A cooperative control method for multiple underactuated unmanned vessel systems under input saturation constraints

Abstract

The present invention belongs to the technical field of unmanned vessel control and specifically discloses a collaborative control method for multiple underactuated unmanned vessel systems under input saturation constraints, comprising the following steps: Step S1, establishing a full-drive model of the multiple underactuated unmanned vessel systems; Step S2, designing a fixed-time disturbance observer; Step S3, designing a fixed-time adaptive RBF neural network observer to estimate the input saturation term; Step S4, establishing a collaborative tracking error system based on the collaborative control objective and network topology; Step S5, establishing a fixed-time terminal sliding mode surface to obtain a distributed terminal sliding mode control law; Step S6, constructing a fixed-threshold event triggering mechanism and designing a distributed event-triggered terminal sliding mode controller to implement collaborative tracking control of the system through the output instructions of the controller. The present invention simultaneously achieves the two major indicators of "fast collaborative control" and "saving communication resources", solving the fixed-time collaborative tracking control problem of multiple underactuated unmanned vessel systems.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vessel control, and in particular to a collaborative control method for a multiple underactuated unmanned vessel system under input saturation constraints. Background Art

[0002] With the development of marine resource development and intelligent shipping, collaborative control technology for multiple unmanned vessels (USVs) has shown significant value in areas such as environmental monitoring and swarm search and rescue. Currently, in order to improve control efficiency and meet practical needs, "rapid collaborative control" has become one of the focus issues for experts and scholars in the control field. At the same time, considering the limited communication resources, how to "save communication resources" on the basis of "rapid collaborative control" and further optimize control performance is the research focus of collaborative control of multiple unmanned vessel systems. However, existing related research results have the following shortcomings:

[0003] Most existing results only study one of the two, that is, only consider "rapid collaborative control" or "saving communication resources", and there are few research results that consider both at the same time. In fact, event-triggered control is a consensus method to solve the problem of "saving communication resources", and the introduction of event-triggered mechanism will inevitably reduce control efficiency. How to integrate event-triggered control strategy into "rapid collaborative control" to achieve a "win-win" control performance is still a huge challenge. The realization of "rapid collaborative control" implies that the speed of the unmanned ship may be very fast, which also means that the corresponding control input is likely to be saturated. If it is not compensated, the overall control will fail. However, existing research results are missing in this regard. In addition, because the unmanned ship system model has characteristics such as under-driving and strong nonlinearity, the design process of the corresponding controller is very complicated and the design is very difficult.

[0004] In summary, a collaborative control method for multiple underactuated unmanned vessel systems under input saturation constraints is needed to solve the above problems in the existing technology. Summary of the Invention

[0005] The present invention provides a cooperative control method for multiple underactuated unmanned vessel systems under input saturation constraints, which solves the fixed-time cooperative tracking problem of multiple underactuated unmanned vessel systems.

[0006] In order to achieve the purpose of solving the above technical problems, the present invention adopts the following technical solutions:

[0007] A collaborative control method for a multi-underactuated unmanned vessel system under input saturation constraints comprises the following steps:

[0008] Step S1, establishing an all-wheel drive model of a multi-underactuated unmanned vessel system;

[0009] Step S2: designing a fixed-time disturbance observer for the all-wheel drive model to achieve fixed-time estimation of disturbances;

[0010] Step S3, designing a fixed-time adaptive RBF neural network observer for estimating the input saturation term of the multiple underactuated unmanned vessel system;

[0011] Step S4: establishing a collaborative tracking error system according to the collaborative control target and the network topology;

[0012] Step S5: Establish a fixed-time terminal sliding mode surface and derive it to obtain an equivalent distributed terminal sliding mode control law;

[0013] Step S6: Construct a fixed threshold event trigger mechanism, design a distributed event trigger-terminal sliding mode controller in combination with the sliding mode belt idea, and realize the coordinated tracking control of the multi-underactuated unmanned vessel system through the output instructions of the controller.

[0014] In some embodiments of the present invention, step S1 includes the following steps:

[0015] Establish a dynamic mathematical model of a three-degree-of-freedom multi-underactuated unmanned vessel system under input saturation constraints;

[0016] Performing order-up and dimension-reduction on the motion state parameters in the kinematic model to construct an equivalent all-wheel drive model of the under-actuated unmanned ship system;

[0017] A saturated input constraint is introduced into the formula of the all-wheel drive model.

