Intelligent control method and system applied to marine floating target

Abstract

The invention discloses an intelligent control method and system applied to a marine floating target, and the method comprises the steps: collecting target ship information and marine environment data in real time through a multi-mode sensor array, and carrying out the time-space alignment and fusion processing, and obtaining an environment state vector; based on the ship kinematics principle, wind and flow disturbance force is decomposed into resultant force and moment vectors under a ship body coordinate system; evaluating the danger degree of the current navigation environment through the dynamic risk index, carrying out navigation risk intelligent grading, and triggering a grading control strategy; a self-adaptive controller is designed based on the Lyapunov stability theory, anti-interference dynamic adjustment is carried out on the target ship, and track deviation caused by environmental interference is eliminated; when an obstacle or a high-risk sea condition is detected, intelligent obstacle avoidance and path re-planning are carried out, and an optimized path for avoiding the obstacle or a surge area is generated in real time; and calculating the minimum safety distance based on the ship dynamics model, and implementing a graded braking strategy to perform dynamic safety braking on the target ship.

Description

Technical Field

[0001] This invention relates to the field of wireless control technology, and more specifically to an intelligent control method and system for use with floating targets at sea. Background Technology

[0002] Floating targets are dynamic maritime targets designed for army aviation units for live-fire or simulated training of active-duty air-to-ground missiles and rockets. By using floating targets, naval personnel can hone their reaction speed, aiming skills, and tactical decision-making abilities in a near-combat environment, making them an indispensable piece of equipment in modern naval training.

[0003] Currently, the floating target at sea mainly consists of a hull, a target body, and a remote control system. The hull serves as a carrier, supporting the floating and movement of the target on the sea surface. The target body is used to simulate maritime targets and has realistic target characteristics. The remote control system enables remote control and parameter adjustment of the floating target.

[0004] However, existing methods for controlling floating targets at sea have the following problems:

[0005] Data acquisition typically relies on a single sensor, such as a camera, which fails when obscured by rain, fog, or surges, leading to unreliable environmental perception and paralyzed obstacle avoidance functions.

[0006] The calculation of wind / current disturbance forces does not take into account ship dynamics, such as inertia and rudder effect delay. The obstacle avoidance path planning lacks kinematic constraints and is prone to generating infeasible paths.

[0007] The safety distance model is based on empirical constants and does not dynamically correlate with hull mass, braking performance and wave height, resulting in a rigid safety mechanism.

[0008] The perception, control, and obstacle avoidance modules operate independently, leading to conflicting responses in emergency situations and a lack of system coordination. Summary of the Invention

[0009] In view of this, the present invention provides an intelligent control method and system for offshore floating targets to solve some of the technical problems mentioned in the background.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] A smart control method for offshore floating targets includes the following steps:

[0012] S1. Real-time acquisition of target ship information and marine environmental data through a multimodal sensor array, followed by spatiotemporal alignment and fusion processing to obtain an environmental state vector;

[0013] S2. Based on the principles of ship kinematics, the wind and current interference forces are decomposed into resultant forces and moment vectors in the ship's coordinate system;

[0014] S3. Assess the degree of danger of the current navigation environment through dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies;

[0015] S4. An adaptive controller is designed based on Lyapunov stability theory to dynamically adjust the target ship to eliminate track deviation caused by environmental interference.

[0016] S5. When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or surge areas in real time.

[0017] S6. Calculate the minimum safe distance based on the ship dynamics model and implement a graded braking strategy to dynamically brake the target ship.

[0018] Preferably, step S1 includes the following:

[0019] S11. Deploy multimodal sensors on the target ship to build an all-round perception network, collect information about the target ship and its navigation environment. The multimodal sensors include millimeter-wave radar, binocular camera, IMU, water flow velocity sensor and ultrasonic anemometer.

[0020] S12. Extended Kalman filter is used to fuse sensor data, perform spatiotemporal alignment of multi-source data, and output the fused environmental state vector.

[0021] Preferably, step S2 includes the following:

[0022] S21. Based on the environmental state vector obtained in step S1, calculate the components of wind force and water flow force in the longitudinal and transverse directions of the hull;

[0023] S22. The disturbance force components are integrated into a three-dimensional torque vector, which is used as the input parameter for dynamic adjustment to compensate for environmental disturbances.

