CN119292046A - Finite-time distributed formation method for unmanned surface vehicle swarm system - Google Patents
Finite-time distributed formation method for unmanned surface vehicle swarm system Download PDFInfo
- Publication number
- CN119292046A CN119292046A CN202311162377.2A CN202311162377A CN119292046A CN 119292046 A CN119292046 A CN 119292046A CN 202311162377 A CN202311162377 A CN 202311162377A CN 119292046 A CN119292046 A CN 119292046A
- Authority
- CN
- China
- Prior art keywords
- representing
- function
- unmanned surface
- matrix
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
提供一种无人水面艇集群系统有限时间分布式编队方法，不仅能保证无人水面艇系统在有限时间达到期望的编队模式，而且能确保系统速度和编队误差在整个运行期间始终满足时变约束需求，该方法包括下列步骤：建立三自由度无人水面艇运动学和动力学模型；利用光滑函数近似系统的饱和约束，并结合故障模型建立受制于驱动器故障以及饱和约束的新的系统动力学模型；设计虚拟控制律又称运动学指导律；设计虚拟领导者的参数化通道速率；设计有限时间分布式编队控制器；设计不确定补偿自适应律。本发明可根据使用者的需要，设计时变编队模式以及时变约束函数，动态地调整多无人水面艇的编队形式，同时确保速度和编队跟踪误差始终不偏离指定的约束集。
A finite-time distributed formation method for an unmanned surface vessel cluster system is provided, which can not only ensure that the unmanned surface vessel system reaches a desired formation mode in a finite time, but also ensure that the system speed and formation error always meet the time-varying constraint requirements during the entire operation period. The method includes the following steps: establishing a kinematic and dynamic model of a three-degree-of-freedom unmanned surface vessel; using a smooth function to approximate the saturation constraint of the system, and combining a fault model to establish a new system dynamic model subject to driver faults and saturation constraints; designing a virtual control law, also known as a kinematic guidance law; designing a parameterized channel rate of a virtual leader; designing a finite-time distributed formation controller; and designing an uncertain compensation adaptive law. The present invention can design a time-varying formation mode and a time-varying constraint function according to the needs of the user, dynamically adjust the formation form of multiple unmanned surface vessels, and ensure that the speed and formation tracking error never deviate from the specified constraint set.
Description
Technical Field
The invention relates to a finite time formation control method of an unmanned surface vessel system, in particular to a finite time distributed formation method of a finite time unmanned surface vessel cluster system, which takes speed constraint, collaborative formation error constraint and input saturation constraint into consideration.
Background
In recent years, the problem of formation control of multiple unmanned surface vessel (Unmanned Surface Vessels, USVs) systems has received attention because of their higher efficiency and performance than single vessels in completing complex tasks such as environmental monitoring, territorial sea monitoring, rescue, etc. And the development of a distributed formation control strategy based on limited time stabilization has important significance for enhancing the robustness of the multi-unmanned surface vessel system and improving the task completion efficiency of the multi-unmanned surface vessel system.
In an actual operating scenario, unmanned surface vessel systems are often faced with a wide variety of system constraints due to their performance specifications and requirements, including speed constraints, input or output constraints, and tracking error constraints, among others. If these constraints are ignored, the transient performance of the system may not be met and may even lead to system damage. However, due to the lack of a unified and effective nonlinear control method for processing constraint signals, the existing finite time formation control method （N. Gu, D. Wang, Z. H. Peng, and L. Liu, "Observer-based finite-time control for distributed path maneuvering of underactuated unmanned surface vehicles with collision avoidance and connectivity preservation," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 8, pp.5105-5115, Aug. 2021.） for realizing the unmanned ship cluster system only performs distributed controller design aiming at an unconstrained scene, cannot cope with the problem of limited system variables, and lacks practical value.
In addition, existing control methods （B. J. Guerreiro, C. Silvestre, R. Cunha, and A. Pascoal, "Trajectory tracking nonlinear model predictive control for autonomous surface craft," IEEE Trans. Control Syst. Technol., vol. 22, no. 6, pp. 2160-2175, Nov. 2014.）(Z. H. Peng, J. Wang, and D. Wang, "Distributed maneuvering of autonomous surface vehicles based on neurodynamic optimization and fuzzy approximation," IEEE Trans. Control Syst. Technol., vol. 26, no. 3, pp. 1083-1090, Mar. 2017.) that focus on system constraints also consider unmanned surface vessel system control with a single input constraint or speed constraint, and limit constraint signals that fall within a constant constraint range only enable asymptotic stabilization of the system convergence at infinite time. However, in a practical complex marine enqueuing task, the constraint requirements on system speed, input, etc. are often varied.
