Underactuated intelligent ship adaptive formation control method based on distributed line-of-sight guidance

Abstract

The invention provides an underactuated intelligent ship adaptive formation control method based on distributed sight guidance, and the method comprises the following steps: S1, obtaining the reference state information of a neighbor underactuated intelligent ship, and generating a reference path and a cooperation speed based on the reference state information of the neighbor underactuated intelligent ship through employing an expansion second-order consistency algorithm; s2, based on the reference path and the cooperative speed, adopting a distributed sight guidance method to obtain a reference course and a reference speed; s3, inputting the reference course into a bow turning torque controller to obtain bow turning torque control input; s4, inputting the reference speed into a propulsive force controller to obtain propulsive force control input; and S5, inputting the bow turning torque control input and the propulsive force control input into the mathematical model of the under-actuated intelligent ship, and controlling the mathematical model of the under-actuated intelligent ship. According to the invention, autonomous formation control of multiple intelligent ships can be realized, and the method has a wide application prospect in the aspects of maritime search and rescue, maritime resource exploration, maritime patrol, maritime environment monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous control of intelligent ships, and in particular to an adaptive formation control method for underactuated intelligent ships based on distributed line-of-sight guidance. Background Art

[0002] Underactuated smart ship formation control refers to controlling underactuated smart ships to sail in a specific formation and complete collaborative operations.

[0003] Existing underactuated smart ship formation control methods often design a parameterized reference path for each underactuated smart ship and achieve formation control by making parameters consistent, which increases the design cost.

[0004] There are often three problems with the distributed formation control of underactuated smart ships. First, an underactuated smart ship refers to a ship equipped with a rudder and a propeller but not a thruster, and the formation motion of an underactuated smart ship generally includes three degrees of freedom: lateral, longitudinal, and bow. This makes the dimension of the control input smaller than the dimension of the control output, which poses a challenge to the formation control of underactuated smart ships. Secondly, introducing distributed strategies into complex control systems will increase the complexity of the design of the distributed formation controller for underactuated smart ships. Finally, smart ships are easily affected by environmental interference and have model uncertainty, which poses a challenge to the model-based distributed formation controller for underactuated ships. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose an adaptive formation control method for under-actuated intelligent ships based on distributed line-of-sight guidance. Firstly, the line-of-sight guidance method is introduced to reduce the dimension of the output space of the under-actuated intelligent ships. Secondly, the design of the formation controller of the under-actuated intelligent ships is simplified by introducing a distributed strategy into the guidance system. Finally, the fuzzy logic system is used to approximate the nonlinear function.

[0006] The technical means adopted by the present invention are as follows:

[0007] An adaptive formation control method for underactuated intelligent ships based on distributed line-of-sight guidance comprises the following steps:

[0008] S1. Obtain reference state information of neighboring underactuated smart ships, and generate reference paths and coordinated speeds using an extended second-order consistency algorithm based on the reference state information of neighboring underactuated smart ships;

[0009] S2. Based on the reference path and the coordinated speed, a distributed line-of-sight guidance method is used to obtain a reference heading and a reference speed;

[0010] S3, introducing a fuzzy logic system to approximate an unknown nonlinear function, inputting the reference heading into the bow turning moment controller, and obtaining a bow turning moment control input;

[0011] S4, introducing a fuzzy logic system to approximate the unknown nonlinear function, inputting the reference speed into the propulsion force controller, and obtaining the propulsion force control input;

[0012] S5. Inputting the bow torque control input and the propulsion force control input into the mathematical model of the under-actuated smart ship, and controlling the mathematical model of the under-actuated smart ship.

[0013] Furthermore, S1 specifically includes the following steps:

[0014] Generate reference paths and coordination speeds using the extended second-order consensus algorithm:

[0015]

[0016] Among them, ξ i (t) = [x di (t),y di (t)] T represents the generated reference path, whose derivative is .ζ i (t)=[u di (t),v di (t)] T , and define the coordination speed as ξ j and j represents the reference state information of the neighboring smart ship obtained through the communication topology; if the i-th smart ship can receive the information of the j-th smart ship, then a ij =1, otherwise a ij =0; δ ij is the expected deviation between the i-th smart ship and the j-th smart ship; K ri and K vi is the design parameter; (n+1) represents the state of the virtual navigator, and its derivative is ζ (n+1) ; δ i(n+1) represents the ideal deviation between the i-th smart ship and the virtual navigator.