[0024] Preferably, step S3 includes the following:

[0025] S31. Calculate the real-time risk index by taking into account environmental disturbances and wave height factors;

[0026] S32. Automatically switch control modes based on risk index thresholds and execute risk grading strategies.

[0027] Preferably, the risk index threshold is preset based on the critical value of the sea state level; the control modes include low-risk mode, medium-risk mode and high-risk mode; the risk classification strategy is implemented as follows: in low-risk mode, the PID controller is used to track the predetermined route and the console outputs standard rudder angle commands; in medium-risk mode, the anti-interference dynamic adjustment algorithm is activated; in high-risk mode, intelligent obstacle avoidance path planning is triggered.

[0028] Preferably, the specific content of step S4 is as follows:

[0029] S41. Calculate the deviation between the current position and the planned route;

[0030] S42. Construct an energy function that guarantees system stability based on the Lyapunov function;

[0031] S43. Solve for the anti-interference control law, derive the control command to drive the error to converge to zero, and output the control vector.

[0032] Preferably, the specific content of step S5 is as follows:

[0033] S51. Construct a three-dimensional obstacle map using SLAM (Simultaneous Localization and Mapping) technology;

[0034] S52. Design a cost function that includes environmental disturbance compensation;

[0035] S53. Expand random tree nodes from the current position, calculate the cost value of the new node based on the cost function, select the collision-free path with the minimum cost, and output the heading angle adjustment command to the target ship actuator in real time.

[0036] Preferably, the specific content of step S6 is as follows:

[0037] S61. Calculate the minimum braking distance, i.e., the minimum safe distance, in real time based on ship speed, braking performance, and wave height:

[0038] S62. Based on the dynamic response to obstacle distance, perform graded braking, including: during the warning phase, finely adjust the rudder angle to avoid obstacles; during the emergency braking phase, trigger a full-speed reverse command.

[0039] An intelligent control system for a floating target at sea, based on the aforementioned intelligent control method for a floating target at sea, includes: an intelligent device module, a data acquisition module, a data processing module, a dynamic adjustment module, and an intelligent judgment module. The intelligent device module includes a target vessel and a control console.

[0040] The data acquisition module is used to collect target ship information and marine environmental data in real time through a multimodal sensor array, and perform spatiotemporal alignment and fusion processing to obtain an environmental state vector;

[0041] The data processing module is used to perform spatiotemporal alignment and fusion processing on the collected data to obtain the environmental state vector, and based on the principles of ship kinematics, decompose the wind and current interference forces into the resultant force and torque vectors in the ship coordinate system.

[0042] The intelligent judgment module is used to assess the degree of danger of the current navigation environment through a dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies.

[0043] The dynamic adjustment module, through an adaptive controller designed based on Lyapunov stability theory, performs anti-interference dynamic adjustment of the target ship to eliminate track deviation caused by environmental interference.

[0044] When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or swell areas in real time.

[0045] The minimum safe distance is calculated based on the ship dynamics model, and a graded braking strategy is implemented to dynamically and safely brake the target ship.

[0046] The control console is used to control the target ship to move along a predetermined route, and to dynamically control the movement of the target ship based on the control parameters of the dynamic adjustment module.

[0047] Preferably, the intelligent control system for use with floating targets at sea further includes a path preset module for generating and storing a predetermined path based on the combat mission.

[0048] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses an intelligent control method and system for marine floating targets. Through multi-source sensor fusion and ship kinematics modeling, the control accuracy and reliability under complex sea conditions are significantly improved; the dynamic adjustment algorithm based on Lyapunov stability reduces track tracking error; intelligent obstacle avoidance and path replanning improve obstacle avoidance success rate and shorten path planning time; and the dynamic safety distance model reduces emergency braking distance and effectively avoids collisions. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 A schematic diagram of an intelligent control method for offshore floating targets provided by the present invention;

[0051] Figure 2 This is a schematic diagram of the hierarchical control strategy provided by the present invention;

[0052] Figure 3 This is a schematic diagram of an intelligent control system for a floating target at sea, provided by the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] This invention discloses an intelligent control method for use with marine floating targets, such as... Figure 1 This includes the following steps:

[0055] S1. Real-time acquisition of target ship information and marine environmental data through a multimodal sensor array, followed by spatiotemporal alignment and fusion processing to obtain an environmental state vector;

[0056] S2. Based on the principles of ship kinematics, the wind and current interference forces are decomposed into resultant forces and moment vectors in the ship's coordinate system;

[0057] S3. Assess the degree of danger of the current navigation environment through dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies;

[0058] S4. An adaptive controller is designed based on Lyapunov stability theory to dynamically adjust the target ship to eliminate track deviation caused by environmental interference.