The actual modeling unmanned surface vessel system often has internal and external multisource uncertainty, multi-time-varying constraint requirements and complex task requirements, and even more complex marine environments are faced in the task implementation process, the risks of faults and the like are increased. The effective formation control algorithm with strong robustness, strong constraint and high convergence speed is designed, and is very important for promoting the safe and efficient operation of the multi-unmanned surface vessel system.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a finite time distributed formation method of an unmanned surface vessel cluster system considering multiple constraints, which comprises the following specific steps:
Step 1-consider the method of the first embodiment A network of unmanned surface vessel systems, whereinModeling is carried out on the unmanned surface vessel by adopting a geodetic coordinate system and a hull coordinate system respectively, so as to obtain the following kinematic and dynamic models:
(1),
equation (1) represents the first An unmanned surface vessel system, wherein: Is the first Position information of unmanned surface vessel under geodetic coordinate systemAnd heading angleThe vector of components, the superscript T represents the transpose of the vector or matrix,Representing vectorsWith respect to the derivative of time,Is a velocity vector consisting of surge, sway and yaw velocities in the hull coordinate system,Respectively representing forward speed, horizontal drift speed and bow swing angular speed; is an unknown bounded disturbance vector caused by wind, waves and surges, Respectively representing unknown bounded perturbations in different speed directions; Is the driving force subject to the following fault and saturation constraints, Respectively represent driving forces in three speed directions,
(2),
Equation (2) represents a failure model in whichRepresenting a matrix of loss efficiency and,Respectively represent the loss efficiency factors in different driving force directions and satisfy, Representing unknown bounded additive faults, saturating input vectorsIs determined by the saturation function defined below
(3),
Subscript in, Representing the actual control inputs in three speed directions respectively,AndIs a saturation function, two known constant boundaries, definitionWhich represents the control input vector actually to be designed, and, furthermore,Represents a known nonlinear function in whichRepresenting a coriolis force centripetal matrix and satisfying, AndRepresenting a matrix of known functions,Representing an unknown real number vector composed of imprecisely measurable parameters in a nonlinear suppression matrix of a system, a quality matrixIs positively symmetric and meetsMatrixRepresents a transfer matrix and has the following definition
,
Initial state of unmanned surface vessel systemWhereinThe function values of the three quantities at the initial time are respectively represented, and the parameterized path of the virtual leader is set as ,Respectively representing the parameterized track of the forward direction position, the horizontal drifting position and the heading angle direction,Representing the set path parameters, setting the desired convergence rate of the parameterized path toFirst, theThe expected formation track of unmanned surface vessel is,。
Step 2, utilizing nonlinear smooth functionEstimating the saturation function defined in equation (3):
(4),
The following approximate relationship is thus obtained according to the Lagrangian mean theorem:
(5) ,
Equation (5) represents a function approximation model, where Representing the approximation error and its absolute valueHaving an upper boundary valueSymbol(s)Representing a functionFor variableIs to satisfyWherein ,Is a weight coefficient to be selected and satisfies,For the real number to be selected, furthermore, the derivativeThe function value of (2) satisfiesWhereinIs a known positive constant.
Definition of diagonal matrixWhereinRespectively represent the nonlinear smoothing function in the formula (4)Defining a total error vector WhereinRespectively representing the estimation errors of the saturation functions in the three control force directions, and then, combining the fault model (2), saturating the input vectorAnd a saturation function approximation model (5) to which the first is applied-The unmanned surface vessel system (1) is rewritten as follows:
(6),
Wherein: Representation matrix Is used for the inverse matrix of (a),Representing the total external uncertainty of the system and being a bounded vector, i.eThe norm satisfiesWhereinRepresenting an unknown external disturbance boundary.
