresponding to the leader vehicle and the leader interference observation model includes: Based on the leader vehicle's trajectory and the desired trajectory, a leader tracking error model is constructed for the leader vehicle; the leader tracking error model is used to determine the tracking error of the leader vehicle while it is following the desired trajectory. Based on the dynamics model, the interference observation model, and the tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined.

[0069] In this step, a leader tracking error model corresponding to the leader vehicle can be constructed. Based on the dynamic model, the leader disturbance observation model, and the leader tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle can be determined, which helps to ensure that the leader vehicle can stably track the desired trajectory under disturbance and network change environments.

[0070] In some possible implementations, constructing a leader tracking error model corresponding to the leader vehicle based on the leader vehicle's trajectory and the desired trajectory includes: Based on the leader vehicle's trajectory and the desired trajectory, determine the leader tracking error between the leader vehicle's current state and the current state corresponding to the desired trajectory; Determine the leader control input error between the virtual driving control input and the stabilization function set for the leader vehicle; the stabilization function corresponding to the leader vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the leader vehicle. The leader tracking error model is constructed based on the leader tracking error and the leader control input error.

[0071] Specifically, the leader tracking error model can be represented by the following formula (4): (4) in, This indicates leader tracking error; Indicates the status of the leader's aircraft; This represents the information exchange between the leader aircraft and is part of the Laplace matrix; This represents the information exchange between the generator of the reference trajectory and the leader navigator. Represents the identity matrix; Indicates the reference trajectory; This indicates that the leader controls input errors; This indicates the virtual driving control inputs set for the leader's aircraft; This represents the stabilization function set for the leader's aircraft.

[0072] In some possible implementations, before determining the driving strategy of the leader vehicle based on the dynamics model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the method further includes: Based on the leader tracking error and leader control input error obtained through the leader tracking error model, and the sea state disturbance estimate obtained through the leader disturbance observation model, a leader tracking error function is determined; the leader tracking error function is used to verify the stability of the leader tracking error model. The leader tracking error model is verified by the leader tracking error function, and it is determined that the leader tracking error is less than the second threshold.

[0073] In this step, a leader tracking error function, such as the Lyapunov function, can be designed to verify the stability of the leader tracking error model.

[0074] Specifically, the leader tracking error difference function can be represented by the following formula (5): (5) in, This represents the leader tracking error function; This indicates leader tracking error; This indicates that the leader controls input errors; The non-singular symmetric positive definite inertial matrix representing the leader vehicle; This represents the target interference error between the estimated sea state interference obtained from the leader interference observation model and the corresponding actual sea state interference.

[0075] If the leader tracking error is less than the second threshold, it indicates that the leader tracking error is stabilizing, meaning that the leader vehicle's trajectory is approaching the desired trajectory.

[0076] In this way, the stability of the leader tracking error model is verified by using the leader tracking error function, ensuring that the disturbance estimation has convergence and stability under disturbance changes.

[0077] The method for determining the driving strategy of the follower vehicle is similar to the method for determining the driving strategy of the leader.

[0078] In some possible implementations, determining the flight strategy of the follower vehicle based on the dynamics model corresponding to the follower vehicle and the follower interference observation model includes: Based on the travel trajectory of the follower vehicle and the travel trajectory of the leader vehicle, a follower tracking error model corresponding to the follower vehicle is constructed; the follower tracking error model is used to determine the tracking error of the follower vehicle during the process of following the leader vehicle. Based on the dynamic model, the follower interference observation model, and the follower tracking error model corresponding to the follower vehicle, the driving strategy of the follower vehicle is determined.

[0079] In some possible implementations, constructing a follower tracking error model for the follower vehicle based on the travel trajectories of the follower vehicle and the leader vehicle includes: Based on the travel trajectory of the follower vehicle and the travel trajectory of the leader vehicle, determine the follower tracking error between the travel states of the follower vehicle and the travel states of the leader vehicle; The follower control input error between the virtual driving control input and the stabilization function set for the follower vehicle is determined; the stabilization function corresponding to the follower vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the follower vehicle. The follower tracking error model is constructed based on the follower tracking error and the follower control input error.