[0059] S5. When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or surge areas in real time.

[0060] S6. Calculate the minimum safe distance based on the ship dynamics model and implement a graded braking strategy to dynamically brake the target ship.

[0061] To further implement the above technical solution, the specific content of step S1 includes:

[0062] S11. Deploy multimodal sensors on the target ship to build an all-round perception network, collect information about the target ship and its navigation environment. The multimodal sensors include millimeter-wave radar, binocular camera, IMU, water flow velocity sensor and ultrasonic anemometer.

[0063] S12. Extended Kalman filter is used to fuse sensor data, perform spatiotemporal alignment of multi-source data, and output the fused environmental state vector.

[0064] In this embodiment, the specific method for fusing sensor data using the extended Kalman filter algorithm is as follows: sensor data from different coordinate systems are uniformly transformed to the ship's central coordinate system; acquisition delay is eliminated through timestamp synchronization; and the fused environmental state vector E is output. t ;

[0065] E t =[F,θ F V q ,θ Vq H wave ] T

[0066] Where F is the wind speed, θ is the wind angle, and θ is the yaw rate. F That is, the angle between the wind direction and the bow direction, V q Let θ be the flow velocity. Vq The angle between the water flow direction and the bow direction is H. wave The waves are high.

[0067] To further implement the above technical solution, step S2 includes the following:

[0068] S21. Based on the environmental state vector obtained in step S1, calculate the components of wind force and water flow force in the longitudinal and transverse directions of the hull;

[0069] S22. The disturbance force components are integrated into a three-dimensional torque vector, which is used as the input parameter for dynamic adjustment to compensate for environmental disturbances.

[0070] In this embodiment, the disturbance torque vector is:

[0071] τ env =[T x ,T y ,0] T

[0072] Among them, T x T represents the longitudinal component of the resultant force of wind and airflow interference along the x-axis. y This represents the horizontal component, i.e., the y-axis.

[0073] To further implement the above technical solutions, such as Figure 2 The specific content of step S3 includes:

[0074] S31. Calculate the real-time risk index R by taking into account environmental disturbance and wave height factors;

[0075]

[0076] Where M is the ship's weight, k is the wave height risk coefficient, which is typically 0.5 after sea trials, and ||τ env || represents the magnitude of the interference force vector, reflecting the total interference intensity;

[0077] S32. Automatically switch control modes based on risk index thresholds and execute risk grading strategies.

[0078] To further implement the above technical solution, the risk index threshold is preset according to the critical value of the sea state level; the control mode includes low risk mode, medium risk mode and high risk mode; the risk classification strategy is implemented as follows: in low risk mode, the PID controller is used to track the predetermined route and the console outputs the standard rudder angle command, i.e. step S4; in medium risk mode, the anti-interference dynamic adjustment algorithm is started; in high risk mode, intelligent obstacle avoidance path planning is triggered, i.e. step S5.

[0079] In this embodiment, the risk index threshold is preset to R. th1 and R th2 R <R th1 In low-risk mode, R th1 ≤R <R th2 In the medium-risk mode, R ≥ R th2 Or obstacle warning is a high-risk mode. In practical applications, the critical value R for sea state level 3 is... th1 =0.3, critical value R for sea state 5 th2 =0.6.

[0080] To further implement the above technical solution, the specific content of step S4 is as follows:

[0081] S41. Calculate the deviation between the current position and the planned route;

[0082]

[0083] in, Given the target position coordinates and the bow vector, This is the current actual pose;

[0084] S42. Construct an energy function that guarantees system stability based on the Lyapunov function;

[0085]

[0086] V is a scalar function that represents the energy difference between the current state of the system and the target state. By designing a control law that makes V decrease with time, the error e can be guaranteed to converge asymptotically to zero, thus achieving stable tracking of the trajectory.

[0087] S43. Solve for the anti-interference control law, derive the control command to drive the error to converge to zero, and output the control vector;

[0088]

[0089] Where u is the output control vector, including thrust and rudder angle, and K p and K d The proportional and differential gain matrices were calibrated through ship model experiments. Let be the second derivative of the desired pose.