Step 3, respectively defining the tracking error vectors of the collaborative formationVirtual error coordinate transformationIs that
(7),
Wherein: Represents the first Personal agent and the firstThe weight of the adjacency matrix between the individual agents, in particular,Representing an agentAnd an agentA connection relation exists between the two terminals, otherwise, no communication relation exists; Representing the connection between the agent and the virtual leader, Equal to 1 represents that there is a connection relationship, otherwise represents that there is no connection relationship,, Respectively represent the firstAnd (b)The position information vector of the unmanned surface vessel,Representing the kinematic guidance law to be designed, also called virtual control law,Matrix in expression (1)Is a transpose of (2);
Step 4, constraint requirements of system formation errors and speed variables are given
,
In the middle ofRespectively represent given error variable normsVelocity variable normsThey are all defined as monotonically decreasing functions with the following finite time properties:
(8),
Wherein: , And Is a given normal number of times,Representing time, finite time。
Step 5, designing a virtual control law
(9),
Wherein:, And Is a positive control parameter to be designed,Is a positive number to be designed and is a number to be designed,,Respectively representWith respect to the derivative of time,Representation ofFor a pair ofFor convenience, simplified symbols are usedRepresenting constraint function termsAnd controls nonlinear termsDefined as the following piecewise function
(10),
Wherein the method comprises the steps ofIs a predetermined small positive parameter, process coefficient,。
Step 6, defining,Is thatWith respect to the derivative of time,Representing a desired reference channel, designing channel rate of a virtual leader using a filtered gradient method
(11),
In the middle ofAndIs a positive parameter to be designed;
Step 7, designing a nonlinear tracking differential observer to estimate (9) a virtual control law Time derivative of (2)
(12),
Wherein: Representation of Is used for the estimation of (a),Representative ofIs used for the estimation of (a),Representation ofIs used for the time derivative of (a),Representing the positive real parameters to be selected, and, in addition, for arbitrary vectors
Given normal numberDefinition of mathematical operation symbols WhereinRepresenting vectorsIs used for the control of the degree of freedom of the composition,Representation ofIs used as a function of the sign of (c),Representation ofAbsolute value of (2);
step 8, is the first Finite time control input vector for unmanned surface vessel system design
(13),
Wherein: And Is a positive control parameter to be designed, and simplified symbols are used for convenienceRepresenting constraint function terms,Representing unknown parametersIs used for the estimation of the signal of (a),Representing unknown external disturbance boundsNon-linear control functionThe following definitions are satisfied:
,
Wherein the method comprises the steps of Is a predetermined small positive parameter, process coefficient,. Furthermore, hyperbolic tangent diagonal function matrixThe definition is as follows:
In the middle of Nonlinear cross function vectorRespectively, by multiplying the unit vectors by,AndThe resulting component(s) is (are),Is three positive real numbers to be selected;
Step 9, designing an adaptive update law for compensating the uncertainty of the system to be
(14),
(15),
Wherein: All are normal numbers to be designed.
In one embodiment of the present invention, in step 2,。
The finite time distributed formation control scheme provided by the invention not only can ensure that a plurality of intelligent agents realize a desired formation mode in finite time, but also can ensure that the speed and the cooperative error vector are always in a specified constraint set under the conditions of internal and external multisource uncertainty, driver fault and multiple constraints. Compared with the prior art, the invention has the advantages that:
(1) For the condition that the multi-unmanned surface vessel system has multi-constraint variable and internal and external multi-source uncertainty, the step 4 establishes a monotonically decreasing function with limited time performance as a constraint upper bound to better meet the actual application requirements, but the design of a limited time controller is difficult. Steps 5 and 8 process the time-varying constraint by designing a virtual control law with a tangent barrier function and control inputs to enable a finite time distributed controller design with multiple time-varying constraint requirements and multiple uncertain couplings present.
(2) The invention introduces a smooth function as the approximation of a saturation function in the control design, processes saturation constraint and reduces the dynamic order of the system. In addition, the invention avoids solving the derivative of the virtual control law by means of a nonlinear differential tracker, thereby avoiding the problem of explosion of the computational complexity of the traditional back-stepping method. Therefore, the controller designed by the invention has the advantages of low complexity, strong robustness and high convergence speed, and most importantly, the speed and the formation error can be ensured not to deviate from the designated constraint set all the time during the whole operation.
Drawings
FIG. 1 shows a schematic diagram of a general control process;
FIG. 2 shows a two-dimensional plan view of a motion profile of a multi-unmanned surface vessel;
FIG. 3 shows a trace plot of the velocity variable norms;
FIG. 4 shows a trace plot of a formation error norm;
FIG. 5 shows a motion profile graph of an adaptive law norm;
fig. 6 shows a trajectory graph of the system control input norm.
