Finite time self-adaptive adjustment multi-unmanned ship formation consistency control method and system

Abstract

The invention relates to the technical field of unmanned ship formation consistency control, in particular to a finite time self-adaptive adjustment multi-unmanned ship formation consistency control method and system, and the method comprises the steps: defining auxiliary variables according to a dynamic equation, nonlinear dynamic characteristics and external disturbance of each unmanned ship; constructing a finite time consistency control law and an adaptive law according to the auxiliary variables and conditions for realizing bounded consistency of the multi-unmanned ship system; determining a closed-loop error controller according to the position error, the speed error, the finite time consistency control law and the adaptive law; constructing a Lyapunov function according to the auxiliary variable and a closed-loop error controller; the error stability verification of the position and the speed of each unmanned ship is realized through a Lyapunov function; and based on a finite time consistency control law, multi-unmanned ship formation consistency control is realized. The problems of disturbance compensation with more general state related disturbance and finite time formation consistency control are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of formation consensus control for unmanned surface vehicles, and particularly to a formation consensus control method and system for multiple unmanned surface vehicles with finite-time adaptive adjustment. Background Art

[0002] In recent years, the formation consensus problem in multi-unmanned surface vehicle systems has been one of the research hotspots, and has attracted much attention due to its wide application prospects in different non-linear dynamic systems. In complex working environments, these non-linear dynamic systems are often affected by some non-ideal factors, such as strong uncertainties, external disturbances, non-linear couplings, etc., which makes it extremely difficult to achieve formation consensus control of multi-unmanned surface vehicle systems.

[0003] As is well known, finite-time control is one of the most effective control methods for achieving high-precision tracking, fast convergence, and resilience to unpredictable variables in dynamic systems. To address the impact of these non-ideal factors on the consensus problem, numerous scholars have developed a variety of effective methods. These methods include control strategies based on neural networks, observer-based control strategies, and sliding mode control strategies, etc. However, these traditional advanced control algorithms require imposing constraint conditions on the differentiability of disturbances and non-linearities, and require adjusting a large number of parameters, which undoubtedly increases computational resources. It is found that in order to ensure finite-time consensus while reducing constraint conditions and computational burdens, it is necessary to further develop more effective non-linear and disturbance compensation control methods. Summary of the Invention

[0004] To solve the deficiencies of the prior art, the present invention provides a formation consensus control method and system for multiple unmanned surface vehicles with finite-time adaptive adjustment; the present invention develops a new adaptive distributed consensus control scheme for multi-unmanned surface vehicle systems, and solves the problems of disturbance compensation and finite-time formation consensus control with more general state-dependent disturbances.

[0005] On the one hand, a formation consensus control method for multiple unmanned surface vehicles with finite-time adaptive adjustment is provided, including: According to the dynamic characteristics of the multi-unmanned surface vehicle system, establish the dynamic equation of each unmanned surface vehicle, and limit the boundaries of the non-linear dynamic characteristics and external disturbances of each unmanned surface vehicle by assumption; Define the position error and velocity error of each unmanned surface vehicle, and define the conditions for the multi-unmanned surface vehicle system to achieve bounded consensus; Define auxiliary variables according to the dynamic equation, non-linear dynamic characteristics, and external disturbances of each unmanned surface vehicle; construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-unmanned surface vehicle system to achieve bounded consensus; Determine a closed-loop error controller according to the position error, velocity error, finite-time consensus control law, and adaptive law; Construct a Lyapunov function based on the auxiliary variable and the closed-loop error controller; verify the error stability of the position and velocity of each unmanned boat through the Lyapunov function; Based on the finite-time consensus control law, achieve the multi-unmanned-boat formation consensus control.