[0017] Furthermore, S2 specifically includes the following steps:

[0018] The reference path [x di (t),y di (t)] and the coordinated speed U ci (t) is introduced into the line-of-sight guidance method, and the position error is defined in the path tangent coordinate system as:

[0019]

[0020] Among them, x eirepresents the longitudinal tracking error; y ei represents the lateral tracking error; represents the path tangent angle; [x di (t),y di (t)] represents the reference path generated by the extended second-order consistency algorithm;

[0021] Taking the derivative of the position error, we get:

[0022]

[0023] The reference heading is:

[0024]

[0025] Among them, Δ i >0 indicates forward distance, β i =atan2(v i ,u i ) represents the sideslip angle;

[0026] The reference speed is:

[0027]

[0028] Among them, κ 1i >0 is the design parameter.

[0029] Furthermore, the design steps of the bow torque controller are as follows:

[0030] The heading tracking error is defined as:

[0031] z ψi =ψ i -ψ di

[0032] The sliding surface is defined as:

[0033]

[0034] Among them, c i >0 is the design parameter;

[0035] Taking the derivative of the sliding surface, we get:

[0036]

[0037] in,

[0038] In the fuzzy logic system, the nonlinear function f ri Written as in represents the ideal parameter vector, h(υ) represents the basis vector, Represents the approximation error, and uses the fuzzy logic system to calculate the nonlinear function f ri Approximation, we get:

[0039]

[0040] in, represents the parameter vector of the approximation;

[0041] The adaptive law is designed as:

[0042]

[0043] where γ 1i >0 is the design parameter;

[0044] Bow turning moment control input τ ri for:

[0045]

[0046] Among them, k 1i >0 and ι 1i >0 is the design parameter.

[0047] Furthermore, the design steps of the propulsion force controller are as follows:

[0048] Define velocity tracking error:

[0049] z ui =u i -U di

[0050] Taking the derivative of the velocity tracking error, we get:

[0051]

[0052] In the fuzzy logic system, the nonlinear function f ui Written as in represents the ideal parameter vector, represents the approximation error;

[0053] Using fuzzy logic system to analyze the nonlinear function f ui Approximation, we get:

[0054]

[0055] in, represents the parameter vector of the approximation;

[0056] The adaptive law is designed as

[0057]

[0058] The above formula γ 2i >0 is the design parameter;

[0059] Propulsion control input τ ui for:

[0060]

[0061] Among them, k 2i >0 and ι 2i >0 indicates design parameters.

[0062] Furthermore, in S5, the mathematical model of the underactuated smart ship is as follows:

[0063]

[0064] Among them, η i =[x i ,y i ,ψ i ] T represents the position and heading of the i-th smart ship; i =[u i ,v i ,r i ] T Indicates the ship's forward, drifting and pitching speed; τ i =[τ ui ,0,τ ri ] T represents the control input; τ wi =[τ wui ,τ wvi ,τ wri ] T Indicates external interference; represents the rotation matrix; represents the inertia matrix; represents the damping matrix; represents the Coriolis centripetal matrix.

[0065] The present invention also provides a storage medium, which includes a stored program, wherein when the program is run, any of the above-mentioned methods for adaptive formation control of under-actuated intelligent ships based on distributed line-of-sight guidance is executed.

[0066] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes any one of the above-mentioned methods for adaptive formation control of under-actuated intelligent ships based on distributed line-of-sight guidance through the computer program.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] Aiming at the problem that the dimension of control output of underactuated intelligent ship is greater than the dimension of control input, the present invention introduces a line of sight guidance method and realizes the dimensionality reduction of control output by designing reference heading.

[0069] Compared with introducing distributed strategies into the control system, the present invention introduces an extended second-order consistency algorithm into the line-of-sight guidance system to generate a reference heading and a reference speed for the control system, and proposes an under-actuated intelligent ship adaptive formation controller based on a distributed line-of-sight guidance system, which simplifies the design of the controller.

[0070] Aiming at the uncertainty problem of underactuated intelligent ship model, the present invention introduces a fuzzy logic system to approximate nonlinear functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0072] Figure 1 This is a control block diagram of the present invention.