Detailed Description
The invention provides a finite time distributed formation method of an unmanned surface vessel cluster system based on finite time stabilization, which can ensure that the speed and formation errors are not deviated from a specified time-varying constraint set all the time under the condition of uncertainty of internal and external multisources, and form a desired formation mode in finite time. The method comprises the following technical ideas of firstly designing a Liapunov (Lyapunov) function by utilizing a hyperbolic tangent function to ensure time-varying constraint requirements of a system, secondly, introducing a nonlinear continuous smooth function to approximate system input saturation, taking saturation errors, external disturbance of the system and additive faults as total external uncertain factors of the system, and then, iteratively designing a virtual control law and actual control input by utilizing an adaptive compensation strategy, and simultaneously introducing a nonlinear differential tracker to avoid solving derivatives of the virtual control law, thereby reducing complexity of a design algorithm and finally forming an effective finite time formation control scheme to realize expected system performance.
The method comprises the following specific steps:
Step 1-consider the method of the first embodiment A network of unmanned surface vessel systems, whereinModeling is carried out on the unmanned surface vessel by adopting a geodetic coordinate system and a hull coordinate system respectively, so as to obtain the following kinematic and dynamic models:
(1),
equation (1) represents the first An unmanned surface vessel system, wherein: Is the first Position information of unmanned surface vessel under geodetic coordinate systemAnd heading angleThe vector of components, the superscript T represents the transpose of the vector or matrix,Representing vectorsWith respect to the derivative of time,Is a velocity vector consisting of surge, sway and yaw velocities in the hull coordinate system,Respectively representing forward speed, horizontal drift speed and bow swing angular speed; is an unknown bounded disturbance vector caused by wind, waves and surges, Respectively representing unknown bounded perturbations in different speed directions; Is the driving force subject to the following fault and saturation constraints, Respectively represent driving forces in three speed directions,
(2),
Equation (2) represents a failure model in whichRepresenting a matrix of loss efficiency and,Respectively represent the loss efficiency factors in different driving force directions and satisfy, Representing unknown bounded additive faults, saturating input vectorsIs determined by the saturation function defined below
(3),
Subscript in, Representing the actual control inputs in three speed directions respectively,AndIs a saturation function, two known constant boundaries, definitionWhich represents the control input vector actually to be designed, and, furthermore,Represents a known nonlinear function in whichRepresenting a coriolis force centripetal matrix and satisfying, AndRepresenting a matrix of known functions,Representing an unknown real number vector composed of imprecisely measurable parameters in a nonlinear suppression matrix of a system, a quality matrixIs positively symmetric and meetsMatrixRepresents a transfer matrix and has the following definition
,
Initial state of unmanned surface vessel systemWhereinThe function values of the three quantities at the initial time are respectively represented, and the parameterized path of the virtual leader is set as ,Respectively representing the parameterized track of the forward direction position, the horizontal drifting position and the heading angle direction,Representing the set path parameters, setting the desired convergence rate of the parameterized path toFirst, theThe expected formation track of unmanned surface vessel is,。
Step 2, utilizing nonlinear smooth functionEstimating the saturation function defined in equation (3):
(4),
The following approximate relationship is thus obtained according to the Lagrangian mean theorem:
(5) ,
Equation (5) represents a function approximation model, where Representing the approximation error and its absolute valueHaving an upper boundary valueSymbol(s)Representing a functionFor variableIs to satisfyWherein
,Is a weight coefficient to be selected and satisfies,For the real number to be selected, furthermore, the derivativeThe function value of (2) satisfiesWhereinIs a known positive constant.
Definition of diagonal matrixWhereinRespectively represent the nonlinear smoothing function in the formula (4)Defining a total error vector WhereinRespectively representing the estimation errors of the saturation functions in the three control force directions, and then, combining the fault model (2), saturating the input vectorAnd a saturation function approximation model (5) to which the first is applied-The unmanned surface vessel system (1) is rewritten as follows:
(6),
Wherein: Representation matrix Is used for the inverse matrix of (a),Representing the total external uncertainty of the system and being a bounded vector, i.eThe norm satisfiesWhereinRepresenting an unknown external disturbance boundary.