[0006] On the other hand, a multi-unmanned-boat formation consensus control system with finite-time adaptive regulation is provided, including: A building module, which is configured to: establish the dynamic equation of each unmanned boat according to the dynamic characteristics of the multi-unmanned-boat system, and limit the boundaries of the nonlinear dynamic characteristics and external disturbances of each unmanned boat through assumptions; A definition module, which is configured to: define the position error and velocity error of each unmanned boat, and define the conditions for the multi-unmanned-boat system to achieve bounded consensus; A construction module, which is configured to: define auxiliary variables according to the dynamic equation, nonlinear dynamic characteristics and external disturbances of each unmanned boat; construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-unmanned-boat system to achieve bounded consensus; A determination module, which is configured to: determine a closed-loop error controller according to the position error, velocity error, finite-time consensus control law and adaptive law; A verification module, which is configured to: construct a Lyapunov function according to the auxiliary variable and the closed-loop error controller; verify the error stability of the position and velocity of each unmanned boat through the Lyapunov function; A control module, which is configured to: achieve the multi-unmanned-boat formation consensus control based on the finite-time consensus control law.

[0007] The above technical solutions have the following advantages or beneficial effects: (1) Compared with the traditional method, the present invention significantly reduces the consumption of computing resources and improves the real-time control performance by introducing a finite-time adaptive compensation mechanism; (2) The present invention only needs to assume that the disturbance satisfies partial boundedness. Compared with the traditional finite-time control method, the present invention does not require high-order differentiability or strict norm boundedness conditions, expanding the application scope of the method.

[0008] (3) The finite-time consensus control scheme designed by the present invention can effectively achieve the finite-time consensus of the multi-unmanned-boat system under nonlinear dynamics and external disturbances, and enable the multi-unmanned-boat system to reach a consistent state within a predetermined time, improving the control efficiency and response speed.

[0009] (4) Through the designed finite-time control function, the multi-unmanned surface vehicle system can achieve rapid convergence of the consensus error within a predetermined time. Through the adaptive compensation term and the parameter update law, the system can online estimate and cancel unknown non-linear dynamics and external disturbances when the disturbances are state-dependent and without prior knowledge of the boundaries. Compared with the existing neural network methods, the control law structure of the present invention is simple and significantly reduces the computational burden. In addition, the present invention only needs to meet partial bounded conditions and can be extended to various second-order multi-agent systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0011] Figure 1 It is a flowchart of the method of the present invention.

[0012] Figure 2 It represents the planar motion diagram of the unmanned surface vehicle.

[0013] Figure 3 It represents the motion topology diagram of the unmanned surface vehicle; wherein, Figure 3 The numbers 1, 2, 3, 4, and 5 respectively represent the numbers of five unmanned surface vehicles.

[0014] Figure 4 It represents the schematic diagram of the lateral position trajectory and the reference lateral position trajectory of each unmanned surface vehicle.

[0015] Figure 5 It represents the schematic diagram of the longitudinal position trajectory and the reference longitudinal position trajectory of each unmanned surface vehicle.

[0016] Figure 6 It represents the schematic diagram of the heading angle trajectory and the reference heading angle trajectory of each unmanned surface vehicle. Figure 7 It represents the schematic diagram of the lateral velocity trajectory and the reference lateral velocity trajectory of each unmanned surface vehicle.

[0017] Figure 8 It represents the schematic diagram of the longitudinal velocity trajectory and the reference longitudinal velocity trajectory of each unmanned surface vehicle.

[0018] Figure 9 It represents the schematic diagram of the yaw angular velocity trajectory and the reference yaw angular velocity trajectory of each unmanned surface vehicle. Figure 10 It represents the trajectory tracking error diagram of the first unmanned surface vehicle.

[0019] Figure 11 It represents the trajectory tracking error diagram of the second unmanned surface vehicle.

[0020] Figure 12Shows the trajectory tracking error graph of the 3rd unmanned boat.

[0021] Figure 13 Shows the trajectory tracking error graph of the 4th unmanned boat.

[0022] Figure 14 Shows the trajectory tracking error graph of the 5th unmanned boat.

[0023] Figure 15 Shows the speed tracking error graph of the 1st unmanned boat.