[0073] Figure 2 The communication topology diagram is switched for the present invention.

[0074] Figure 3 This is a performance diagram of the adaptive formation controller based on distributed line-of-sight guidance under the switching topology of the present invention.

[0075] Figure 4 This is the control input diagram of the smart ship of the present invention.

[0076] Figure 5 It is the speed diagram of the smart ship of the present invention.

[0077] Figure 6 The fuzzy logic system of the present invention is ri approximation performance.

[0078] Figure 7 The fuzzy logic system of the present invention is ui approximation performance. DETAILED DESCRIPTION

[0079] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0080] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0081] Figure 1 In i represents the reference path generated by the extended second-order consistency algorithm; U ci represents the coordination speed generated by the extended second-order consensus algorithm; U di represents the reference velocity generated by the distributed line-of-sight guidance method; ψ di represents the reference heading generated by distributed line-of-sight guidance; and Represents the approximation result of the fuzzy logic system to the unknown nonlinear function; τ ri and τ ui represents the controller input; τ wi Indicates environmental interference; x i ,y i Indicates the current position of the ship; u i Indicates the current speed of the ship; ψ i Indicates the current heading of the ship.

[0082] The mathematical model of ship motion is

[0083]

[0084] In formula (1), η i =[x i ,y i ,ψ i ] T represents the position and heading of the i-th smart ship;i =[u i ,v i ,r i ] T Indicates the ship's forward, drifting and pitching speed; τ i =[τ ui ,0,τ ri ] T represents the control input; τ wi =[τ wui ,τ wvi ,τ wri ] T Indicates external interference; represents the rotation matrix; represents the inertia matrix; represents the damping matrix; represents the Coriolis centripetal matrix.

[0085] An adaptive formation control method for underactuated intelligent ships based on distributed line-of-sight guidance. Figure 1 As shown, the following steps are included:

[0086] S1. Obtain reference state information of neighboring underactuated smart ships, and generate reference paths and coordinated speeds using an extended second-order consistency algorithm based on the reference state information of neighboring underactuated smart ships;

[0087]

[0088] In formula (2), ξ i (t) = [x di (t),y di (t)] T represents the generated reference path, whose derivative is ζ i (t)=[u di (t),v di (t)] T , and define the coordination speed as ξ j and j represents the reference state information of the neighboring smart ship obtained through the communication topology; if the i-th smart ship can receive the information of the j-th smart ship, then a ij =1, otherwise a ij =0; δ ij is the expected deviation between the i-th smart ship and the j-th smart ship; K ri and K vi is the design parameter; (n+1) represents the state of the virtual navigator, and its derivative is ζ (n+1) ; δ i(n+1) represents the ideal deviation between the i-th smart ship and the virtual navigator.

[0089] S2. Based on the reference path and the coordinated speed, a distributed line-of-sight guidance method is used to obtain a reference heading and a reference speed;

[0090] By using the reference path [x di (t),y di (t)] and the coordinated speed U ci (t) was introduced into the line-of-sight guidance method and proposed

[0091] Distributed line-of-sight guidance method. The position error is defined in the path tangent coordinate system as:

[0092]

[0093] In formula (3), x ei represents the longitudinal tracking error; y ei represents the lateral tracking error; represents the path tangent angle; [x di (t),y di (t)] represents the reference path generated by the extended second-order consistency algorithm.

[0094] Taking the derivative of formula (3) we can get

[0095]

[0096] The reference heading is designed as

[0097]

[0098] In formula (5), Δ i >0 indicates forward distance, β i =atan2(v i ,u i ) represents the sideslip angle.

[0099] The reference speed is designed as

[0100]

[0101] In formula (6), κ 1i >0 is the design parameter.

[0102] In order to analyze the stability of the proposed distributed line-of-sight guidance method, the Lyapunov equation is defined as follows:

[0103]

[0104] By taking the derivative of equation (7) and substituting equations (5) and (6) into its derivative, we can obtain

[0105]

[0106] According to equations (7) and (8), it can be proved that the closed-loop system is stable.

[0107] Considering the model uncertainty of intelligent ships, the present invention first introduces a fuzzy logic system to approximate the nonlinear function; then, based on the obtained reference heading ψ di and reference speed U di , design bow torque controller and propulsion force controller.