Step 3, respectively defining the tracking error vectors of the collaborative formationVirtual error coordinate transformationIs that
(7),
Wherein: Represents the first Personal agent and the firstThe weight of the adjacency matrix between the individual agents, in particular,Representing an agentAnd an agentA connection relation exists between the two terminals, otherwise, no communication relation exists; Representing the connection between the agent and the virtual leader, Equal to 1 represents that there is a connection relationship, otherwise represents that there is no connection relationship,, Respectively represent the firstAnd (b)The position information vector of the unmanned surface vessel,Representing the kinematic guidance law to be designed, also called virtual control law,Matrix in expression (1)Is a transpose of (2);
Step 4, constraint requirements of system formation errors and speed variables are given
,
In the middle ofRespectively represent given error variable normsVelocity variable normsThey are all defined as monotonically decreasing functions with the following finite time properties:
(8),
Wherein: , And Is a given normal number of times,Representing time, finite time。
Step 5, designing a virtual control law
(9),
Wherein:, And Is a positive control parameter to be designed,Is a positive number to be designed and is a number to be designed,,Respectively representWith respect to the derivative of time,Representation ofFor a pair ofFor convenience, simplified symbols are usedRepresenting constraint function termsAnd controls nonlinear termsDefined as the following piecewise function
(10),
Wherein the method comprises the steps ofIs a predetermined small positive parameter, process coefficient,。
Step 6, defining,Is thatWith respect to the derivative of time,Representing a desired reference channel, designing channel rate of a virtual leader using a filtered gradient method
(11),
In the middle ofAndIs a positive parameter to be designed.
Step 7, designing a nonlinear tracking differential observer to estimate (9) a virtual control lawTime derivative of (2)
(12),
Wherein: Representation of Is used for the estimation of (a),Representative ofIs used for the estimation of (a),Representation ofIs used for the time derivative of (a),Representing the positive real parameters to be selected, and, in addition, for arbitrary vectors
Given normal numberDefinition of mathematical operation symbols WhereinRepresenting vectorsIs used for the control of the degree of freedom of the composition,Representation ofIs used as a function of the sign of (c),Representation ofAbsolute value of (2);
step 8, is the first Finite time control input vector for unmanned surface vessel system design
(13),
Wherein: And Is a positive control parameter to be designed, and simplified symbols are used for convenienceRepresenting constraint function terms,Representing unknown parametersIs used for the estimation of the signal of (a),Representing unknown external disturbance boundsNon-linear control functionThe following definitions are satisfied:
,
Wherein the method comprises the steps of Is a predetermined small positive parameter, process coefficient,. Furthermore, hyperbolic tangent diagonal function matrixThe definition is as follows:
In the middle of Nonlinear cross function vectorRespectively, by multiplying the unit vectors by,AndThe resulting component(s) is (are),Is three positive real numbers to be selected;
Step 9, designing an adaptive update law for compensating the uncertainty of the system to be
(14),
(15),
Wherein: All are normal numbers to be designed.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
According to the specific implementation steps of the technical scheme of the invention, the following embodiment is provided.
And 1, establishing an unmanned surface vessel system model shown in the formula (1). A directed network consisting of a virtual leader and five unmanned surface vessels is selected, and an adjacency matrix of the communication topology is defined as follows:, The remaining matrix elements are all 0. Setting initial position state of five unmanned surface vessel systems Respectively is, ,,AndDefining parameterized paths of virtual leaders asAnd a desired formation modeGiven an input saturation function parameter of,。
And 2, substituting the driver fault model (2) and the approximate model (4) of the saturation function into the model (1) to obtain a model (6).
Step 3, defining the cooperative formation error in the formula (7)Virtual control error。
Step 4, selecting a time-varying constraint function in the form of formula (8), wherein parameters in the constraint function are defined as And,。
Step 5, designing a virtual control law in the form of formula (9), wherein parameters of the virtual controller are selected as follows。
Step 6, designing the virtual leader channel rate as shown in formula (11)Part of the parameters of the controller are selected asThe desired virtual leader rate is selected as。
Step 7, designing a nonlinear tracking differential observer shown as a formula (12), wherein part of parameters of the observer are selected as, 。
Step 8, designing a finite time control input vector in the form of formula (13), wherein part of parameters of the controller are selected as。
Step 9, designing the adaptive law of the formulas (14) and (15), wherein the adaptive law parameters are selected as,
。
Fig. 2 shows a two-dimensional plan view of the unmanned surface vessel system motion profile formation, indicating that the desired time-varying formation pattern has been formed. Fig. 3 and 4 are running trajectories of a velocity norm and a formation error norm, respectively, and it can be seen from fig. 3 and 4 that the velocity and formation error do not deviate from a given set of time-varying constraints throughout the run. In addition, the formation error is finally converged to a small neighborhood of the origin, namely the constraint requirement of the system can be met by adopting the provided control algorithm. Fig. 5 shows that the parameter adaptation law of the proposed method is bounded as well as the external uncertainty adaptation law. As can be seen from fig. 6, the saturation input and the drive failure input are bounded and their range of values is relatively small, i.e., employing the proposed control method allows the system to be subject to both drive failure and saturation constraints. Under the uncertain and multi-time-varying constraint of internal and external multisources, the time-varying formation performance of the system can be realized, constraint requirements can be met, and the performance of the controller can be exerted to the maximum extent.
