Unmanned ship preset time affine formation control method

Abstract

The application discloses a preset time affine formation control method of unmanned surface vehicle, and belongs to the technical field of unmanned surface vehicle cooperative control. The method acquires measurable position and yaw angle information of a follower unmanned surface vehicle, neighborhood communication information and input amplitude limiting range; generates a composite desired state according to preset task trajectory parameters, and constructs a composite weight matrix; generates an observation estimation according to the measurable position and yaw angle information, actual control input and coordinate rotation matrix; generates input saturation error and auxiliary compensation vectors according to ideal control input and actual control input; and according to the observation estimation, the neighborhood communication information, the composite weight matrix and the auxiliary compensation vectors, performs weighted compensation on an estimated state difference value between the current follower unmanned surface vehicle and neighbor unmanned surface vehicles, generates a conversion error variable; and generates actual control input according to the conversion error variable and a preset time parameter, so as to control the follower unmanned surface vehicle to perform affine formation tracking.

Description

Technical Field

[0001] This invention belongs to the field of unmanned surface vessel (USV) cooperative control technology, specifically a method for pre-set time affine formation control of USVs. Background Technology

[0002] With the increasing demand for unmanned collaborative operations in tasks such as port patrols, maritime search and rescue, environmental monitoring, and narrow waterway passage, unmanned surface vessel (USV) formation control technology is gradually being applied to multi-vessel collaborative maneuvering scenarios. Existing formation control methods are typically based on displacement, distance, or orientation constraints, enabling multiple USVs to maintain a preset formation under communication topology constraints and follow a leader to complete collaborative navigation. These methods are mainly suitable for maintaining overall formation translation or local geometric constraints. When the mission requires the USV formation to undergo formation changes such as translation, rotation, scaling, or shearing during navigation, existing methods struggle to uniformly describe the expected planar position and yaw angle of each USV within the same formation tracking target, easily creating processing breakpoints between formation geometric changes and heading follow-up.

[0003] Affine formation control can describe formation changes through affine transformations, enabling unmanned surface vessels (USVs) to perform complex maneuvers while maintaining overall geometric relationships. However, in real-world marine environments, USVs are affected by wind, waves, currents, and unmodeled hydrodynamics, resulting in unknown disturbances to the controlled object. Furthermore, due to limitations in speed sensor cost, fault conditions, or installation, the vessel's velocity state may not be directly measurable. If existing control methods still rely on fully measurable or accurate dynamic models, the velocity state cannot be stably used as feedback data for control calculations, and the effects of unknown disturbances are difficult to convert into processing quantities that can be used for control compensation, thus creating data gaps between observed data and control inputs.

[0004] Furthermore, unmanned surface vessel (USV) propulsion systems have upper limits on thrust and torque output. When USVs encounter strong ocean currents, sharp turns, or rapid formation changes, the ideal control input calculated by the controller may exceed the physical limits of the propulsion system, leading to a deviation between the ideal control quantity and the actual execution quantity. If the existing formation control process does not handle these execution deviations, the actual propulsion output cannot fully respond to the controller's requests, and the control error will continue to propagate into subsequent navigation, making it difficult to maintain consistent formation tracking among the following USVs.

[0005] For example, in port patrol missions, multiple unmanned surface vessels (USVs) need to gradually transition from a laterally deployed formation to a longitudinally contracting formation suitable for navigating narrow waterways, and then deploy again upon entering open water. Existing formation control methods based on fixed distance or fixed bearing constraints typically only maintain the local relative relationships between adjacent USVs, making it difficult to simultaneously handle changes in the desired planar position and yaw angle of each USV during formation transitions. If some follower USVs cannot directly obtain their velocity status during navigation, and their thrusters reach their output limits under sharp turns or strong current conditions, a deviation will occur between the ideal control quantity calculated by the controller and the actual quantity executed by the thrusters. The formation error will be difficult to converge within the mission's allowed time, thus affecting the continuity of multi-vessel cooperative passage through waterways.

[0006] On the other hand, missions such as port patrols, search and rescue, and rapid water coverage typically require unmanned surface vessel (USV) formations to complete formation tracking within the mission-allowed timeframe. Traditional asymptotically stable control struggles to provide finite convergence times; the convergence time of finite-time control is usually related to the initial state; while fixed-time control can reduce the impact of the initial state on the convergence time, the upper bound of the convergence time is still not easily set directly according to the mission-allowed formation adjustment time. Therefore, existing USV formation control methods, under conditions of only measuring position and yaw angle, the presence of unknown ocean disturbances, and thruster input limitations, still face the technical challenge of failing to complete affine formation tracking control within the mission-preset timeframe. Summary of the Invention