[0018] In some embodiments of the present invention, the formula of the all-wheel drive model is:

[0019] ;

[0020] Among them, B i is the state transfer matrix, q i is the position information in the inertial coordinate system, τ ci is the control input information of the multiple underactuated unmanned ship system without saturation constraint; Δτ i is the difference caused by saturated input; d i is the external disturbance information; f i is the nonlinear term information.

[0021] In some embodiments of the present invention, the difference Δτ caused by the saturated input i The calculation formula is:

[0022]

[0023] in is the control input of the all-wheel drive system; τu i is the control input generated by the thruster under saturation constraint, τ r i is the control input generated by the servo under saturation constraint; τ ci =[τ ci,1 , τ ci,2 ] T is the control input of the unmanned ship under saturation constraint, τ ci,1 is the control input generated by the thruster without saturation constraint, τ ci,2 is the control input generated by the servo without saturation constraint, Δτ u i is the control input τ under saturation constraint u i With the control input τ without saturation constraint ci,1 The difference, Δτ r i is the control input τ under saturation constraint r i With the control input τ without saturation constraint ci,2 and With input saturation constraints, the following formula is satisfied:

[0024]

[0025] Among them, τ max is the saturation upper limit of the system input, τ min The saturation lower limit of the system input.

[0026] In some embodiments of the present invention, the fixed threshold event triggering mechanism is designed as follows:

[0027]

[0028] where t i,m k+1 represents the triggering time of the k+1th event of the mth control input of the i-th unmanned ship; t i,m k Indicates the triggering time of the kth event of the mth control input of the i-th unmanned ship; m=1,2; t At time t, the event triggering error function e of the mth control input of the i-th unmanned ship is im (t)=u * i,m (t)-τ ci,m (t), where u * i,m (t) is t The time-fixed terminal sliding mode control law, σ im is a positive constant, when e imThe absolute value of (t) is greater than σ im When the controller is updated, it is triggered, otherwise it is not updated.

[0029] In some embodiments of the present invention, the formula of the fixed-time RBF neural network observer in step S3 is as follows:

[0030] ;

[0031] in: is the velocity vector w i The estimated value of B i is the transfer matrix, τ ci is the control input information of the multiple underactuated unmanned ship system without saturation constraints; f i is the nonlinear term information; is the disturbance information d i estimated value of; m =1,2;a0, is a positive diagonal gain matrix used to adjust the convergence speed of the observer; , , is the optimal weight W of the adaptive RBF neural network i estimated value of; is a bounded neuron radial basis function vector satisfying , Is a positive number.

[0032] In some embodiments of the present invention, the estimated value The adaptive update rule is:

[0033]

[0034] Among them, the observation error , is the speed observation error of the i-th unmanned ship in the x-axis direction, is the speed observation error of the i-th unmanned ship in the y-axis direction, μ1 and μ2 are positive constants, representing the learning rate and damping coefficient of weight update, respectively.

[0035] In some embodiments of the present invention, a control system for a multi-underactuated unmanned vessel system under input saturation constraints is provided, comprising:

[0036] A model conversion module, which is used to convert the mathematical model of the multiple underactuated unmanned ship system into an all-wheel drive model;

[0037] The disturbance observation module estimates the environmental disturbance through a designed fixed-time disturbance observer;

[0038] An input saturation estimation module estimates the input saturation term of the multiple underactuated unmanned vessel system using a fixed-time adaptive RBF neural network observer;

[0039] The controller design execution module is used to design a fixed-time sliding surface and a fixed-threshold event trigger mechanism to obtain a controller, and is also used to output control instructions to achieve collaborative tracking control of multiple underactuated unmanned vessels;

[0040] Communication module, used for communicating with external devices.

[0041] In some embodiments of the present invention, an electronic device is provided, comprising:

[0042] a processor, and a memory and a transceiver communicatively connected to the processor;

[0043] The memory stores computer-executable instructions; the transceiver is used to transmit and receive data;

[0044] The processor executes the computer-executable instructions stored in the memory to implement the control method of the multiple underactuated unmanned vessel system.

[0045] In some embodiments of the present invention, a computer-readable storage medium is provided, characterized in that:

[0046] The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the control method of the multiple underactuated unmanned vessel system.