Claims (2)
1. The finite time distributed formation method of the unmanned surface vessel cluster system is characterized by comprising the following specific steps of:
Step 1-consider the method of the first embodiment A network of unmanned surface vessel systems, whereinModeling is carried out on the unmanned surface vessel by adopting a geodetic coordinate system and a hull coordinate system respectively, so as to obtain the following kinematic and dynamic models:
(1),
equation (1) represents the first An unmanned surface vessel system, wherein: Is the first Position information of unmanned surface vessel under geodetic coordinate systemAnd heading angleThe vector of components, the superscript T represents the transpose of the vector or matrix,Representing vectorsWith respect to the derivative of time,Is a velocity vector consisting of surge, sway and yaw velocities in the hull coordinate system,Respectively representing forward speed, horizontal drift speed and bow swing angular speed; is an unknown bounded disturbance vector caused by wind, waves and surges, Respectively representing unknown bounded perturbations in different speed directions; Is the driving force subject to the following fault and saturation constraints, Respectively represent driving forces in three speed directions,
(2),
Equation (2) represents a failure model in whichRepresenting a matrix of loss efficiency and,Respectively represent the loss efficiency factors in different driving force directions and satisfy, Representing unknown bounded additive faults, saturating input vectorsIs determined by the saturation function defined below
(3),
Subscript in, Representing the actual control inputs in three speed directions respectively,AndIs a saturation function, two known constant boundaries, definitionWhich represents the control input vector actually to be designed, and, furthermore,Represents a known nonlinear function in whichRepresenting a coriolis force centripetal matrix and satisfying, AndRepresenting a matrix of known functions,Representing an unknown real number vector composed of imprecisely measurable parameters in a nonlinear suppression matrix of a system, a quality matrixIs positively symmetric and meetsMatrixRepresents a transfer matrix and has the following definition
,
Initial state of unmanned surface vessel systemWhereinThe function values of the three quantities at the initial time are respectively represented, and the parameterized path of the virtual leader is set as ,Respectively representing the parameterized track of the forward direction position, the horizontal drifting position and the heading angle direction,Representing the set path parameters, setting the desired convergence rate of the parameterized path toFirst, theThe expected formation track of unmanned surface vessel is,;
Step 2, utilizing nonlinear smooth functionEstimating the saturation function defined in equation (3):
(4),
The following approximate relationship is thus obtained according to the Lagrangian mean theorem:
(5) ,
Equation (5) represents a function approximation model, where Representing the approximation error and its absolute valueHaving an upper boundary valueSymbol(s)Representing a functionFor variableIs to satisfyWherein ,Is a weight coefficient to be selected and satisfies,For the real number to be selected, furthermore, the derivativeThe function value of (2) satisfiesWhereinIs a known positive constant;
Definition of diagonal matrix WhereinRespectively represent the nonlinear smoothing function in the formula (4)Defining a total error vector WhereinRespectively representing the estimation errors of the saturation functions in the three control force directions, and then, combining the fault model (2), saturating the input vectorAnd a saturation function approximation model (5) to which the first is applied-The unmanned surface vessel system (1) is rewritten as follows:
(6),
Wherein: Representation matrix Is used for the inverse matrix of (a),Representing the total external uncertainty of the system and being a bounded vector, i.eThe norm satisfiesWhereinRepresenting an unknown external disturbance boundary;
Step 3, respectively defining the tracking error vectors of the collaborative formation Virtual error coordinate transformationIs that
(7),
Wherein: Represents the first Personal agent and the firstThe weight of the adjacency matrix between the individual agents, in particular,Representing an agentAnd an agentA connection relation exists between the two terminals, otherwise, no communication relation exists; Representing the connection between the agent and the virtual leader, Equal to 1 represents that there is a connection relationship, otherwise represents that there is no connection relationship,, Respectively represent the firstAnd (b)The position information vector of the unmanned surface vessel,Representing the kinematic guidance law to be designed, also called virtual control law,Matrix in expression (1)Is a transpose of (2);