[0080] Specifically, the leader tracking error model can be represented by the following formula (6): (6) in, This indicates the follower tracking error; Indicates the status of the follower aircraft; This represents the information exchange between follower vehicles and is part of the Laplace matrix; This indicates the information exchange between the leader and follower aircraft. Represents the identity matrix; Indicates the status of the leader's aircraft; This indicates that the follower controls the input error; This indicates the virtual driving control inputs set for the follower vehicle. This represents the stabilization function set for the follower aircraft.

[0081] In some possible implementations, before determining the driving strategy of the follower vehicle based on the dynamics model, the follower interference observation model, and the follower tracking error model corresponding to the follower vehicle, the method further includes: Based on the follower tracking error and follower control input error obtained through the follower tracking error model, and the estimated sea state disturbance obtained through the follower disturbance observation model, a follower tracking error function is determined; the follower tracking error function is used to verify the stability of the follower tracking error model. The follower tracking error model is verified by the follower tracking error function, and it is determined that the follower tracking error is less than the third threshold.

[0082] In this step, a follower tracking error function, such as the Lyapunov function, can be designed to verify the stability of the follower tracking error model.

[0083] Specifically, the follower tracking error difference function can be represented by the following formula (7): (7) in, This represents the follower tracking error function; This indicates the follower tracking error; This indicates that the follower controls the input error; The non-singular symmetric positive definite inertial matrix represents the follower vehicle. This represents the target interference error between the estimated sea state interference obtained from the follower interference observation model and the corresponding actual sea state interference.

[0084] If the follower tracking error is less than the third threshold, it indicates that the follower tracking error is stabilizing, meaning that the trajectory of the follower vehicle is becoming closer to that of the leader vehicle.

[0085] In this way, the stability of the follower tracking error model is verified by using the follower tracking error function, ensuring that the disturbance estimation has convergence and stability under disturbance changes.

[0086] When determining the driving strategy of the leader vehicle based on the dynamic model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the following formula (8) can be used as a reference: (8) in, This represents the actual driving control input of the leader's vehicle; Represents the Coriolis force matrix and centripetal force matrix of the leader's aircraft; This indicates the total speed of the leader's aircraft; This represents the damping matrix of the leader's aircraft. The non-singular symmetric positive definite inertial matrix representing the leader vehicle; This represents the stabilization function set for the leader's aircraft. The rotation matrix representing the Earth's fixed coordinate system and the leader spacecraft's volume coordinate system; This indicates leader tracking error; This represents the leader control gain parameter; This indicates that the leader controls input errors; This indicates estimated sea state interference targeting the leader's vehicle.

[0087] When determining the driving strategy of the leader vehicle based on the dynamic model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the following formula (9) can be used as a reference: (9) in, This represents the actual driving control input of the follower vehicle; Represents the Coriolis force matrix and centripetal force matrix of the follower aircraft; This represents the damping matrix of the follower aircraft. This indicates the total speed of the follower aircraft; The non-singular symmetric positive definite inertial matrix represents the follower vehicle. This represents the stabilization function set for the follower aircraft. The rotation matrix between the Earth's fixed coordinate system and the volume coordinate system of the follower vehicle; Indicates the status of the follower aircraft; This represents the information exchange between follower vehicles and is part of the Laplace matrix; This indicates the information exchange between the leader and follower aircraft. Represents the identity matrix; Indicates the status of the leader's aircraft; This represents the follower control gain parameter; This indicates that the follower controls the input error; This indicates an estimated sea state disturbance for the follower vehicle.

[0088] For a clearer illustration of the process of controlling a marine vehicle, see [link to relevant documentation]. Figure 4 This is a schematic diagram illustrating a process for controlling a marine vehicle, as shown in an exemplary embodiment of this application. Figure 4 As shown, the process involves acquiring the state information of multiple ocean vehicles, including a leader vehicle and follower vehicles; constructing dynamic models for each ocean vehicle; constructing a leader interference observation model for the leader vehicle and a follower interference observation model for the follower vehicles; constructing a leader tracking error model for the leader vehicle; constructing a follower tracking error model for the follower vehicles; and determining the driving strategies for the leader vehicle and the follower vehicles. Specific steps are described in the preceding embodiments and will not be repeated here.