[0047] The technical solution of the present invention has the following technical effects compared with the prior art:

[0048] Based on the idea of ​​upgrading and dimensionality reduction, this paper constructs an equivalent full-drive model of the under-actuated unmanned ship system, laying an important foundation for the subsequent simplified controller design; adopts a fixed-time terminal sliding surface to ensure that the collaborative tracking error of multiple unmanned ships converges within a fixed time, designs a fixed threshold event trigger mechanism, introduces the idea of ​​sliding mode belt, solves the fusion problem of event-triggered control and sliding mode control, and simultaneously realizes the two major indicators of "fast collaborative control" and "saving communication resources"; innovatively introduces a fixed-time adaptive RBF neural network to realize input saturation compensation control, which reduces the computational burden while improving the system's robustness to changes in distributed event-triggered controller parameters, making the designed collaborative control method of multiple under-actuated unmanned ship systems more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 The present invention is a flowchart of the control method.

[0051] Figure 2 The present invention relates to a network topology structure of a multi-underactuated unmanned vessel system.

[0052] Figure 3 The present invention provides a cooperative tracking route map for a multiple underactuated unmanned vessel system when the desired trajectory is circular.

[0053] Figure 4 The invention relates to a multi-underactuated unmanned vessel system, and discloses an estimation error of the disturbance in the x-axis direction when the expected trajectory is circular.

[0054] Figure 5 It is the estimation error of the disturbance in the y-axis direction of the multiple underactuated unmanned vessel system involved in the present invention when the expected trajectory is circular.

[0055] Figure 6 It is the estimated error of the saturation compensation term in the x-axis direction when the expected trajectory of the multiple underactuated unmanned vessel system involved in the present invention is circular.

[0056] Figure 7 It is the estimated error of the saturation compensation term in the y-axis direction when the expected trajectory of the multiple underactuated unmanned vessel system involved in the present invention is circular.

[0057] Figure 8 The control input τ of the multi-underactuated unmanned ship system of the present invention is when the desired trajectory is circular u Event triggering time diagram.

[0058] Figure 9 The control input τ of the multi-underactuated unmanned ship system of the present invention is when the desired trajectory is circular r Event triggering time diagram.

[0059] Figure 10 Schematic diagram of the structure of the control system.

[0060] Figure 11 Schematic diagram of the structure of the electronic device.

[0061] Figure numerals: 100, control system; 110, model conversion module; 120, disturbance observation module; 130, input saturation estimation module; 140, controller design execution module; 150, communication module; 200, electronic device; 210, processor; 220, memory; 230, transceiver. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0063] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections. A person of ordinary skill in the art will understand the specific meanings of the above terms in the present invention in specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any appropriate manner in any one or more embodiments or examples.

[0064] Example 1: Reference Figure 1 As shown, this embodiment provides a control method for a multi-underactuated unmanned vessel system under input saturation constraints, comprising the following steps:

[0065] Step S1: Establishing a fully driven model of the multi-underactuated unmanned vessel system

[0066] Step S11: The multi-underactuated unmanned vessel system of this embodiment is composed of N The first is composed of three isomorphic underactuated unmanned ships with three degrees of freedom. i strip( i =1,…, N ) Mathematical model of the underactuated unmanned vessel system with external disturbances:

[0067]

[0068] where η i =[x i ,y i ,ψ i ] T For the i The pose state vector of the unmanned ship, ν i =[u i ,v i ,r i ] T is the velocity state vector of the i-th unmanned ship. Specifically, x i 、y i , ψ i They represent the longitudinal displacement, transverse displacement and yaw angle in the earth coordinate system respectively; u i 、v i 、r i represent the longitudinal velocity, transverse velocity and yaw angular velocity in the inertial coordinate system respectively; τ i =[τ i u , 0,τ i r ] T represents the control input of the i-th unmanned ship, τ ei =[τ ei u , τ ei v , τ ei r ] T It represents the external disturbance force on the three degrees of freedom of the unmanned ship. i ), inertia matrix M i , Coriolis force and centripetal force matrix C(v i ), damping matrix D i They are as follows:

[0069]

[0070] where d 11 d 22 d 23 d 32 d 33 represents the fluid dynamic damping, m 11 、m 22 、m 23 、m 32 、m 33 Represents inertia and additional mass, and both are positive numbers; where m 23 =m 32 , c i 13 =m 32 r i -m 22 v i , c i 23 =m 11 u i .