Step 4, constraint requirements of system formation errors and speed variables are given
,
In the middle ofRespectively represent given error variable normsVelocity variable normsThey are all defined as monotonically decreasing functions with the following finite time properties:
(8),
Wherein: , And Is a given normal number of times,Representing time, finite time;
Step 5, designing a virtual control law
(9),
Wherein:, And Is a positive control parameter to be designed,Is a positive number to be designed and is a number to be designed,,Respectively representWith respect to the derivative of time,Representation ofFor a pair ofFor convenience, simplified symbols are usedRepresenting constraint function termsAnd controls nonlinear termsDefined as the following piecewise function
(10),
Wherein the method comprises the steps ofIs a predetermined small positive parameter, process coefficient,
;
Step 6, defining,Is thatWith respect to the derivative of time,Representing a desired reference channel, designing channel rate of a virtual leader using a filtered gradient method
(11),
In the middle ofAndIs a positive parameter to be designed;
Step 7, designing a nonlinear tracking differential observer to estimate (9) a virtual control law Time derivative of (2)
(12),
Wherein: Representation of Is used for the estimation of (a),Representative ofIs used for the estimation of (a),Representation ofIs used for the time derivative of (a),Representing the positive real parameters to be selected, and, in addition, for arbitrary vectors;
Given normal numberDefinition of mathematical operation symbols WhereinRepresenting vectorsIs used for the control of the degree of freedom of the composition,Representation ofIs used as a function of the sign of (c),Representation ofAbsolute value of (2);
step 8, is the first Finite time control input vector for unmanned surface vessel system design
(13),
Wherein: And Is a positive control parameter to be designed, and simplified symbols are used for convenienceRepresenting constraint function terms,Representing unknown parametersIs used for the estimation of the signal of (a),Representing unknown external disturbance boundsNon-linear control functionThe following definitions are satisfied:
,
Wherein the method comprises the steps of Is a predetermined small positive parameter, process coefficient,Furthermore, hyperbolic tangent diagonal function matrixThe definition is as follows:
In the middle of Nonlinear cross function vectorRespectively, by multiplying the unit vectors by,AndThe resulting component(s) is (are),Is three positive real numbers to be selected;
Step 9, designing an adaptive update law for compensating the uncertainty of the system to be
(14),
(15),
Wherein: All are normal numbers to be designed.
2. The limited time distributed formation method of an unmanned surface vessel cluster system of claim 1, wherein, in step 2,。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311162377.2A CN119292046A (en) | 2023-09-11 | 2023-09-11 | Finite-time distributed formation method for unmanned surface vehicle swarm system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311162377.2A CN119292046A (en) | 2023-09-11 | 2023-09-11 | Finite-time distributed formation method for unmanned surface vehicle swarm system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119292046A true CN119292046A (en) | 2025-01-10 |
Family
ID=94163407
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311162377.2A Pending CN119292046A (en) | 2023-09-11 | 2023-09-11 | Finite-time distributed formation method for unmanned surface vehicle swarm system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119292046A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007606A (en) * | 2019-05-28 | 2019-07-12 | 哈尔滨工程大学 | An Error Constrained Control Method for Surface Unmanned Vehicles Considering Input Saturation |
| CN113189979A (en) * | 2021-04-02 | 2021-07-30 | 大连海事大学 | Distributed queue finite time control method of unmanned ship |
| US20220004191A1 (en) * | 2020-07-01 | 2022-01-06 | Wuhan University Of Technology | Usv formation path-following method based on deep reinforcement learning |
| CN114035566A (en) * | 2021-09-08 | 2022-02-11 | 哈尔滨工程大学 | Design method, system and device of finite-time anti-saturation controller for unmanned boat |
| CN115617044A (en) * | 2022-10-28 | 2023-01-17 | 华中科技大学 | Non-singular finite time unmanned ship preset performance dynamic positioning control method and system |
| US11616460B1 (en) * | 2021-10-26 | 2023-03-28 | Guizhou University | Fuzzy finite-time optimal synchronization control method for fractional-order permanent magnet synchronous generator |