[0108] S3, introducing a fuzzy logic system to approximate an unknown nonlinear function, inputting the reference heading into the bow turning moment controller, and obtaining a bow turning moment control input;

[0109] The heading tracking error is defined as

[0110] z ψi =ψ i -ψ di (9)

[0111] The sliding surface is defined as

[0112]

[0113] In formula (10), c i >0 is the design parameter. Taking the derivative of equation (10) we can get

[0114]

[0115] In formula (11) In the fuzzy logic system, the nonlinear function f ri It can be written as in represents the ideal parameter vector, h(υ i ) represents the basis vector, Represents the approximation error. Using fuzzy logic system to analyze the nonlinear function f ri By approximation, we can get

[0116]

[0117] In formula (12), represents the approximation parameter vector. The control input τ ri Can be designed as

[0118]

[0119] In formula (13), k 1i >0 and ι 1i >0 is the design parameter. The adaptive law can be designed as

[0120]

[0121] In formula (14), γ 1i >0 is the design parameter. Define the Lyapunov equation in the following form

[0122]

[0123] In formula (15) Taking the derivative of equation (15) and substituting equations (13) and (14) into equation (15), we can obtain

[0124]

[0125] According to equations (15) and (16), the closed-loop system is stable.

[0126] S4, introducing a fuzzy logic system to approximate the unknown nonlinear function, inputting the reference speed into the propulsion force controller, and obtaining the propulsion force control input;

[0127] Defining velocity tracking error

[0128] z ui =u i -U di (17)

[0129] Taking the derivative of formula (17), we can get

[0130]

[0131] In the fuzzy logic system, the nonlinear function f ui It can be written as in represents the ideal parameter vector, Represents the approximation error. Use fuzzy logic system to analyze the nonlinear function f ui By approximation, we can get

[0132]

[0133] In formula (19), represents the approximation parameter vector. The control input τ ui Designed to

[0134]

[0135] In formula (20), k 2i >0 and ι 2i >0 indicates the design parameter. The adaptive law is designed as

[0136]

[0137] In formula (21), γ 2i >0 is the design parameter. Define the Lyapunov equation in the following form

[0138]

[0139] In formula (21) Taking the derivative of equation (22) and substituting equations (20) and (21) into equation (22), we can obtain

[0140]

[0141] According to equations (22) and (23), the closed-loop system is stable.

[0142] S5. Inputting the bow torque control input and the propulsion force control input into the mathematical model of the under-actuated smart ship, and controlling the mathematical model of the under-actuated smart ship.

[0143] Use Figure 2 The switching topology shown is used to verify the effectiveness of the proposed adaptive formation controller based on distributed line-of-sight guidance. Figure 2 Node 5 represents the virtual navigator, and nodes 1-4 represent the smart ship. Figure 2 The communication topology in is switched every 75 seconds in the order of (a), (b), (c) and (d). The initial states of the four smart ships are

[0144]

[0145] x4=[2.5-2.5π000] T The design parameters of the adaptive formation controller based on distributed line-of-sight guidance are K vi =diag(1,1),K ri =diag(1,1),κ 1i =0.1, c i =1, k 1i =1,ι 1i =0.0001, k 2i =1.5,ι 2i =0.000001,γ 1i =1 and γ 2i =1.

[0146] Under the switching topology, the performance of the adaptive formation controller based on distributed line-of-sight guidance is shown in Figure 3-7 . Figure 3 In this example, the four smart ships can maintain a formation and sail. The control inputs of the four smart ships are as follows: Figure 4 The speed of the smart ship is shown in Figure 5 shown. Figure 6 and Figure 7It shows that fuzzy logic system can approximate unknown nonlinear functions.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive formation control method for underactuated intelligent ships based on distributed line-of-sight guidance, characterized in that: The steps include: S1. Obtain reference state information of neighboring underactuated smart ships, and generate reference paths and coordinated speeds using an extended second-order consistency algorithm based on the reference state information of neighboring underactuated smart ships; S2. Based on the reference path and the coordinated speed, a distributed line-of-sight guidance method is used to obtain a reference heading and a reference speed; S3, introducing a fuzzy logic system to approximate an unknown nonlinear function, inputting the reference heading into the bow turning moment controller, and obtaining a bow turning moment control input; S4, introducing a fuzzy logic system to approximate the unknown nonlinear function, inputting the reference speed into the propulsion force controller, and obtaining the propulsion force control input; S5. Inputting the bow torque control input and the propulsion force control input into the mathematical model of the under-actuated smart ship, and controlling the mathematical model of the under-actuated smart ship.