[0071] Step S12: performing order-up and dimension-down operations on the motion state parameters in the kinematic model to construct an equivalent all-wheel drive model of the under-actuated unmanned vessel system;

[0072] According to formula (1), we can get:

[0073]

[0074] According to the dynamic equation in formula (1) and the actual system Facts, we can get:

[0075]

[0076] in:

[0077]

[0078] According to formula (2) and formula (3), we can get:

[0079]

[0080] in:

[0081]

[0082]

[0083] Define position information in the inertial coordinate system , nonlinear term information , control input information , external disturbance information , the state transfer matrix of the system is:

[0084]

[0085] Obviously there are: 、 , we can get the converted all-wheel drive model:

[0086]

[0087] Step S13: introducing a saturated input constraint into the formula of the all-wheel drive model.

[0088] Considering the input saturation constraint of the system, formula (7) can be rewritten as:

[0089]

[0090] The control input of the unmanned ship without saturation constraint is τ ci =[τ ci,1 , τ ci,2 ] T , τ ci,1 is the control input generated by the thruster without saturation constraint, τ ci,2is the control input generated by the servo without saturation constraint, and the difference caused by saturation input ;Δτ u i is the control input τ under saturation constraint u i With the control input τ without saturation constraint ci,1 The difference, Δτ r i is the control input τ under saturation constraint r i With the control input τ without saturation constraint ci,2 The difference.

[0091] In order to facilitate subsequent calculations, let is the inertial coordinate system i The speed information of the ship.

[0092] Therefore, the converted all-wheel drive model can be written as:

[0093]

[0094] The input function is:

[0095]

[0096]

[0097] where τ max is the saturation upper limit of the system input, τ min The saturation lower limit of the system input.

[0098] Step S2: designing a fixed-time disturbance observer for the all-wheel drive model to achieve fixed-time estimation of disturbances;

[0099] Define h i is the external disturbance information d i The derivative of , so formula (9) can be written as follows

[0100]

[0101] in The above system can be considered as an uncoupled system, so:

[0102]

[0103] where w im d im , τ z im 、f im 、h z im Represents vector w respectivelyi d i , τ z i 、f i 、h z i No. m elements, m =1,2. For a given vector x=[x1,x2,…,x n ] T , define the vector function sig α (x)=[sig α (x1), sig α (x2),…,sig α (x n )] T , function sig α (x i )=| x i | α sign (x i ), sign (·) is the sign function.

[0104] According to formula (13), the following fixed-time disturbance observer is designed:

[0105]

[0106] in , They are speed information w im and disturbance information d im The estimated value of , , k1 and k2 are control constants and are greater than 0, and ε is a parameter with a very small value.

[0107] Step S3, designing a fixed-time adaptive RBF neural network observer for estimating the input saturation term of the multiple underactuated unmanned vessel system;

[0108] Introducing adaptive RBF neural network:

[0109]

[0110] The ideal weight matrix of the neural network , , l is the number of neurons in the hidden layer of the neural network, Approximate the error vector for the neural network and satisfy , is a bounded constant, is a bounded neuron radial basis function vector satisfying , is a positive constant. Bi Δτ i It represents the influence of saturation difference on the dynamics of the system. It is the approximation of saturation difference by neural network under ideal weight. The neural network is used to dynamically estimate B i Δτ i , thereby compensating for the saturation effect in the controller design and improving the system robustness.

[0111] The fixed-time adaptive RBF neural network observer is designed as follows:

[0112]

[0113] in is the velocity vector w i The estimated value of a0, is a positive diagonal gain matrix used to adjust the convergence speed of the observer; , , is the optimal weight W of the adaptive RBF neural network i The adaptive update rule of is:

[0114]

[0115] in: The observation error, μ1 and μ2 are positive constants, representing the learning rate and damping coefficient of weight update, respectively.

[0116] Step S4: establishing a collaborative tracking error system according to the collaborative control target and the network topology;

[0117] Step S41: determining a coordinated control target for multiple underactuated unmanned vessels;

[0118] Given the expected formation position information: , where E i ( i =1,…, N ) is a 2-dimensional time-invariant column vector. The expected tracking trajectory position information is , the corresponding expected tracking trajectory velocity information is .