[0089] The marine vehicle control method provided in this application hierarchically divides a distributed marine vehicle system, which includes multiple marine vehicles, into leader vehicles and follower vehicles. It then performs hierarchical interference observation and determines corresponding driving strategies for the leader and follower vehicles based on the leader interference observation model and the follower interference observation model, respectively. This effectively solves the problem of cross-layer information transmission of marine vehicles. As a result, it can effectively realize hierarchical collaborative trajectory tracking control of the distributed marine vehicle system in complex environments where communication network topology changes randomly and external unknown interference exists simultaneously. This helps to improve the stability and anti-interference capability of the distributed marine vehicle system.

[0090] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0091] Corresponding to the aforementioned embodiments of the marine vehicle control method, this application also provides embodiments of a marine vehicle control device.

[0092] Please refer to Figure 5 This is a schematic diagram illustrating a marine vehicle control device as an exemplary embodiment of this application. Figure 5 As shown in the figure, the marine vehicle control device 500 provided in this application embodiment includes: Information acquisition module 501 is used to acquire status information of multiple marine vehicles; the multiple marine vehicles include a leader vehicle and a follower vehicle, the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the driving trajectory of the leader vehicle as a reference trajectory. The model building module 502 is used to construct a dynamic model corresponding to each of the ocean vehicles, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle, based on the state information of each ocean vehicle. The dynamic model is used to characterize the correlation between the actual driving control input of the ocean vehicle, the actual sea state interference experienced by the ocean vehicle during driving, and the dynamic state of the ocean vehicle. The leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving. The strategy determination module 503 is used to determine the driving strategy of the leader vehicle based on the dynamic model corresponding to the leader vehicle and the leader interference observation model, and to determine the driving strategy of the follower vehicle based on the dynamic model corresponding to the follower vehicle and the follower interference observation model.

[0093] In some possible implementations, the model building module 502, when constructing the dynamic model corresponding to each marine vehicle based on the state information of each marine vehicle, is specifically used for: Based on the state information of the marine vehicle, determine the transformation relationship of the marine vehicle's velocity between the body coordinate system and the Earth coordinate system; Based on the actual driving control input of the marine vehicle and the actual sea condition disturbances encountered by the marine vehicle during its operation, the speed change information of the marine vehicle is determined. Based on the transformation relationship and the velocity change information, the dynamic model is constructed.

[0094] In some possible implementations, the model building module 502, when determining the speed change information of the marine vehicle based on the actual navigation control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during navigation, is specifically used for: The first fusion result is determined between the actual driving control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during driving, and the second fusion result is determined between the inertial effect, centrifugal effect and hydrodynamic damping of the marine vehicle. The velocity change information is determined based on the deviation between the first fusion result and the second fusion result.

[0095] In some possible implementations, the model building module 502, when constructing the leader interference observation model corresponding to the leader vehicle, is specifically used for: Based on the state information of the leader vehicle, the correlation between the estimated sea state disturbance and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state disturbance approximate the actual sea state disturbance. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the leader disturbance observation model is constructed.

[0096] In some possible implementations, the model building module 502 is further configured to: Based on the estimated sea state disturbance obtained from the leader disturbance observation model, the leader disturbance error function is determined; the leader disturbance error function is used to verify the stability of the leader disturbance observation model. The leader interference observation model is validated by the leader interference error function, and it is determined that the target interference error between the estimated sea state interference obtained by the leader interference observation model and the corresponding real sea state interference is less than a first threshold.

[0097] In some possible implementations, the strategy determination module 503, when determining the driving strategy of the leader vehicle based on the dynamics model corresponding to the leader vehicle and the leader interference observation model, is specifically used for: Based on the leader vehicle's trajectory and the desired trajectory, a leader tracking error model is constructed for the leader vehicle; the leader tracking error model is used to determine the tracking error of the leader vehicle while it is following the desired trajectory. Based on the dynamics model, the interference observation model, and the tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined.

[0098] In some possible implementations, the strategy determination module 503, when constructing a leader tracking error model corresponding to the leader vehicle based on the leader vehicle's trajectory and the desired trajectory, is specifically used for: Based on the leader vehicle's trajectory and the desired trajectory, determine the leader tracking error between the leader vehicle's current state and the current state corresponding to the desired trajectory; Determine the leader control input error between the virtual driving control input and the stabilization function set for the leader vehicle; the stabilization function corresponding to the leader vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the leader vehicle. The leader tracking error model is constructed based on the leader tracking error and the leader control input error.