[0024] Figure 16 Shows the speed tracking error graph of the 2nd unmanned boat.

[0025] Figure 17 Shows the speed tracking error graph of the 3rd unmanned boat.

[0026] Figure 18 Shows the speed tracking error graph of the 4th unmanned boat.

[0027] Figure 19 Shows the speed tracking error graph of the 5th unmanned boat.

[0028] Figure 20 Shows the control input response graph of the 1st unmanned boat.

[0029] Figure 21 Shows the control input response graph of the 2nd unmanned boat.

[0030] Figure 22 Shows the control input response graph of the 3rd unmanned boat.

[0031] Figure 23 Shows the control input response graph of the 4th unmanned boat.

[0032] Figure 24 Shows the control input response graph of the 5th unmanned boat.

[0033] Figure 25 Shows the disturbance estimation parameter adaptive regulation graph of the 1st unmanned boat.

[0034] Figure 26 Shows the disturbance estimation parameter adaptive regulation graph of the 2nd unmanned boat.

[0035] Figure 27 Shows the disturbance estimation parameter adaptive regulation graph of the 3rd unmanned boat.

[0036] Figure 28 Shows the disturbance estimation parameter adaptive regulation graph of the 4th unmanned boat.

[0037] Figure 29 Shows the disturbance estimation parameter adaptive regulation graph of the 5th unmanned boat. Detailed implementation manners

[0038] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0039] Term explanation: 1. An unmanned boat system is an unmanned surface ship system, usually composed of a platform system, a mission payload system, a communication system, a navigation system, a control system, etc. It can complete various specified tasks through autonomous navigation or remote control. The multi-unmanned boat system consists of multiple unmanned boats and cooperates through specific technologies and methods to achieve the mission objectives.

[0040] 2. Finite-time control refers to designing a control law to make the system state converge to the desired state within a finite time or meet the specified control objectives. It has the advantages of fast convergence speed and strong anti-interference ability, and is often used in control scenarios with strict requirements for response time.

[0041] 3. Formation consensus control means that in a multi-unmanned boat system, through local information interaction and control strategies, a certain state variable (such as position, speed, etc.) of all unmanned boats tends to be consistent, which is a key method to achieve cooperative control.

[0042] 4. Adaptive control refers to online estimating and adjusting the system control parameters so that the control system can adapt to changes such as internal dynamics or external disturbances of the system.

[0043] 5. Anti-interference control is a control strategy that designs a control strategy to compensate for the non-linear dynamics for unknown non-linear dynamics, including external disturbances and internal uncertainties of the system, so that the control system can still maintain good performance and stability in the presence of external disturbances.

[0044] To solve the deficiencies of the prior art, the present invention develops a new adaptive finite-time formation consensus control scheme for a multi-unmanned boat system. First, a finite-time control function is used to ensure that the system state error converges within a finite time. Then, an adaptive law is used to dynamically compensate for system disturbances, reducing the prior assumption requirements for the disturbance model. Finally, the adaptive compensation term and the control function are combined to obtain the control input, ensuring that the system reaches consensus within a finite time, with low computational complexity and strong robustness, and achieving fast and high-precision consensus of the multi-unmanned boat system under more general disturbance assumptions, overcoming the strong constraint problems of the prior methods for non-linear terms and disturbances.

[0045] Embodiment 1 This embodiment provides a multi-unmanned surface vehicle (USV) formation consensus control method with finite-time adaptive adjustment; As Figure 1 shown, the multi-USV formation consensus control method with finite-time adaptive adjustment includes: S100: According to the dynamic characteristics of the multi-USV system, establish the dynamic equation of each USV, and limit the boundaries of the nonlinear dynamic characteristics and external disturbances of each USV through assumptions; S200: Define the position error and velocity error of each USV, and define the conditions for the multi-USV system to achieve bounded consensus; S300: Define auxiliary variables according to the dynamic equation, nonlinear dynamic characteristics, and external disturbances of each USV; construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-USV system to achieve bounded consensus; S400: Determine the closed-loop error controller according to the position error, velocity error, finite-time consensus control law, and adaptive law; S500: Construct a Lyapunov function according to the auxiliary variables and the closed-loop error controller; verify the error stability of the position and velocity of each USV through the Lyapunov function; S600: Based on the finite-time consensus control law, achieve multi-USV formation consensus control.