[0119] For the i For an unmanned ship, in any initial state, there is a time constant T>0,

[0120] If the above hypothesis holds, the multi-underactuated unmanned vessel system is said to be able to achieve fixed-time collaborative tracking control.

[0121] Step S42: determining the network topology structure in the collaborative tracking control process of multiple underactuated unmanned vessels;

[0122] Reference Figure 2 As shown, N The interactive communication topology formed by the underactuated unmanned vessels contains at least one directed spanning tree, and the root node can directly obtain the desired trajectory information.

[0123] Step S43: defining the cooperative tracking error of the underactuated unmanned vessel;

[0124] Definition i Coordinated tracking error of the ships:

[0125]

[0126]

[0127] where z i =[z i1 , z i2 ] T For the i The position information of the coordinated tracking error of the two ships, z i1 is the position information in the x-axis direction, z i2 It is the position information in the y-axis direction. is the speed information of the cooperative tracking error of the i-th ship, is the velocity information in the x-axis direction, is the speed information in the y-axis direction.

[0128] a ij The graph adjacency matrix i Rank j Column element, E i For the i The expected formation information of the ships, E j For the j The expected formation information of the ships, b i is the containment gain. q0 is the position information of the desired trajectory, and w0 is the speed information of the desired trajectory. j For the j The speed information of the ship. id =b i +d i in , where d i in For the i The entry degree of the boat.

[0129] Step S5: Establish a fixed-time terminal sliding mode surface and derive it to obtain an equivalent distributed terminal sliding mode control law;

[0130] Step S51: Design a fixed time terminal sliding surface S i as follows:

[0131]

[0132] where and

[0133]

[0134] where m = 1, 2, known constants a s > 0, b s > 0, r1 > 1, 1 < r2 < 2, are positive numbers. and ensure the continuity of the sliding surface S i .

[0135] Step S52: According to the sliding surface (19), the fixed-time terminal sliding mode controller can be obtained as follows:

[0136]

[0137] where: u * i (t) = [u * i,1 (t), u * i,2 (t)] T , u * i,1 (t) represents the terminal sliding mode controller of the i-th unmanned ship in the x-axis direction, and u * i,2 (t) represents the terminal sliding mode controller of the i-th unmanned ship in the y-axis direction.

[0138] The parameters , α n , β n , r3, r4 are all positive constants, , . is the input saturation compensation term.

[0139] is the equivalent control part, where ; ( m = 1, 2) is in the following form:

[0140]

[0141] is the fixed-time control part.

[0142]

[0143]

[0144] in,

[0145]

[0146] in: m= 1,2, It is a parameter to be designed. is a non-negative function and satisfies hour .

[0147] Step S6: Construct a fixed threshold event trigger mechanism, design a distributed event trigger-terminal sliding mode controller in combination with the sliding mode belt idea, and realize the coordinated tracking control of the multi-underactuated unmanned vessel system through the output instructions of the controller.

[0148] Step S61: Based on the controller u i * Design a distributed event-triggered controller:

[0149]

[0150] Where: u * i,m (t i,m k ) represents the value of the mth control input of the i-th unmanned ship at the last triggering moment, τ ci,m represents the output value of the mth control input of the i-th unmanned ship at time t, where t i,m k represents the triggering time of the kth event of the mth control input of the i-th unmanned ship, t i,m k+1 It represents the triggering time of the k+1th event of the mth control input of the i-th unmanned ship, where m=1,2.

[0151] Step S62: Design a fixed threshold event trigger mechanism as follows:

[0152]

[0153] where σ im is a positive constant, t Moment i Unmanned ship m The event trigger error function e of the control input im =u * i,m -τ ci,m , when e im The absolute value of im When the controller is triggered to update, otherwise it will not be updated, avoiding frequent communication and saving communication resources.

[0154] Effect verification:

[0155] Reference Figure 2 As shown in the figure, a multi-underactuated unmanned ship system consisting of five underactuated unmanned ships is considered. i The relevant parameters of the unmanned ship are as follows:

[0156]

[0157] External disturbance: τ ei =[τ ei u , τ ei v , τ ei r ] T ,

[0158] where τ ei u =-0.15cos(0.01t)cos(0.015t),τ ei v =0.15sin(0.21t)cos(0.2t), τ ei r =-0.15sin(0.2t)cos(0.23t);

[0159] The saturation constraint of each unmanned ship control input is: τ max =50,τ min =-50.