[0099] In some possible implementations, the strategy determination module 503 is further configured to: Based on the leader tracking error and leader control input error obtained through the leader tracking error model, and the sea state disturbance estimate obtained through the leader disturbance observation model, a leader tracking error function is determined; the leader tracking error function is used to verify the stability of the leader tracking error model. The leader tracking error model is verified by the leader tracking error function, and it is determined that the leader tracking error is less than the second threshold.

[0100] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0101] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0102] Based on the same technical concept, this application also provides a computer device 600, referring to... Figure 6 The diagram shown is a schematic representation of the structure of a computer device according to an exemplary embodiment of this application, comprising: The processor 610, memory 620, and bus 630 are included. The memory 620 is used to store execution instructions and includes main memory 621 and external memory 622. The main memory 621, also known as internal memory, is used to temporarily store the operation data in the processor 610 and the data exchanged with external memory 622 such as hard disk. The processor 610 exchanges data with external memory 622 through main memory 621.

[0103] In this embodiment, the memory 620 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 610. That is, when the electronic device 600 is running, the processor 610 communicates with the memory 620 through the bus 630, or the processor 610 communicates with the memory 620 through other means, so that the processor 610 executes the application code stored in the memory 620, and then executes the steps of the marine vehicle control method described in any of the foregoing embodiments.

[0104] The memory 620 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0105] Processor 610 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0106] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 600. In other embodiments of this application, the electronic device 600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0107] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the marine vehicle control method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0108] This disclosure also provides a computer program product, which stores a computer program. When the computer program is run by a processor, it executes the steps of the marine vehicle control method provided in any of the above embodiments of this disclosure. For details, please refer to the above method embodiments, which will not be repeated here.

[0109] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0110] Furthermore, embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0111] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0112] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0113] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0114] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0115] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0116] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0117] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling a marine vehicle, characterized in that, The method includes: The status information of multiple ocean vehicles is acquired; the multiple ocean vehicles include a leader vehicle and a follower vehicle, wherein the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the trajectory of the leader vehicle as a reference trajectory. Based on the state information of each of the aforementioned marine vehicles, a dynamic model corresponding to each marine vehicle, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle are constructed. The dynamic model is used to characterize the correlation between the actual driving control input of the marine vehicle, the actual sea state interference experienced by the marine vehicle during driving, and the dynamic state of the marine vehicle. The leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving. Based on the dynamics model and the leader interference observation model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined, and based on the dynamics model and the follower interference observation model corresponding to the follower vehicle, the driving strategy of the follower vehicle is determined. The process of determining the driving strategy of the leader vehicle based on the dynamics model and the leader interference observation model corresponding to the leader vehicle includes: Based on the leader vehicle's trajectory and the desired trajectory, a leader tracking error model is constructed for the leader vehicle; the leader tracking error model is used to determine the tracking error of the leader vehicle while it is following the desired trajectory. Based on the dynamics model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined. The step of constructing a leader tracking error model for the leader vehicle based on its trajectory and the desired trajectory includes: Based on the leader vehicle's trajectory and the desired trajectory, determine the leader tracking error between the leader vehicle's current state and the current state corresponding to the desired trajectory; Determine the leader control input error between the virtual driving control input and the stabilization function set for the leader vehicle; the stabilization function corresponding to the leader vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the leader vehicle. Based on the leader tracking error and the leader control input error, the leader tracking error model is constructed; Before determining the driving strategy of the leader vehicle based on the dynamics model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the method further includes: Based on the leader tracking error and leader control input error obtained through the leader tracking error model, and the sea state disturbance estimate obtained through the leader disturbance observation model, a leader tracking error function is determined; the leader tracking error function is used to verify the stability of the leader tracking error model. The leader tracking error model is verified by the leader tracking error function, and it is determined that the leader tracking error is less than the second threshold.

2. The method according to claim 1, characterized in that, Based on the state information of each of the aforementioned marine vehicles, a dynamic model corresponding to each marine vehicle is constructed, including: Based on the state information of the marine vehicle, determine the transformation relationship of the marine vehicle's velocity between the body coordinate system and the Earth coordinate system; Based on the actual driving control input of the marine vehicle and the actual sea condition disturbances encountered by the marine vehicle during its operation, the speed change information of the marine vehicle is determined. Based on the transformation relationship and the velocity change information, the dynamic model is constructed.