2. The adaptive formation control method of underactuated intelligent ships based on distributed line-of-sight guidance according to claim 1 is characterized in that: S1 specifically includes the following steps: Generate reference paths and coordination speeds using the extended second-order consensus algorithm: Among them, ξ i (t) = [x di (t),y di (t)] T represents the generated reference path, and its derivative is denoted as ζ i (t)=[u di (t),v di (t)] T , and define the coordination speed as ξ j and j represents the reference state information of the neighboring smart ship obtained through the communication topology; if the i-th smart ship can receive the information of the j-th smart ship, then a ij =1, otherwise a ij =0; δ ij is the expected deviation between the i-th smart ship and the j-th smart ship; K ri and K vi is the design parameter; (n+1) represents the state of the virtual navigator, and its derivative is ζ (n+1) ; δ i(n+1) represents the ideal deviation between the i-th smart ship and the virtual navigator.

3. The adaptive formation control method of underactuated intelligent ships based on distributed line-of-sight guidance according to claim 1 is characterized in that: S2 specifically includes the following steps: The reference path [x di (t),y di (t)] and the coordinated speed U ci (t) is introduced into the line-of-sight guidance method, and the position error is defined in the path tangent coordinate system as: Among them, x ei represents the longitudinal tracking error; y ei represents the lateral tracking error; represents the path tangent angle; [x di (t),y di (t)] represents the reference path generated by the extended second-order consistency algorithm; Taking the derivative of the position error, we get: The reference heading is: Among them, Δ i >0 indicates forward distance, β i =atan2(v i ,u i ) represents the sideslip angle; The reference speed is: Among them, κ 1i >0 is the design parameter.

4. The adaptive formation control method of underactuated intelligent ships based on distributed line-of-sight guidance according to claim 1 is characterized in that: The design steps of the bow torque controller are as follows: The heading tracking error is defined as: z ψi =ψ i -ψ di The sliding surface is defined as: Among them, c i >0 is the design parameter; Taking the derivative of the sliding surface, we get: in, In the fuzzy logic system, the nonlinear function f ri Written as in represents the ideal parameter vector, h(υ i ) represents the basis vector, Represents the approximation error, and uses the fuzzy logic system to calculate the nonlinear function f ri Approximation, we get: in, represents the parameter vector of the approximation; The adaptive law is designed as: where γ 1i >0 is the design parameter; Bow turning moment control input τ ri for: Among them, k 1i >0 and ι 1i >0 is the design parameter.

5. The method for adaptive formation control of underactuated intelligent ships based on distributed line-of-sight guidance according to claim 1 is characterized in that: The design steps of the propulsion controller are as follows: Define velocity tracking error: z ui =u i -U di Taking the derivative of the velocity tracking error, we get: In the fuzzy logic system, the nonlinear function f ui Written as in represents the ideal parameter vector, represents the approximation error; Using fuzzy logic system to analyze the nonlinear function f ui Approximation, we get: in, represents the parameter vector of the approximation; The adaptive law is designed as The above formula γ 2i >0 is the design parameter; Propulsion control input τ ui for: Among them, k 2i >0 and ι 2i >0 indicates design parameters.

6. The method for adaptive formation control of underactuated intelligent ships based on distributed line-of-sight guidance according to claim 1 is characterized in that: In S5, the mathematical model of the underactuated smart ship is as follows: Among them, η i =[x i ,y i ,ψ i ] T represents the position and heading of the i-th smart ship; υ i =[u i ,v i ,r i ] T Indicates the ship's forward, drifting and pitching speed; τ i =[τ ui ,0,τ ri ] T represents the control input; τ wi =[τ wui ,τ wvi ,τ wri ] T Indicates external interference; represents the rotation matrix; represents the inertia matrix; represents the damping matrix; represents the Coriolis centripetal matrix.

7. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the method for adaptive formation control of under-actuated intelligent ships based on distributed line-of-sight guidance as described in any one of claims 1 to 6 is executed.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the adaptive formation control method for underactuated intelligent ships based on distributed line-of-sight guidance as described in any one of claims 1 to 6 through the computer program.

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