[0160] Only ship 1 can directly receive the information of the desired trajectory, and the Laplacian matrix is:

[0161] .

[0162] Control objective: Multiple underactuated unmanned ship systems coordinate to track a circular trajectory q0 = [3cos(0.09t), 3cos(0.09t)] according to the following formation information. T The initial position information, initial speed information and expected formation information of each unmanned ship are as follows:

[0163]

[0164] Reference Figure 3 As shown in the figure, it is demonstrated that when the desired trajectory is circular, the multi-underactuated unmanned vessel system realizes fixed-time cooperative tracking control under the distributed event-triggered-terminal sliding mode control method proposed in the present invention.

[0165] Figure 4 and Figure 5The figure shows the evolution of the observation error of the fixed-time disturbance observer. It can be clearly seen from the figure that the estimation error of the disturbance observer converges smoothly in both the x-axis and y-axis directions within the specified convergence time and stabilizes in a small error range.

[0166] Figure 6 and Figure 7 The estimated error variation curve of the fixed-time RBF neural network observer is shown. It can be observed from the figure that the estimated error of the fixed-time RBF neural network observer in both the x-axis and y-axis directions is within the specified time.

[0167] It converges quickly and stabilizes within a small error range.

[0168] Figure 8 and Figure 9 The control input τ of the multiple underactuated unmanned vessel system is shown u , τ r Event triggering time image From the figure, we can see that the average event triggering interval of the system controller is significantly longer than the given simulation sampling period of 0.01s, so there is no Zeno phenomenon in the system.

[0169] The technical solution of the present invention has the following technical effects compared with the prior art:

[0170] Based on the idea of ​​upgrading and dimensionality reduction, this paper constructs an equivalent full-drive model of the under-actuated unmanned ship system, laying an important foundation for the subsequent simplified controller design; adopts a fixed-time terminal sliding surface to ensure that the collaborative tracking error of multiple unmanned ships converges within a fixed time, designs a fixed threshold event trigger mechanism, introduces the idea of ​​sliding mode belt, solves the fusion problem of event-triggered control and sliding mode control, and simultaneously realizes the two major indicators of "fast collaborative control" and "saving communication resources"; innovatively introduces a fixed-time adaptive RBF neural network to realize input saturation compensation control, which reduces the computational burden while improving the system's robustness to changes in distributed event-triggered controller parameters, making the designed collaborative control method of multiple under-actuated unmanned ship systems more practical.

[0171] Example 2: This example will be based on Figure 10 and Figure 11 A control system 100 and an electronic device 200 for a multiple underactuated unmanned vehicle system under input saturation constraints are described.

[0172] Among them, reference Figure 10 As shown, a control system 100 of a multi-underactuated unmanned vessel system under input saturation constraint is provided, comprising:

[0173] The model conversion module 110 is used to convert the mathematical model of the multi-underactuated unmanned vessel system into an equivalent all-wheel drive model; it avoids the complex nonlinear expressions caused by traditional mathematical reconstruction, reduces the state space dimension, significantly simplifies the complexity of controller design, and improves computational efficiency and control intuitiveness.

[0174] The disturbance observation module 120 estimates environmental disturbances through a designed fixed-time disturbance observer. Traditional sliding mode control generates discontinuous control signals through sign functions to suppress disturbances, which can lead to chattering. Traditional disturbance observers require known uncertainty and upper bound information on disturbance derivatives, and can only guarantee asymptotic stability, but cannot complete disturbance estimation within a fixed time. This embodiment achieves rapid disturbance estimation by designing a fixed-time disturbance observer, and the upper bound of the convergence time is independent of the initial state, thereby reducing the chattering of the control input.

[0175] Input saturation estimation module 130 utilizes a fixed-time adaptive RBF neural network observer to estimate the input saturation term of the multi-underactuated unmanned vessel system. Existing approaches have employed auxiliary systems to achieve compensatory control for input saturation, but these methods suffer from complex parameter tuning and high computational burdens, making them difficult to adapt to the real-time requirements of multi-vessel collaboration. This embodiment innovatively introduces an RBF neural network to estimate and compensate for input saturation, adaptively adjusting network weights online. This method is independent of unmanned vessel model parameters and offers enhanced generalization capabilities.

[0176] The controller design execution module 140 is used to design a fixed-time sliding surface and a fixed threshold trigger mechanism to obtain a controller, and is also used to output control instructions to realize collaborative tracking control of multiple under-actuated unmanned ships; a fixed-time terminal sliding surface is designed based on the fixed-time adaptive RBF neural network to ensure that the collaborative tracking error of multiple unmanned ships converges within a fixed time, and a fixed threshold event trigger mechanism is designed. The sliding belt idea is introduced to solve the fusion problem of event-triggered control and sliding mode control, that is, the two major indicators of "fast collaborative control" and "saving communication resources" are achieved at the same time.

[0177] The communication module 150 is used to communicate with external devices.

[0178] It should be understood that the control system 100 here is embodied in the form of a functional module. The term "module" here can refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art will understand that the control system 100 can be specifically the electronic device 200 in the above embodiment, or the functions of the electronic device 200 in the above embodiment can be integrated in the control system 100, and the control system 100 can be used to execute the various processes and / or steps corresponding to the electronic device 200 in the above method embodiment. To avoid repetition, they will not be described here.

[0179] The control system 100 has the functions of implementing the corresponding steps performed by the electronic device 200 of the control method in Example 1. These functions can be implemented through hardware or through hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. For example, the acquisition module can be a communication interface, such as a transceiver interface.

[0180] Reference Figure 11 As shown, in this embodiment, an electronic device 200 is provided, including:

[0181] A processor 210, and a memory 220 and a transceiver 230 communicatively connected to the processor;

[0182] The memory 220 stores computer-executable instructions; the transceiver 230 is used to send and receive data;

[0183] The processor 210 executes the computer-executable instructions stored in the memory 220 to implement the control method in embodiment 1.

[0184] It should be understood that the electronic device 200 can be used to execute the corresponding steps and / or processes in the above-mentioned method embodiments. Optionally, the memory 220 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory 220 may also include a non-volatile random access memory. For example, the memory 220 may also store device type information. The processor 210 can be used to execute the instructions stored in the memory 220, and when the processor 210 executes the instructions, the processor 210 may perform the corresponding steps and / or processes in the above-mentioned method embodiments.

[0185] It should be understood that in the embodiment of the present application, the processor 210 may be a central processing unit (CPU), or other general-purpose processors, a DSP digital signal processor 210, an ASIC application-specific integrated circuit, an FPGA field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0186] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 210 or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor 210. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor executes the instructions in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0187] Example 3: In this example, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the control method in Example 1.

[0188] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0191] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0192] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0193] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A collaborative control method for multiple underactuated unmanned vessel systems under input saturation constraints, characterized in that: The following steps are involved: Step S1, establishing an all-wheel drive model of a multi-underactuated unmanned vessel system; The formula of the all-wheel drive model is: ； Among them, B i is the state transfer matrix, q i is the position information in the inertial coordinate system, τ ci is the control input information of the multiple underactuated unmanned ship system without saturation constraint; Δτ i is the difference caused by saturated input; d i is the external disturbance information; f i is the nonlinear term information; Step S2: designing a fixed-time disturbance observer for the all-wheel drive model to achieve fixed-time estimation of disturbances; Step S3, designing a fixed-time adaptive RBF neural network observer for estimating the input saturation term of the multiple underactuated unmanned vessel system; The formula of the fixed-time adaptive RBF neural network observer in step S3 is as follows: ； in: is the velocity vector w i The estimated value of B i is the transfer matrix, τ ci is the control input information of the multiple underactuated unmanned ship system without saturation constraints; f i is the nonlinear term information; is the disturbance information d i estimated value of; m =1,2;a0, is a positive diagonal gain matrix used to adjust the convergence speed of the observer; , , is the optimal weight W of the adaptive RBF neural network i estimated value of; is a bounded neuron radial basis function vector satisfying , is a positive constant; Step S4: establishing a collaborative tracking error system according to the collaborative control target and the network topology; Step S5: Establish a fixed-time terminal sliding mode surface and derive it to obtain an equivalent distributed terminal sliding mode control law; The fixed-time terminal sliding mode controller is: ； in: , represents the terminal sliding mode controller of the i-th unmanned ship in the x-axis direction, represents the terminal sliding mode controller of the i-th unmanned ship in the y-axis direction; Definition i Coordinated tracking error of the ships: ； ； where z i =[z i1 , z i2 ] T For the i The position information of the coordinated tracking error of the two ships, z i1 is the position information in the x-axis direction, z i2 is the y-axis position information; is the speed information of the cooperative tracking error of the i-th ship, is the velocity information in the x-axis direction, is the speed information in the y-axis direction; a ij The graph adjacency matrix i Rank j Column element, E i For the i The expected formation information of the ships, E j For the j The expected formation information of the ships, b i is the restraint gain; q0 is the position information of the desired trajectory, w0 is the speed information of the desired trajectory; w j For the j The speed information of the ship; id =b i +d i in , where d i in For the i The entry degree of the boat; S i is the fixed time terminal sliding surface, and the calculation formula is as follows: ； in, and ； in m =1, 2; constant a is known s >0,b s >0,r1>1,1 <r2<2, is a positive number; ; ; is the equivalent control part, It is the fixed time control part; ， ， parameter , α n , β n , r3, r4 are both positive numbers, , ; is the input saturation compensation term; as follows: ； in: m= 1,2, is a parameter to be designed; is a non-negative function and satisfies hour ; Step S6: Construct a fixed threshold event trigger mechanism, design a distributed event trigger-terminal sliding mode controller in combination with the sliding mode belt idea, and realize the coordinated tracking control of the multi-underactuated unmanned vessel system through the output instructions of the controller.

2. The control method according to claim 1, characterized in that: The step S1 comprises the following steps: Establish a dynamic mathematical model of a three-degree-of-freedom multi-underactuated unmanned vessel system under input saturation constraints; Performing order-up and dimension-down on the motion state parameters in the dynamic mathematical model to construct an equivalent all-wheel drive model of the underactuated unmanned ship system; A saturated input constraint is introduced into the formula of the all-wheel drive model.

3. The control method according to claim 1, wherein: The difference Δτ caused by the saturated input i The calculation formula is: ； in, is the control input of the all-wheel drive model; τ u i is the control input generated by the thruster under saturation constraint, τ r i is the control input generated by the servo under saturation constraint; τ ci =[τ ci,1 , τ ci,2 ] T is the control input of the unmanned ship under saturation constraint, τ ci,1 is the control input generated by the thruster without saturation constraint, τ ci,2 is the control input generated by the servo without saturation constraint, Δτ u i is the control input τ under saturation constraint u i With the control input τ without saturation constraint ci,1 The difference, Δτ r i is the control input τ under saturation constraint r i With the control input τ without saturation constraint ci,2 and With input saturation constraints, the following formula is satisfied: ； ； Among them, τ max is the saturation upper limit of the system input, τ min It is the lower saturation limit of the system input.

4. The control method according to claim 1, wherein: The fixed threshold event trigger mechanism is designed as follows: ； where t k+1 i,m represents the triggering time of the k+1th event of the mth control input of the i-th unmanned ship; t k i,m Indicates the triggering time of the kth event of the mth control input of the i-th unmanned ship; m=1,2; t At time t, the event trigger error function of the mth control input of the i-th unmanned ship is ,in for t The time-fixed terminal sliding mode control law, σ im is a positive constant, when e im The absolute value of (t) is greater than σ im When the controller is updated, it is triggered, otherwise it is not updated.

5. The control method according to claim 1, characterized in that: The adaptive update rule for the estimated value is: ； The observation error , is the speed observation error of the i-th unmanned ship in the x-axis direction, is the speed observation error of the i-th unmanned ship in the y-axis direction, μ1 and μ2 are positive constants, representing the learning rate and damping coefficient of weight update, respectively.

6. A control system for a multi-underactuated unmanned vessel system under input saturation constraint, characterized in that: To implement the method according to any one of claims 1 to 5, comprising: A model conversion module, which is used to convert the mathematical model of the multiple underactuated unmanned ship system into an all-wheel drive model; The disturbance observation module estimates the environmental disturbance through a designed fixed-time disturbance observer; An input saturation estimation module estimates the input saturation term of the multiple underactuated unmanned vessel system using a fixed-time adaptive RBF neural network observer; The controller design execution module is used to design a fixed-time sliding surface and a fixed-threshold event trigger mechanism to obtain a controller, and is also used to output control instructions to achieve collaborative tracking control of multiple underactuated unmanned vessels; Communication module, used for communicating with external devices.

7. An electronic device, characterized in that: include: a processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executable instructions; the transceiver is used to transmit and receive data; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

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