3. The method according to claim 2, characterized in that, The determination of the speed change information of the ocean vehicle based on the actual navigation control input of the ocean vehicle and the actual sea state disturbances encountered by the ocean vehicle during navigation includes: The first fusion result is determined between the actual driving control input of the marine vehicle and the actual sea state disturbances experienced by the marine vehicle during driving, and the second fusion result is determined between the inertial effect, centrifugal effect and hydrodynamic damping of the marine vehicle. The velocity change information is determined based on the deviation between the first fusion result and the second fusion result.

4. The method according to claim 1, characterized in that, Constructing the leader interference observation model corresponding to the leader vehicle includes: Based on the state information of the leader vehicle, the correlation between the estimated sea state disturbance and speed and auxiliary variables is determined, as well as the change information of the auxiliary variables; the auxiliary variables are used to make the estimated sea state disturbance approximate the actual sea state disturbance. Based on the estimated correlation between sea state disturbance and speed, auxiliary variables, and the change information of the auxiliary variables, the leader disturbance observation model is constructed.

5. The method according to claim 1 or 4, characterized in that, Before determining the driving strategy of the leader vehicle based on the dynamics model and the leader interference observation model corresponding to the leader vehicle, the method further includes: Based on the estimated sea state disturbance obtained from the leader disturbance observation model, the leader disturbance error function is determined; the leader disturbance error function is used to verify the stability of the leader disturbance observation model. The leader interference observation model is validated by the leader interference error function, and it is determined that the target interference error between the estimated sea state interference obtained by the leader interference observation model and the corresponding real sea state interference is less than a first threshold.

6. A control device for a marine vehicle, characterized in that, The device includes: An information acquisition module is used to acquire the status information of multiple ocean vehicles; the multiple ocean vehicles include a leader vehicle and a follower vehicle, wherein the leader vehicle uses the received desired trajectory as a reference trajectory, and the follower vehicle uses the driving trajectory of the leader vehicle as a reference trajectory. The model building module is used to construct a dynamic model corresponding to each of the ocean vehicles, a leader interference observation model corresponding to the leader vehicle, and a follower interference observation model corresponding to the follower vehicle, based on the state information of each ocean vehicle. The dynamic model is used to characterize the correlation between the actual driving control input of the ocean vehicle, the actual sea state interference experienced by the ocean vehicle during driving, and the dynamic state of the ocean vehicle. The leader interference observation model is used to estimate the sea state interference experienced by the leader vehicle during driving, and the follower interference observation model is used to estimate the sea state interference experienced by the follower vehicle during driving. The strategy determination module is used to determine the driving strategy of the leader vehicle based on the dynamic model corresponding to the leader vehicle and the leader interference observation model, and to determine the driving strategy of the follower vehicle based on the dynamic model corresponding to the follower vehicle and the follower interference observation model. When determining the driving strategy of the leader vehicle based on the dynamics model and the leader interference observation model corresponding to the leader vehicle, the strategy determination module is specifically used for: Based on the leader vehicle's trajectory and the desired trajectory, a leader tracking error model is constructed for the leader vehicle; the leader tracking error model is used to determine the tracking error of the leader vehicle while it is following the desired trajectory. Based on the dynamics model, the leader interference observation model, and the leader tracking error model corresponding to the leader vehicle, the driving strategy of the leader vehicle is determined. When the strategy determination module is used to construct the leader tracking error model corresponding to the leader vehicle based on the leader vehicle's trajectory and the expected trajectory, it is specifically used for: Based on the leader vehicle's trajectory and the desired trajectory, determine the leader tracking error between the leader vehicle's current state and the current state corresponding to the desired trajectory; Determine the leader control input error between the virtual driving control input and the stabilization function set for the leader vehicle; the stabilization function corresponding to the leader vehicle is used to offset the deviation between the virtual driving control input and the actual driving control input corresponding to the leader vehicle. Based on the leader tracking error and the leader control input error, the leader tracking error model is constructed; The strategy determination module is also used for: Based on the leader tracking error and leader control input error obtained through the leader tracking error model, and the sea state disturbance estimate obtained through the leader disturbance observation model, a leader tracking error function is determined; the leader tracking error function is used to verify the stability of the leader tracking error model. The leader tracking error model is verified by the leader tracking error function, and it is determined that the leader tracking error is less than the second threshold.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the marine vehicle control method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the marine vehicle control method according to any one of claims 1 to 5.

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