[0090] To further implement the above technical solution, the specific content of step S5 is as follows:

[0091] S51. Construct a three-dimensional obstacle map using SLAM (Simultaneous Localization and Mapping) technology;

[0092] Specifically, it includes:

[0093] Fusion of millimeter-wave radar point cloud and binocular vision data;

[0094] Generate a rasterized 3D map and mark the coordinates of static obstacles;

[0095] Areas with wave heights exceeding a preset safety threshold are marked as dynamic danger zones;

[0096] S52. Design a cost function that includes environmental disturbance compensation;

[0097] J = PathLength + α·||τ env ||

[0098] Wherein, PathLength is the total length of the planned path, calculated by accumulating the Euclidean distance between path nodes, and α is the interference weight factor, typically 0.2; by minimizing J, the generated path satisfies both geometric shortestness and environmental resistance to interference; in practical applications, if two paths are similar in length, the system will automatically select the route with less wind and wave interference.

[0099] S53. Expand random tree nodes from the current position, calculate the cost value of the new node based on the cost function, select the collision-free path with the minimum cost, and output the heading angle adjustment command to the target ship actuator in real time.

[0100] In this embodiment, the energy function V is used for closed-loop control to ensure the stability of dynamic adjustment; the cost function J is used for path planning to achieve synergistic optimization of obstacle avoidance and anti-interference; the two are linked through a central decision-maker: when the error displayed by V increases, the replanning mechanism of J is triggered, and the path output by J is used as the new expected input of V to form a closed loop.

[0101] To further implement the above technical solution, the specific content of step S6 is as follows:

[0102] S61. Calculate the minimum braking distance, i.e., the minimum safe distance, in real time based on ship speed, braking performance, and wave height:

[0103]

[0104] Among them, V t Given the current ship speed, a max , where is the maximum reverse acceleration, is the maximum thrust of the thruster / M, and β is the wave height buffer coefficient, which is generally taken as 0.8;

[0105] S62. Based on the dynamic response to obstacle distance, perform graded braking, including:

[0106] During the warning phase, the rudder angle is finely adjusted to avoid obstacles, and the obstacle avoidance path in step S5 is output at this time.

[0107] During emergency braking, trigger the full-speed reverse command. brake ;

[0108]

[0109] Among them, K brake The braking gain coefficient is calibrated through hydrodynamic testing.

[0110] An intelligent control system for offshore floating targets, such as Figure 3 Based on an intelligent control method for offshore floating targets, the method includes: an intelligent device module, a data acquisition module, a data processing module, a dynamic adjustment module, and an intelligent judgment module. The intelligent device module includes the target ship and the control console.

[0111] The data acquisition module is used to collect target ship information and marine environmental data in real time through a multimodal sensor array, and perform spatiotemporal alignment and fusion processing to obtain an environmental state vector;

[0112] The data processing module is used to perform spatiotemporal alignment and fusion processing on the collected data to obtain the environmental state vector, and based on the principles of ship kinematics, decompose the wind and current interference forces into the resultant force and torque vectors in the ship coordinate system.

[0113] The intelligent judgment module is used to assess the degree of danger of the current navigation environment through a dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies.

[0114] The dynamic adjustment module, through an adaptive controller designed based on Lyapunov stability theory, performs anti-interference dynamic adjustment of the target ship to eliminate track deviation caused by environmental interference.

[0115] When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or swell areas in real time.

[0116] The minimum safe distance is calculated based on the ship dynamics model, and a graded braking strategy is implemented to dynamically and safely brake the target ship.

[0117] The control console is used to control the target ship to move along a predetermined route, and to dynamically control the movement of the target ship based on the control parameters of the dynamic adjustment module.

[0118] To further implement the above technical solution, an intelligent control system for maritime floating targets also includes a path preset module, which generates and stores a predetermined path based on the combat mission.

[0119] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an intelligent control method for a floating target at sea.

[0120] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements an intelligent control method for a floating target at sea.

[0121] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart control method for offshore floating targets, characterized in that, Includes the following steps: S1. Real-time acquisition of target ship information and marine environmental data through a multimodal sensor array, followed by spatiotemporal alignment and fusion processing to obtain an environmental state vector; S2. Based on the principles of ship kinematics, the wind and current interference forces are decomposed into resultant forces and moment vectors in the ship's coordinate system; S3. Assess the degree of danger of the current navigation environment through dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies; S4. An adaptive controller is designed based on Lyapunov stability theory to dynamically adjust the target ship to eliminate track deviation caused by environmental interference. S5. When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or surge areas in real time. S6. Calculate the minimum safe distance based on the ship dynamics model and implement a graded braking strategy to dynamically brake the target ship.

2. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S1 includes: S11. Deploy multimodal sensors on the target ship to build an all-round perception network, collect information about the target ship and its navigation environment. The multimodal sensors include millimeter-wave radar, binocular camera, IMU, water flow velocity sensor and ultrasonic anemometer. S12. Extended Kalman filter is used to fuse sensor data, perform spatiotemporal alignment of multi-source data, and output the fused environmental state vector.

3. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S2 includes: S21. Based on the environmental state vector obtained in step S1, calculate the components of wind force and water flow force in the longitudinal and transverse directions of the hull; S22. The disturbance force components are integrated into a three-dimensional torque vector, which is used as the input parameter for dynamic adjustment to compensate for environmental disturbances.

4. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S3 includes: S31. Calculate the real-time risk index by taking into account environmental disturbances and wave height factors; S32. Automatically switch control modes based on risk index thresholds and execute risk grading strategies.

5. The intelligent control method for offshore floating targets according to claim 4, characterized in that, The risk index threshold is preset based on the critical value of the sea state level; the control modes include low-risk mode, medium-risk mode and high-risk mode; the risk classification strategy is implemented as follows: in low-risk mode, the PID controller is used to track the predetermined route and the console outputs standard rudder angle commands; in medium-risk mode, the anti-interference dynamic adjustment algorithm is activated; in high-risk mode, intelligent obstacle avoidance path planning is triggered.

6. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S4 is as follows: S41. Calculate the deviation between the current position and the planned route; S42. Construct an energy function that guarantees system stability based on the Lyapunov function; S43. Solve for the anti-interference control law, derive the control command to drive the error to converge to zero, and output the control vector.

7. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S5 is as follows: S51. Construct a three-dimensional obstacle map using SLAM (Simultaneous Localization and Mapping) technology; S52. Design a cost function that includes environmental disturbance compensation; S53. Expand random tree nodes from the current position, calculate the cost value of the new node based on the cost function, select the collision-free path with the minimum cost, and output the heading angle adjustment command to the target ship actuator in real time.

8. The intelligent control method for offshore floating targets according to claim 1, characterized in that, The specific content of step S6 is as follows: S61. Calculate the minimum braking distance, i.e., the minimum safe distance, in real time based on ship speed, braking performance, and wave height: S62. Based on the dynamic response to obstacle distance, perform graded braking, including: during the warning phase, finely adjust the rudder angle to avoid obstacles; during the emergency braking phase, trigger a full-speed reverse command.

9. An intelligent control system for offshore floating targets, characterized in that, A smart control method for a floating target at sea, based on any one of claims 1-8, includes: a smart device module, a data acquisition module, a data processing module, a dynamic adjustment module, and a smart judgment module. The smart device module includes a target ship and a control console. The data acquisition module is used to collect target ship information and marine environmental data in real time through a multimodal sensor array, and perform spatiotemporal alignment and fusion processing to obtain an environmental state vector; The data processing module is used to perform spatiotemporal alignment and fusion processing on the collected data to obtain the environmental state vector, and based on the principles of ship kinematics, decompose the wind and current interference forces into resultant force and moment vectors in the ship coordinate system. The intelligent judgment module is used to assess the degree of danger of the current navigation environment through a dynamic risk index, perform intelligent classification of navigation risks, and trigger classified control strategies. The dynamic adjustment module, through an adaptive controller designed based on Lyapunov stability theory, performs anti-interference dynamic adjustment of the target ship to eliminate track deviation caused by environmental interference. When obstacles or high-risk sea conditions are detected, intelligent obstacle avoidance and path replanning are performed to generate optimized paths that avoid obstacles or swell areas in real time. The minimum safe distance is calculated based on the ship dynamics model, and a graded braking strategy is implemented to dynamically and safely brake the target ship. The control console is used to control the target ship to move along a predetermined route, and to dynamically control the movement of the target ship based on the control parameters of the dynamic adjustment module.

10. The intelligent control system for a marine floating target according to claim 9, characterized in that, It also includes a path pre-setting module, which is used to generate and store pre-defined paths based on combat missions.

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