-
2023
- 2023-09-11 CN CN202311162377.2A patent/CN119292046A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007606A (en) * | 2019-05-28 | 2019-07-12 | 哈尔滨工程大学 | An Error Constrained Control Method for Surface Unmanned Vehicles Considering Input Saturation |
| US20220004191A1 (en) * | 2020-07-01 | 2022-01-06 | Wuhan University Of Technology | Usv formation path-following method based on deep reinforcement learning |
| CN113189979A (en) * | 2021-04-02 | 2021-07-30 | 大连海事大学 | Distributed queue finite time control method of unmanned ship |
| CN114035566A (en) * | 2021-09-08 | 2022-02-11 | 哈尔滨工程大学 | Design method, system and device of finite-time anti-saturation controller for unmanned boat |
| US11616460B1 (en) * | 2021-10-26 | 2023-03-28 | Guizhou University | Fuzzy finite-time optimal synchronization control method for fractional-order permanent magnet synchronous generator |
| CN115617044A (en) * | 2022-10-28 | 2023-01-17 | 华中科技大学 | Non-singular finite time unmanned ship preset performance dynamic positioning control method and system |
Non-Patent Citations (2)
| Title |
|---|
| CHUHAN ZHOU: "Event-Based Fuzzy Adaptive Consensus Tracking for Stochastic High-Order Nonlinear Multiagent Networks With Specified-Time Convergence", IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 31, no. 8, 16 January 2023 (2023-01-16), pages 2694 - 2708, XP011946276, DOI: 10.1109/TFUZZ.2023.3235402 * |
| 董泽洪；等: "考虑输入受限的高超声速飞行器非奇异固定时间自适应切换控制", 系统工程与电子技术, vol. 45, no. 5, 31 May 2023 (2023-05-31), pages 1476 - 1488 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113189979B (en) | Finite time control method for distributed queue of unmanned ship | |
| CN110362095B (en) | Design method of finite time convergence unmanned ship cooperative controller | |
| CN108445892B (en) | An underactuated unmanned ship formation controller structure and design method | |
| CN112965371B (en) | Water surface unmanned ship track rapid tracking control method based on fixed time observer | |
| CN104898688B (en) | The adaptive anti-interference System with Sliding Mode Controller of UUV four-degree-of-freedom dynamic positionings and control method | |
| CN111752280A (en) | A fixed-time control method for multi-unmanned ship formation based on finite-time uncertain observer | |
| CN112612209A (en) | Full-drive ship track tracking control method and system based on instruction filtering neural network controller | |
| CN110032197A (en) | Unmanned ship path tracking control method based on limited time guidance and control | |
| CN109189103B (en) | An underactuated AUV trajectory tracking control method with transient performance constraints | |
| CN114296449B (en) | Water surface unmanned ship track rapid tracking control method based on fixed time H-infinity control | |
| CN110377034A (en) | A kind of waterborne vessel track following Global robust sliding-mode control based on dragonfly algorithm optimization | |
| CN113848887A (en) | Under-actuated unmanned ship trajectory tracking control method based on MLP method | |
| CN114564015A (en) | Distributed formation control method for under-actuated unmanned ship in rejection environment | |
| CN114115262A (en) | Multi-AUV actuator saturated collaborative formation control system and method based on azimuth information | |
| CN114442640A (en) | A kind of trajectory tracking control method of surface unmanned boat | |
| CN119620762B (en) | Multi-unmanned ship cluster collaborative tracking control method based on data driving | |
| CN117369267A (en) | Method and system for controlling underwater helicopter surrounding formation under event triggering frame | |
| CN120255503A (en) | Distributed cooperative dynamic target orbiting control method for unmanned vehicles under multiple constraints | |
| CN109240317B (en) | Finite-time configuration inclusion control method for submarine geophone flight nodes considering propeller failure | |
| CN119126565B (en) | AUV second-order sliding mode three-dimensional track tracking control method based on preset time interference observer | |
| CN115685739A (en) | A Finite Time Tracking Control Method Triggered by Events of Underactuated Ships | |
| CN119292046A (en) | Finite-time distributed formation method for unmanned surface vehicle swarm system | |
| CN118466501A (en) | Model-free parameter intermittent communication saturation control method for unmanned surface vehicle | |
| CN109240081B (en) | Finite time configuration containing fault-tolerant control method of ocean bottom seismic demodulation flight node considering error constraint | |
| CN112987758B (en) | A multi-surface vehicle cooperative tracking formation control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |