A networked surface unmanned vehicle formation scheduled time tracking control method

Abstract

The application discloses a networked sea surface unmanned warship formation pre-time tracking control method, which comprises the following steps: performing system kinematics and dynamics modeling on a formation comprising N networked sea surface unmanned warships, setting a virtual leader and the rest as followers; designing a directed communication topology graph according to the communication conditions among the unmanned warships; constructing a tracking error of the networked sea surface unmanned warship formation system and designing a hierarchical control framework for the system; estimating the real-time state of the virtual leader by using a distributed estimation algorithm based on event triggering and delivering the real-time state to each unmanned warship of the local layer; and adjusting each unmanned warship to form the designed formation shape within the pre-time by using a local control algorithm based on pre-time sliding mode control. The application is more suitable for practical scene application, only needs the virtual leader to have a directed spanning tree, and the required convergence time can be set in advance according to actual conditions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of marine control technology, and particularly relates to a networked sea surface unmanned ship formation predetermined time tracking control method. BACKGROUND

[0002] It is well known that most of the earth's surface is covered by the ocean, and the ocean not only provides an environment for a variety of organisms to survive, but also has rich resources such as minerals and fuels. According to the traditional mode, a large amount of manpower and material resources are needed to maintain the rights and interests of the ocean. Therefore, the development of sea surface unmanned ships is particularly important for the exploitation, transportation and protection of marine resources.

[0003] In recent years, unmanned ship technology has developed rapidly, and with its small size, strong maneuverability, low application cost and many other advantages, it plays an important role in resource exploitation, transportation, underwater map measurement and drawing, and automatic feeding of civil aquaculture. When facing complex and heavy tasks, the capacity of a single unmanned ship is greatly tested, and networked sea surface unmanned ships have greater flexibility and stronger working capacity. Networked unmanned ships have become one of the important research topics at home and abroad. Initially, the control method proposed for the research is based on centralized control development. This control method relies too much on the control and scheduling of the central controller. Once the central controller is attacked or accidentally damaged, the entire system will face the problem of paralysis. Distributed control schemes usually have greater flexibility and robustness, and distributed control schemes rely on information exchange between nodes. In the case of limited communication resources, it is difficult to ensure the normal operation of the entire system. The emergence of event-triggered control solves this problem by setting measurement errors and trigger thresholds to further reduce the local communication burden and control cost of unmanned ships. In addition, unmanned ship formations usually perform different tasks or need to change different formations. If the convergence time of the formation can be adjusted and determined in advance, it will undoubtedly bring great convenience to the control personnel, and the unmanned ship formation will have stronger adaptability and higher execution efficiency. SUMMARY

[0004] Therefore, the present application proposes a networked sea surface unmanned ship formation predetermined time tracking control method, comprising the following steps:

[0005] S1, system kinematics and dynamics modeling is performed on the formation containing N networked sea surface unmanned ships, a virtual leader is set in the N unmanned ships, and the rest are set as followers;

[0006] S2, a directed communication topology graph is designed to describe the communication situation of the virtual leader according to the communication situation between each unmanned ship;

[0007] S3. Based on the communication topology diagram and the constructed kinematic and dynamic models, construct the tracking error of the networked unmanned surface vessel formation system, and design a hierarchical control framework for the system, including an event-triggered distributed estimation algorithm and a local control algorithm based on predetermined time sliding mode control.

[0008] S4. An event-triggered distributed estimation algorithm is used to estimate the real-time state of the virtual leader and transmit it to each unmanned vessel in the local layer. Then, a local control algorithm based on predetermined time sliding mode control is used to adjust each unmanned vessel to form the designed formation within a predetermined time.

[0009] The beneficial effects of the technical solution provided by this invention are:

[0010] (1) It is more in line with the actual application scenario, that is, it takes into account the stability of the system and the normal operation of the formation under the condition of limited communication and control resources.

[0011] (2) The communication between the ships is described by a directed communication topology graph. Compared with an undirected communication topology graph, its advantage is that it does not require bidirectional communication between nodes (unmanned ships), and only the root node (virtual leader) needs to have a directed spanning tree.