[0046] Among them, the multi-USV system includes: multiple USVs, and each USV is controlled by a console.

[0047] The beneficial effects of the above technical solution are: In order to achieve finite-time formation consensus control of the multi-USV system under nonlinear dynamics and external disturbances, an adaptive disturbance estimation and safety controller are established through system error and auxiliary variable design, enabling the USVs to navigate and track a preset target trajectory and maintain formation consensus.

[0048] Further, the S100: According to the dynamic characteristics of the multi-USV system, establish the dynamic equation of each USV, including: S101: The structure of the USV and the formation topology diagram are as Figure 2 and Figure 3 shown. Set the ground coordinate system , define and as the position states in the directions of the axis and axis of the USV respectively, and is the yaw angle of the USV. Correspondingly, define as the axis and The velocity state in the axial direction and the yaw angular velocity. According to the dynamic principle of the multi-unmanned surface vehicle system, the dynamic equation of the -th unmanned surface vehicle can be expressed as: where represents the control input of each unmanned surface vehicle, represents the non-linear dynamics of each unmanned surface vehicle, , represents the unknown external disturbance of each unmanned surface vehicle. Obviously, and respectively represent the position and velocity vectors of the -th unmanned surface vehicle; represents the inertia matrix of the unmanned surface vehicle; and respectively represent the Coriolis and centrifugal matrix and the hydrodynamic damping parameter matrix; represents the unknown external disturbance, where represents the disturbance received by the unmanned surface vehicle in the longitudinal direction, represents the disturbance received by the unmanned surface vehicle in the lateral direction, represents the disturbance received by the unmanned surface vehicle in the yaw direction, in , , are respectively the surge, sway and yaw forces provided by the actuator; represents the rotation matrix used to convert variables between the earth-fixed inertial coordinate system and the body-fixed coordinate system, defined as follows: .

[0049] The beneficial effects of the above technical solution are: Each unmanned surface vehicle is modeled as an Euler-Lagrange type second-order non-linear dynamic model including an inertia matrix, a Coriolis and centrifugal matrix, a hydrodynamic damping parameter matrix and an external disturbance term. Compared with the traditional first-order integrator modeling, this model can more accurately describe the dynamic response characteristics of the unmanned surface vehicle in complex sea conditions. The control system constructed based on this model can more effectively introduce a disturbance compensation mechanism and an adaptive estimation structure, and further achieve the stable consistency control goal of the system within a finite time, with stronger control accuracy, response speed and robustness. At the same time, this modeling method has good versatility and engineering deployment value, and can be widely adapted to various types of unmanned surface vehicle platforms.

[0050] Furthermore, the S100: By assuming to limit the boundaries of the non-linear dynamic characteristics and external disturbances of each unmanned surface vehicle, specifically including: S102: The non-linear dynamic characteristics of the -th unmanned surface vehicle and the disturbance The following conditions should be satisfied: ; and ; wherein, is a positive but unknown constant.

[0051] The beneficial effects of the above technical solution are as follows: By making bounded assumptions about the nonlinear dynamic characteristics of each unmanned boat and the external disturbance term, and uniformly representing them as a weighted combination of state-related terms and constant terms, and introducing three unknown boundary parameters. This method simplifies the disturbance modeling, facilitates the controller design and stability analysis. Combining with the adaptive estimation mechanism, the online identification of the disturbance upper bound can be realized without prior knowledge, enhancing the robustness and practicability of the system, and providing a theoretical support for the finite-time consensus control.

[0052] Furthermore, in S200: Define the position error and velocity error of each unmanned boat, specifically including: S201: Define the position and velocity errors of the th unmanned boat , , wherein and are respectively the desired reference position and velocity signals of the th unmanned boat, satisfying: ; wherein, is the reference input.

[0053] Furthermore, in S200: Define the conditions for the multi-unmanned boat system to achieve bounded consensus, specifically including: S202: Define the conditions for the multi-unmanned boat system to achieve bounded consensus. For any initial values and , if the error satisfies: ; wherein, , , is defined as: ; and is a positive constant, then it is said that the multi-unmanned boat system has achieved bounded consensus.

[0054] The beneficial effects of the above technical solution are as follows: Based on the dynamic characteristics of the multi-unmanned boat system, S200 defines the error variables and reference signal models for each unmanned boat, introduces the concept of bounded consensus, and ensures that after appropriate linear transformation, the error signal is restricted within a small range when time approaches a certain limit.

[0055] Further, the S300: According to the dynamic equations, non-linear dynamic characteristics and external disturbances of each unmanned boat, auxiliary variables are defined, specifically including: S301: Define auxiliary variables ; Among them, , , is the number of unmanned boats in the formation, is the topological factor, indicating the connection state between unmanned boat and . represents connection, represents disconnection.

[0056] Further, the S300: According to the auxiliary variables and the conditions for achieving bounded consensus of the multi-unmanned boat system, a finite-time consensus control law and an adaptive law are constructed, specifically including: S302: Design the finite-time consensus control law according to the auxiliary variables as follows: ; Among them, ; is the finite-time control function: ; Among them, , ; is the maximum eigenvalue of the matrix , , , is a constant, and its adaptive law satisfies the following formula: ; And: ; And: ; Among them, is a positive constant.

[0057] The beneficial effects of the above technical solution are as follows: An auxiliary variable is defined according to the non-linear dynamics and external disturbance characteristics of multiple unmanned boats to enhance the consistency of the system; and a finite-time control function is designed based on the auxiliary variable and the control objective to ensure that the system reaches consistency within a finite time. Based on the auxiliary variable and the finite-time control function, a distributed finite-time consensus control law is constructed to achieve fast state tracking of the unmanned boats.

[0058] Further, in S400: According to the position error, velocity error, finite-time consensus control law, and adaptive law, a closed-loop error controller is determined, including: S401: Substitute the finite-time consensus control law into the state equation of each unmanned boat to obtain: ; Then, combine the desired reference position and velocity signals of the unmanned boat with the state equation of each unmanned boat to further obtain the error system equation as: ; Where: , , , , , where, .

[0059] S402: According to the auxiliary variable, it can be obtained that: ; S403: Combine the error system equation and the time derivative of the auxiliary variable to obtain the following closed-loop error system: ; Where, , , .

[0060] The beneficial effects of the above technical solution are as follows: A closed-loop error system is constructed based on the position error and velocity error, and combined with the finite-time consensus control law and the adaptive disturbance estimation mechanism to achieve fast consensus tracking control under unknown disturbance conditions. This system has the characteristics of closed structure, fast response, strong robustness, etc., and is suitable for the engineering application of multi-unmanned boat formation tasks.

[0061] Further, in S500: According to the auxiliary variable and the closed-loop error controller, a Lyapunov function is constructed; the error stability of the position and velocity of each unmanned boat is verified through the Lyapunov function, including: S501: For the auxiliary variable and error signal of the multi-unmanned boat system, construct a Lyapunov function as follows: ; Among them, , , ; S502: When , taking the time derivative of the Lyapunov function gives: ; Among them, , represents the matrix element defined by .

[0062] S503: Based on the assumptions of the non - linear dynamic characteristics of each unmanned boat and external disturbances and obtain:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] ; S504: According to , and combined with the adaptive law, obtain: ; S505: According to the finite - time stability theorem, the error states , and converge to a region near zero within a finite time . Also, because the auxiliary variable satisfies , where .

[0069] In addition, from the definition of the auxiliary variable, it can be known that the auxiliary variable is expressed as: ; From the above, obtain: ; S506: Let ; Because is a bounded signal, so there exist parameters Such that Thus, the following relationships are obtained: ; ; Then, further obtain: ; This proves that the position and velocity errors of each unmanned boat in the multi-unmanned boat system will be within , and where .

[0070] The beneficial effects of the above technical solution are: Based on the auxiliary variable and the closed-loop error system, a unified Lyapunov function is constructed to analyze the dynamic behavior of the position and velocity errors of the system under external disturbances and model uncertainties. Through this function, it can be strictly proved that the system error converges within a finite time, ensuring the stability and convergence speed of the control system in a complex environment.

[0071] Furthermore, S600: Use the finite-time consensus control law as the finite-time consensus controller, and based on the finite-time consensus controller, achieve multi-unmanned boat formation consensus control.

[0072] Furthermore, the method further includes: performing actual operation through the computer simulation software MATLAB to verify the effectiveness of the method and system of the present invention.

[0073] In this example, the computer software MATLAB is used to select five unmanned boats to verify the invented control method. The parameters related to each step in the simulation are selected as follows: In S100, the parameters of the inertia matrix and the Coriolis and centrifugal matrix of the unmanned boat are selected as: , .

[0074] Among them: , , The hydrodynamic damping parameter matrix is selected as: .

[0075] Among them: , , .

[0076] In the simulation, the multi-unmanned surface vehicle (USV) system consists of five USVs, and its topological structure is described by the following Laplacian matrix: .

[0077] In S200, the reference input signal is selected as: .

[0078] The external disturbance is selected as: .

[0079] Where, .

[0080] In S300, the parameters of the finite-time consensus controller are selected as: , , , , , , , Where, .

[0081] The parameters of the adaptive scheme are selected as: , Where, , .

[0082] The initial values of the adaptation law are selected as: , Where, , .

[0083] Result description: Figure 4 It shows the schematic diagram of the lateral position trajectory of each USV and the reference lateral position trajectory. Figure 5 It shows the schematic diagram of the longitudinal position trajectory of each USV and the reference longitudinal position trajectory. Figure 6 It shows the schematic diagram of the heading angle trajectory of each USV and the reference heading angle trajectory. Figure 7 It shows the schematic diagram of the lateral velocity trajectory of each USV and the reference lateral velocity trajectory. Figure 8 It shows the schematic diagram of the longitudinal velocity trajectory of each USV and the reference longitudinal velocity trajectory. Figure 9 It shows the schematic diagram of the yaw angular velocity trajectory of each USV and the reference yaw angular velocity trajectory. Figure 10Shows the trajectory tracking error graph of the first unmanned boat. Figure 11 Shows the trajectory tracking error graph of the second unmanned boat. Figure 12 Shows the trajectory tracking error graph of the third unmanned boat. Figure 13 Shows the trajectory tracking error graph of the fourth unmanned boat. Figure 14 Shows the trajectory tracking error graph of the fifth unmanned boat.

[0084] Figure 15 Shows the speed tracking error graph of the first unmanned boat. Figure 16 Shows the speed tracking error graph of the second unmanned boat. Figure 17 Shows the speed tracking error graph of the third unmanned boat. Figure 18 Shows the speed tracking error graph of the fourth unmanned boat. Figure 19 Shows the speed tracking error graph of the fifth unmanned boat.

[0085] Figure 20 Shows the control input response graph of the first unmanned boat. Figure 21 Shows the control input response graph of the second unmanned boat. Figure 22 Shows the control input response graph of the third unmanned boat. Figure 23 Shows the control input response graph of the fourth unmanned boat. Figure 24 Shows the control input response graph of the fifth unmanned boat.

[0086] Figure 25 Shows the disturbance estimation parameter adaptive regulation graph of the first unmanned boat. Figure 26 Shows the disturbance estimation parameter adaptive regulation graph of the second unmanned boat. Figure 27 Shows the disturbance estimation parameter adaptive regulation graph of the third unmanned boat. Figure 28 Shows the disturbance estimation parameter adaptive regulation graph of the fourth unmanned boat. Figure 29 Shows the disturbance estimation parameter adaptive regulation graph of the fifth unmanned boat.

[0087] Embodiment II This embodiment provides a multi-unmanned boat formation consensus control system with finite-time adaptive regulation; The multi-unmanned boat formation consensus control system with finite-time adaptive regulation includes: A building module configured to: establish the dynamic equation of each unmanned boat according to the dynamic characteristics of the multi-unmanned boat system, and limit the boundaries of the nonlinear dynamic characteristics and external disturbances of each unmanned boat by assumption; A defining module configured to: define the position error and speed error of each unmanned boat, and define the conditions for the multi-unmanned boat system to achieve bounded consensus; A construction module, configured to: define auxiliary variables according to the dynamic equations, non-linear dynamic characteristics and external disturbances of each unmanned boat; construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for achieving bounded consensus of the multi-unmanned boat system; A determination module, configured to: determine a closed-loop error controller according to the position error, velocity error, finite-time consensus control law and adaptive law; A verification module, configured to: construct a Lyapunov function according to the auxiliary variables and the closed-loop error controller; verify the error stability of the position and velocity of each unmanned boat through the Lyapunov function; A control module, configured to: achieve multi-unmanned boat formation consensus control based on the finite-time consensus control law.

[0088] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-unmanned surface vehicle formation consensus control method with finite-time adaptive adjustment, characterized in that including: Based on the dynamic characteristics of the multi-unmanned boat system, establish the dynamic equation of each unmanned boat, and limit the boundaries of the non-linear dynamic characteristics and external disturbances of each unmanned boat through assumptions; Define the position error and velocity error of each unmanned boat, and define the conditions for the multi-unmanned boat system to achieve bounded consensus; Define auxiliary variables according to the dynamic equation, non-linear dynamic characteristics and external disturbances of each unmanned boat; Construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-unmanned boat system to achieve bounded consensus; Determine the closed-loop error controller according to the position error, velocity error, finite-time consensus control law and adaptive law; Construct a Lyapunov function according to the auxiliary variables and the closed-loop error controller; verify the error stability of the position and velocity of each unmanned boat through the Lyapunov function; Based on the finite-time consensus control law, achieve the formation consensus control of the multi-unmanned boat.

2. The multi-unmanned surface vehicle formation consensus control method with finite-time adaptive regulation as claimed in claim 1, wherein Based on the dynamic characteristics of the multi-unmanned boat system, establish the dynamic equation of each unmanned boat, including: Set the ground coordinate system , define and as the position states of the unmanned boat axis and axis directions respectively, and is the yaw angle of the unmanned boat; correspondingly, define as the velocity states of the unmanned boat axis and axis directions, as well as the yaw angular velocity; according to the dynamic principle of the multi-unmanned boat system, the dynamic equation of the th unmanned boat can be expressed as: ； Among them, represents the control input of each unmanned boat, represents the nonlinear dynamics of each unmanned boat, , represents the unknown external disturbance of each unmanned boat. Obviously, and respectively represent the position and velocity vectors of the th unmanned boat; represents the inertia matrix of the unmanned boat; and respectively represent the Coriolis and centrifugal matrix and the hydrodynamic damping parameter matrix; represents the unknown external disturbance, where represents the disturbance suffered by the unmanned boat in the longitudinal direction, represents the disturbance suffered by the unmanned boat in the lateral direction, represents the disturbance suffered by the unmanned boat in the yaw direction, In , , are respectively the surge, sway and yaw forces provided by the actuator; represents the rotation matrix used to transform variables between the earth-fixed inertial coordinate system and the body-fixed coordinate system, defined as follows: 。 3. The multi-unmanned boat formation consensus control method with finite-time adaptive adjustment as described in claim 2, characterized in that, Limit the boundaries of the non-linear dynamic characteristics and external disturbances of each unmanned boat through assumptions, specifically including: The nonlinear dynamic characteristics of the unmanned surface vehicle and disturbances shall satisfy the following conditions: ； and ； wherein, is a positive but unknown constant.

4. The multi-unmanned surface vehicle formation consensus control method with finite-time adaptive regulation as described in claim 3, characterized in that it is defined The position error and velocity error of each unmanned boat, specifically including: Define the position and velocity errors of the -th unmanned surface vehicle, where , and are the desired reference position and velocity signals of the -th unmanned surface vehicle, respectively, and satisfy: ； Among them, is the reference input.

5. The finite-time adaptive regulation multi-unmanned surface vehicle formation consensus control method according to claim 4, characterized in that, Define the conditions for the multi-unmanned boat system to achieve bounded consensus, specifically including: Define the conditions for the multi-unmanned boat system to achieve bounded consensus for any initial value and , if the error satisfies: ； Among them, , , is defined as: ； and If \(c\) is a positive constant, then it is said that the multi-unmanned boat system achieves bounded consensus.

6. The finite-time adaptive regulation multi-unmanned surface vehicle formation consensus control method according to claim 5, characterized in that, Define auxiliary variables according to the dynamic equation, non-linear dynamic characteristics and external disturbances of each unmanned boat, specifically including: Define auxiliary variables ； Among them, , , is the number of unmanned boats in the formation, is the topological factor, indicating the and connection state between unmanned boats, indicates connection, indicates disconnection.

7. The finite-time adaptive regulation multi-unmanned surface vehicle formation consensus control method according to claim 6, characterized in that Construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-unmanned boat system to achieve bounded consensus, specifically including: Design the finite-time consensus control law according to the auxiliary variables as follows: ； Among them, ; is a finite-time control function: ； Among them, , ; is the largest eigenvalue of the matrix , , , is a constant.

8. The finite-time adaptive regulation multi-unmanned surface vehicle formation consensus control method according to claim 7, characterized in that The adaptive law satisfies the following formula: ； and: ； and: ； wherein, is a positive constant.

9. The multi-unmanned surface vehicle formation consensus control method with finite-time adaptive regulation according to claim 8, characterized in that, Determine the closed-loop error controller according to the position error, velocity error, finite-time consensus control law and adaptive law, including: Substitute the finite-time consensus control law into the state equation of each unmanned boat to obtain: ； Then, combine the desired reference position and velocity signals of the unmanned boat and the state equation of each unmanned boat to further obtain the error system equation as: ； Wherein: , , , , , wherein, ; According to the auxiliary variables, it can be obtained that: ； Combining the error system equation and the time derivative of the auxiliary variables, the following closed-loop error system is obtained: ； Among them, , , .

10. A multi-unmanned boat formation consensus control system with finite-time adaptive regulation, characterized in that, including: A building module, which is configured to: based on the dynamic characteristics of the multi-unmanned boat system, establish the dynamic equation of each unmanned boat, and limit the boundaries of the non-linear dynamic characteristics and external disturbances of each unmanned boat through assumptions; A defining module, which is configured to: define the position error and velocity error of each unmanned boat, and define the conditions for the multi-unmanned boat system to achieve bounded consensus; A constructing module, which is configured to: define auxiliary variables according to the dynamic equation, non-linear dynamic characteristics and external disturbances of each unmanned boat; construct a finite-time consensus control law and an adaptive law according to the auxiliary variables and the conditions for the multi-unmanned boat system to achieve bounded consensus; A determining module, which is configured to: determine the closed-loop error controller according to the position error, velocity error, finite-time consensus control law and adaptive law; A verification module, which is configured to: construct a Lyapunov function according to the auxiliary variable and the closed-loop error controller; verify the error stability of the position and velocity of each unmanned boat through the Lyapunov function. A control module, which is configured to: achieve the formation consistency control of multiple unmanned boats based on the finite-time consensus control law.

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