[0012] (3) This technical solution realizes the predetermined time tracking control of unmanned vessel formations. Compared with ordinary control schemes, this scheme can set the required convergence time in advance according to the actual situation, which brings great convenience to the multi-task execution of unmanned vessels. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the method design of an embodiment of the present invention;

[0014] Figure 2 This is a flowchart illustrating the method control of an embodiment of the present invention;

[0015] Figure 3 This is a coordinate structure diagram of the unmanned vessel provided in the example of this invention;

[0016] Figure 4 This is a communication topology diagram between unmanned vessels in an example of the present invention;

[0017] Figure 5 This is a tracking diagram of the estimated position and estimated velocity of the unmanned vessel in an embodiment of the present invention; the diagram shows... , , Unmanned vessels i Estimated location exist x Components of direction y Components of direction and rotation; , , Unmanned vessels i Estimated speed exist x Components of direction y Components of direction and rotation;

[0018] Figure 6 This is a diagram showing the estimated position and estimated velocity errors of an unmanned surface vessel in an embodiment of the present invention; in the diagram, the unmanned surface vessel... i Estimated position error Estimate speed error , , , Unmanned vessels i Estimated position error exist x Components of direction y Components of direction and rotation; , , Unmanned vessels i Estimated speed error exist x Components of direction y Components of direction and rotation;

[0019] Figure 7 This is a tracking diagram of the actual position and speed of the unmanned vessel in an embodiment of the present invention; in the diagram, the unmanned vessel... i The actual location is an unmanned vessel. i Position in the Earth coordinate system Subtract formation offset , , , These represent unmanned vessels. i The actual location is x Components of direction y Components of direction and rotation; , , These represent unmanned vessels. i actual speed exist x Components of direction y Components of direction and rotation;

[0020] Figure 8 This is a diagram showing the actual position and speed error of the unmanned vessel in an embodiment of the present invention; the diagram shows the actual position error of the unmanned vessel. The actual speed error of unmanned vessels , , , These represent unmanned vessels. i Actual position error exist x Components of direction y Components of direction and rotation; , , These represent unmanned vessels. i The actual speed error is within x Components of direction y Components of direction and rotation;

[0021] Figure 9 This is a diagram showing the triggering times of each unmanned vessel in this embodiment of the invention; the horizontal axis represents the moment when each unmanned vessel's event is triggered, i.e., the moment when it communicates with neighboring unmanned vessels and the controller updates.

[0022] Figure 10 This is a formation tracking diagram formed by unmanned vessels in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0024] This invention provides a predetermined time tracking and control method for networked unmanned surface vessels, which can track the predetermined time of networked unmanned surface vessels with limited communication and control resources, and allows for arbitrary specification of the system's stable time by adjusting parameters.

[0025] refer to Figure 1 , Figure 1 This is a flowchart illustrating the method proposed in this invention, specifically including the following steps:

[0026] S1. Perform system kinematics and dynamics modeling on a formation consisting of N networked unmanned surface vessels. Set a virtual leader among the N unmanned vessels, and set the rest as followers.

[0027] S11, For the first in the formation The kinematic and dynamic models required for the unmanned surface vessel system are constructed as follows:

[0028] (1)

[0029]

[0030] in: , , Let these represent the system's inertia matrix, Coriolis and centripetal matrices, and hydrodynamic damping matrix, respectively. Represents the rotation transformation matrix. Indicates the first The rotation angle of an unmanned surface vessel system in the Earth coordinate system Indicates the first An unmanned surface vessel system at a fixed Earth coordinate The position vector in the middle, express The first derivative, Indicates the first The coordinates of the unmanned surface vessel system within the entire formation system The velocity vector in express exist directional components, express exist y directional components, express The rotational component, express The first derivative, and They represent the first Control inputs and external disturbances of an unmanned surface vessel system; Indicates control input exist directional components, Indicates control input The component in the y-direction, Indicates control input The rotational component, Indicates external disturbance exist Components of direction External disturbances exist Components of direction Indicates external disturbance The rotational component.

[0031] refer to Figure 3 , Figure 3 This is a coordinate structure diagram of the unmanned vessel provided in the example of this invention.

[0032] S12. To facilitate the design of the control scheme, according to the coordinate transformation formula, formula (1) is transformed into the following form:

[0033] (2)

[0034] in: , , for The first derivative.

[0035] S13. The model of the virtual leader in the Earth's fixed coordinate system is established as follows:

[0036] (3)

[0037] in, , , These represent the virtual leader's position, velocity, and acceleration vector in the Earth's fixed coordinate system, respectively. express The first derivative, express The first derivative.

[0038] Assume the virtual leader's reference trajectory is as follows:

[0039]

[0040] like Figure 2 As shown, Figure 2 This is a flowchart of the control method in an embodiment of the present invention. In the study of this algorithm, Cyber-Ships II was selected as the model for dynamic numerical simulation. The parameters of each ship are constructed according to the 1:70 scale of Cyber-Ships II supply ship. The specific parameters are: mass m=23.8kg, length L=1.255m, and width B=0.29m.

[0041] S2. Based on the communication between the various unmanned vessels, design a directed communication topology graph to describe the communication status of the virtual leader. (Reference) Figure 4 , Figure 4 This is a communication topology diagram between unmanned vessels in an example of the present invention. In this embodiment, N is 4.

[0042] S21. A directed communication topology graph containing N nodes. ,in , Figures are respectively The set of points and the set of edges, using Representation diagram The adjacency matrix and when From time to time This indicates unmanned vessels. Capable of receiving unmanned vessels Information, when hour ; Representation diagram The Laplace matrix; where, Represents the Laplace matrix An element in the set, and that element satisfies , ; express Dimensional Euclidean space.

[0043] S22, Define the traction matrix Describe the communication between the virtual leader and followers, if , indicating the first Each unmanned vessel can directly receive information from the virtual leader, and vice versa. Then it means the first Unmanned vessels cannot directly receive information from the virtual leader.

[0044] S3. Based on the communication topology diagram and the constructed dynamic model, construct the tracking error of the networked unmanned surface vessel formation system, and design a hierarchical control framework for the system, including an event-triggered distributed estimation algorithm and a local control algorithm based on predetermined time sliding mode control.

[0045] S31. Based on the communication topology diagram and the constructed kinematic and dynamic models, the tracking error of the networked unmanned surface vessel formation system is as follows:

[0046] (4)

[0047] in: and They represent the first unmanned vessels t Position tracking error and velocity tracking error at all times in a fixed Earth coordinate system; This represents the position vector of the virtual leader in the Earth's fixed coordinate system; This represents the position vector of each follower in a fixed Earth coordinate system. Indicates the offset of the formation; It represents three-dimensional Euclidean space.

[0048] S32. The estimator constructed based on Lyapunov stability and event-triggered control is as follows:

[0049] (5)

[0050] ,and Indicates the first The unmanned vessel in the first Position coordination error at any given time Indicates the first The unmanned vessel in the first The velocity coordination error at any given moment.

[0051] express t In the time formation The measurement error of each unmanned vessel is used to determine whether the triggering conditions are met.

[0052] The control parameters in the formula should meet the following conditions. This estimator can achieve convergence within a fixed timeframe that can be calculated in advance by adjusting the size of the control parameters. , , , Represents a symbolic function. , ; express t Time of the first i Status of unmanned surface vessel systems estimated value The first derivative, express t Time of the first i speed of unmanned vessels estimated value The first derivative, Indicates the first The first unmanned vessel A trigger moment, express t Time of the first The measurement error that determines whether an event is triggered by an unmanned vessel This represents a positive constant that can be adjusted. Indicates the first The unmanned vessel in the first Velocity estimate at time [time] Indicates the first The unmanned vessel in the first Velocity estimate at time [time] The virtual leader of the unmanned vessel is in the The velocity value at any given moment.

[0053] S33. For the above estimation algorithm, in order to reduce the local communication burden and the control cost caused by frequent controller updates, the corresponding trigger function is designed as follows:

[0054] (6)

[0055] in, express t Time of the first i Trigger threshold conditions and parameters for an unmanned surface vessel It is a normal number that can be adjusted; Indicates the first The measurement error that determines whether an event is triggered by an unmanned vessel Indicate decision t Time of the first A function to determine whether an unmanned vessel event is triggered.

[0056] S34. The following is a non-singular sliding surface with predetermined time stability, constructed based on Lyapunov stability and predetermined time stability theories:

[0057] (7)

[0058] In the above formula, Indicates the followers in the considered formation. , It is a positive constant and satisfies the relation , The convergence time of the sliding surface can be predefined. This represents an N-dimensional vector where all elements are 1s. Denotes the Hadamard product of two vectors. e i Indicates the first The tracking error of an unmanned vessel.

[0059] S35. Based on the estimator and predetermined time sliding mode surface designed above, a predetermined time hierarchical control algorithm is designed for networked unmanned surface vessel formations as follows:

[0060] (8)

[0061] (9)

[0062] in, The control parameters involved in the above algorithm need to meet the following requirements. , The time for the tracking error to converge is represented by K, and K represents the gain matrix. , δ , 1. 2. γ 2. These are parameters that need to be defined by the user. This represents the non-singular sliding surface in formula (7). Indicates the first The first derivative of the tracking error of an unmanned surface vessel 13 represents a three-dimensional vector where all elements are 1.

[0063] S36. Based on Lyapunov stability, fixed-time stability, and predetermined-time stability theories, the stability proof of the control algorithm designed above is as follows:

[0064] Based on the control scheme designed for the system, the positive definite Lyapunov function is selected as follows:

[0065] (10)

[0066] in, , , ; β、 γ It is a positive parameter. This represents the sum of the Laplacian matrix and the traction matrix of the topological graph. I N Represents an N-dimensional unit vector. y This indicates the positional coordination error of unmanned vessels. z This indicates the speed coordination error of unmanned vessels.

[0067] For Lyapunov functions Taking the derivative and combining it with the designed distributed estimation algorithm, we obtain the following results:

[0068] (11)

[0069] in, , Representing Lyapunov functions The first derivative, where k1, k2, and k are all positive constants, λ max Representation matrix The largest eigenvalue, where N represents the number of unmanned vessels in the formation.

[0070] Based on Lyapunov stability theory and fixed-time stability theory, the upper bound of the estimator's convergence time is derived as follows:

[0071] (12)

[0072] For Lyapunov functions Taking the derivative and combining it with the designed local control algorithm, under the premise of satisfying some necessary parameter conditions, the following results are obtained:

[0073] (13)

[0074] in, Lyapunov function The Dini derivative.

[0075] Based on Lyapunov stability theory and predetermined time stability theory, we can derive the controller convergence time, i.e., the formation time, at the local layer as follows:

[0076] (14)

[0077] in, These represent the convergence times of the sliding surface and the tracking error, respectively. Indicates a pair with The relevant very small positive numbers, if a smaller one is chosen , can make Since it is a negligible constant, the upper bound of the convergence time of the entire formation can be determined in advance, i.e. .

[0078] S4. An event-triggered distributed estimation algorithm is used to estimate the real-time state of the virtual leader and transmit it to each unmanned vessel in the local layer. Then, a local control algorithm based on predetermined time sliding mode control is used to adjust each unmanned vessel to form the designed formation within a predetermined time.

[0079] refer to Figure 5 , Figure 5 This is a tracking map of the estimated position and estimated velocity of the unmanned vessel in an embodiment of the present invention. Figure 5 It was observed that the estimated state and speed of the networked unmanned vessel under consideration were able to track the trajectory of the virtual leader within a specified time.

[0080] refer to Figure 6 , Figure 6 This is a diagram showing the estimated position and estimated velocity errors of an unmanned surface vessel in an embodiment of the present invention. Figure 6 It was observed that the estimated state and estimated velocity of the networked unmanned vessel under consideration converged to 0 within a predetermined time.

[0081] refer to Figure 7 , Figure 7 This is a tracking map of the actual position and speed of the unmanned vessel in this embodiment of the invention. Figure 7 It was observed that the networked unmanned vessels under consideration were able to track the trajectory of the virtual leader within a specified time.

[0082] refer to Figure 8 , Figure 8 This is a diagram showing the actual position and speed error of the unmanned vessel in this embodiment of the invention. Figure 8 It was observed that the state of the networked unmanned vessel under consideration converged to 0 within a specified time.

[0083] refer to Figure 9 , Figure 9 This is a graph showing the event trigger times for each ship under this estimation algorithm, which can be obtained through... Figure 9 It was observed that the networked unmanned vessels under consideration can achieve system stability with intermittent communication and control updates.

[0084] refer toFigure 10 , Figure 10 This is the formation diagram generated under this control algorithm, through... Figure 10 It was observed that the four unmanned vessels were able to form the desired formation within the predetermined time.

[0085] 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 method for timed tracking and control of a networked unmanned surface vessel formation, characterized in that, Includes the following steps: S1. Perform system kinematics and dynamics modeling on a formation containing N networked unmanned surface vessels. Set a virtual leader among the N unmanned vessels and set the rest as followers. S2. Based on the communication between the various unmanned vessels, design a directed communication topology graph to describe the communication of the virtual leader. S3. Based on the communication topology diagram and the constructed kinematic and dynamic models, construct the tracking error of the networked unmanned surface vessel formation system, and design a hierarchical control framework for the system, including an event-triggered distributed estimation algorithm and a local control algorithm based on predetermined time sliding mode control. S4. The event-triggered distributed estimation algorithm is used to estimate the real-time state of the virtual leader and transmit it to each unmanned vessel in the local layer. The local control algorithm based on the predetermined time sliding mode control is used to adjust each unmanned vessel to form the designed formation within a predetermined time. Step S3 is as follows: S31. Based on the communication topology diagram and the constructed kinematic and dynamic models, the tracking error of the networked unmanned surface vessel formation system is as follows: (4) in: and They represent the first The position tracking error and velocity tracking error of an unmanned vessel at time t in a fixed Earth coordinate system; This represents the position vector of the virtual leader in the Earth's fixed coordinate system; Indicates the first An unmanned surface vessel system at a fixed Earth coordinate The position vector in the middle, Indicates the offset of the formation; Represents three-dimensional Euclidean space; S32. The estimator constructed based on Lyapunov stability and event-triggered control is as follows: (5) in: ,and Indicates the first The unmanned vessel in the first Position coordination error at any given time Indicates the first The unmanned vessel in the first The velocity coordination error at any given moment; Indicates the first element in the formation at time t. The measurement error of an unmanned vessel is used to determine whether the triggering conditions are met. The control parameters in the formula should meet the following conditions. ;definition , , , Represents a symbolic function. , ; Indicates the state of the i-th unmanned surface vessel system estimated value The first derivative, Represents the speed of the i-th unmanned vessel. estimated value The first derivative, Indicates the first The first unmanned vessel A trigger moment, Represents the time t. The measurement error that determines whether an event is triggered by an unmanned vessel This represents a positive constant that can be adjusted. Indicates the first The unmanned vessel in the first Velocity estimate at time [time] Indicates the first The unmanned vessel in the first Velocity estimate at time [time] The virtual leader of the unmanned vessel is in the The velocity value at that moment; S33. Design the corresponding trigger function as follows: (6) in, The parameter represents the trigger threshold condition for the i-th unmanned vessel at time t. It is a normal number that can be adjusted; Indicates the first The measurement error that determines whether an event is triggered by an unmanned vessel Indicates the decision at time t. A function to determine whether an unmanned vessel event is triggered; S34. The following is a non-singular sliding surface with predetermined time stability, constructed based on Lyapunov stability and predetermined time stability theories: （7） In the above formula, Indicates a follower in a formation. , , It is a positive constant and satisfies the relation , It is the predefined sliding surface convergence time. This represents an N-dimensional vector where all elements are 1s. Let e ​​represent the Hadamard product of two vectors. i Indicates the first The tracking error of an unmanned vessel; S35. The following time-based hierarchical control algorithm is designed for networked unmanned surface vessel formations: （8） （9） in, The control parameters involved in the above algorithm need to meet the following requirements. , The time for the tracking error to converge is represented by K, and K represents the gain matrix. ,δ、 γ2, These are parameters that need to be defined by the user. This represents the non-singular sliding surface in formula (7). Indicates the first The first derivative of the tracking error of an unmanned surface vessel , 13 represents a three-dimensional vector in which all elements are 1; S36. Based on Lyapunov stability, fixed-time stability, and predetermined-time stability theories, the stability proof of the control algorithm designed above is as follows: Based on the control scheme designed for the system, the positive definite Lyapunov function is selected as follows: (10) in, , , β and γ are positive parameters. I represents the sum of the Laplacian matrix and the traction matrix of the topological graph. N Let represent an N-dimensional unit vector, y represent the position coordination error of the unmanned vessel, and z represent the velocity coordination error of the unmanned vessel. For Lyapunov functions Taking the derivative and combining it with the designed distributed estimation algorithm, we obtain the following results: （11） in, , Representing Lyapunov functions The first derivative, where k1, k2, and k are all positive constants, λ max Representation matrix The largest eigenvalue, where N represents the number of unmanned vessels in the formation; Based on Lyapunov stability theory and fixed-time stability theory, the upper bound of the estimator's convergence time is derived as follows: （12） For Lyapunov functions Taking the derivative and combining it with the designed local control algorithm, under the premise of satisfying some necessary parameter conditions, the following results are obtained: （13） in, Lyapunov function The Dini derivative; Based on Lyapunov stability theory and predetermined time stability theory, the controller convergence time, i.e. the formation time, is derived at the local layer as follows: （14） in, Indicates a pair with The relevant positive constants, if a smaller one is chosen , It is a negligible constant, and the upper bound of the convergence time of the entire formation can be determined in advance, i.e. .

2. The method for pre-time tracking and control of a networked unmanned surface vessel formation according to claim 1, characterized in that, Step S1 is as follows: S11, For the first in the formation The kinematic and dynamic models required for the unmanned surface vessel system are constructed as follows: （1） in: , , Let these represent the inertia matrix, Coriolis and centripetal matrix, and hydrodynamic damping matrix, respectively. Represents the rotation transformation matrix. Indicates the first The rotation angle of an unmanned surface vessel system in the Earth coordinate system Indicates the first An unmanned surface vessel system at a fixed Earth coordinate The position vector in the middle, express The first derivative, Indicates the first The coordinates of the unmanned surface vessel system within the entire formation system The velocity vector in express exist directional components, express exist y directional components, express The rotational component, express The first derivative, and They represent the first Control inputs and external disturbances of an unmanned surface vessel system; Indicates control input exist directional components, Indicates control input exist y directional components, Indicates control input The rotational component, Indicates external disturbance exist Components of direction Indicates external disturbance exist Components of direction Indicates external disturbance The rotational component; S12. According to the coordinate transformation formula, formula (1) is transformed into the following form: （2） in: , ; for The first derivative; S13. The model of the virtual leader in the Earth's fixed coordinate system is established as follows: （3） in, , , These represent the virtual leader's position, velocity, and acceleration vector in the Earth's fixed coordinate system, respectively. express The first derivative, express The first derivative.

3. The method for pre-time tracking and control of a networked unmanned surface vessel formation according to claim 2, characterized in that, Step S2 is as follows: S21. A directed communication topology graph containing N nodes. ,in , Figures are respectively The set of points and the set of edges, using Representation diagram The adjacency matrix and when From time to time This indicates unmanned vessels. Capable of receiving unmanned vessels Information, when hour ; Representation diagram The Laplace matrix; where, Represents the Laplace matrix An element in the set, and that element satisfies , ; express VIE space; S22, Define the traction matrix Describe the communication between the virtual leader and followers, if , indicating the first Each unmanned vessel can directly receive information from the virtual leader, and vice versa. Then it means the first Unmanned vessels cannot directly receive information from the virtual leader.

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