[0007] To address the above problems, this invention provides a preset-time affine formation control method for unmanned surface vessels (USVs). This method involves processing the measurable position and yaw angle information, neighborhood communication information, and input limiting range of the follower USV. This process includes generating a composite desired state, constructing a composite weight matrix, generating observation estimates, generating input saturation error and auxiliary compensation vectors, generating transformation error variables, and generating preset-time sliding mode control inputs. This forms a continuous processing chain from state estimation, saturation compensation, error transformation to actual control input and output. Thus, preset-time tracking control of USV affine formations is achieved even under conditions where velocity state cannot be directly measured, unknown disturbances exist, and input saturation occurs.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: an unmanned surface vessel preset time affine formation control method, comprising: Acquire the measurable position and yaw angle information, neighborhood communication information, and input limiting range of the follower unmanned surface vessel; A time-varying affine transformation is generated based on the preset mission trajectory parameters to determine the desired plane position and desired yaw angle, thus obtaining the composite desired state. A composite weight matrix is ​​constructed using position affine weights and yaw angle adjacency weights; Based on the measurable position and yaw angle information, the actual control input obtained through the input limiting range, and the coordinate rotation matrix, an observation estimate is generated, which includes position and yaw angle estimates, velocity estimates, and unknown lumped disturbance estimates. An input saturation error is generated based on the ideal control input and the actual control input obtained through the input limiting range, and an auxiliary compensation vector is generated based on the composite weight matrix and the input saturation error. Based on the observed estimate, the neighborhood communication information, the composite weight matrix, and the auxiliary compensation vector, the estimated state difference between the current follower UAV and the neighboring UAV is weighted and compensated to generate a transformation error variable. A preset time sliding mode vector is constructed based on the conversion error variable, and an ideal control input is generated based on the preset time sliding mode vector and the preset time parameter. The ideal control input is then generated based on the preset time sliding mode vector, the unknown lumped disturbance estimate, and the auxiliary compensation vector. The ideal control input is then limited to the actual control input according to the input limit range to control the follower unmanned surface vessel to perform affine formation tracking.

[0009] Compared with related technologies, this application has the following advantages: This application generates a composite desired state, composite weight matrix, observation estimate, input saturation error and auxiliary compensation vector, transformation error variable, preset time sliding mode vector, ideal control input and actual control input in sequence, so that the desired state generation, neighborhood constraint processing, state estimation, input limiting compensation and preset time control in the affine formation tracking process of the unmanned surface vessel form a continuous processing chain. This improves the continuity and controllability of the follower unmanned surface vessel in performing affine formation tracking control under the conditions that the velocity state cannot be directly measured, there are unknown ocean disturbances and thruster input limiting.

[0010] By generating a time-varying affine transformation based on preset mission trajectory parameters, the desired plane position and desired yaw angle are determined. The desired plane position and desired yaw angle are then combined into a composite desired state, enabling the translation, rotation, scaling, shearing, and heading follow of the unmanned surface vessel formation to participate in subsequent control calculations within the same desired state object. This reduces the target description breakpoints caused by handling formation geometry changes and yaw angle follow separately.

[0011] By constructing a composite weight matrix using position affine weights and yaw angle adjacency weights, the affine constraint relationship in the plane position direction and the neighborhood communication relationship in the yaw angle direction can participate in the generation of auxiliary compensation vectors, transformation error variables, and ideal control inputs within the same weight object, thereby improving the processing consistency between position formation control and yaw angle cooperative control.

[0012] The observation estimates are generated based on measurable position and yaw angle information, actual control input obtained through input limiting range, and coordinate rotation matrix. These estimates include position and yaw angle estimates, velocity estimates, and estimates of unknown lumped disturbances. This allows the inability to directly measure velocity state and the influence of unknown ocean disturbances to be transformed into estimates that can be used in subsequent control input generation processes. The composite weight matrix is ​​used for subsequent input saturation auxiliary compensation, transformation error variable generation, and ideal control input generation, ensuring that position affine constraints and yaw angle neighborhood constraints remain consistent in error transformation and control law.

[0013] By generating an input saturation error based on the ideal control input and the actual control input obtained through the input limiting range, and generating an auxiliary compensation vector based on the composite weight matrix and the input saturation error, the deviation between the ideal control input and the actual control input caused by the thruster input limiting can be incorporated into the subsequent conversion error variable and ideal control input generation process, thereby reducing the impact of input saturation on the formation tracking error propagation process.

[0014] By using observation estimates, neighborhood communication information, composite weight matrix, and auxiliary compensation vector, the estimated state difference between the current follower UV and its neighboring UV is weighted and compensated to generate a transformation error variable. A preset time sliding mode vector is then constructed based on the transformation error variable, and a preset time sliding mode control law is constructed based on preset time parameters. This allows the affine formation error, state estimation results, and input saturation compensation results to be uniformly incorporated into the preset time sliding mode control structure, thereby enabling the formation tracking error of the follower UV to converge and be constrained according to the preset time parameters set by the mission.

[0015] By generating ideal control inputs based on preset time sliding mode vectors, unknown lumped disturbance estimates, and auxiliary compensation vectors, and then limiting these ideal control inputs to the actual control inputs according to the input limit range, the unknown disturbance compensation, input saturation compensation, and actual thruster execution constraints can all participate in the generation of the final control inputs, thereby improving the execution reliability of unmanned surface vessel affine formation tracking control under complex sea conditions and thruster limit conditions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the coordinate system of an unmanned surface vessel provided in an embodiment of the present invention.

[0017] Figure 2 This is a general framework diagram of the preset-time anti-saturation affine formation control method based on an adaptive fuzzy observer provided in an embodiment of the present invention.

[0018] Figure 3 The nominal formation configuration and communication topology of the unmanned surface vessel consisting of 3 navigators and 4 followers are provided for embodiments of the present invention.

[0019] Figure 4 This is a comparison diagram of the convergence trajectory of the sliding mode vector with and without an anti-saturation auxiliary compensation system, provided in an embodiment of the present invention.

[0020] Figure 5 This invention provides convergence trajectory diagrams of system errors under different initial conditions, as shown in the embodiments of the invention. Figure 5 In the diagram, 'a' represents the convergence trajectory of the glide vector under different initial conditions. Figure 5 In the diagram, b represents the convergence trajectory of the observation error under different initial conditions.

[0021] Figure 6 A two-dimensional motion trajectory diagram of an unmanned surface vessel formation performing an affine maneuver, provided in an embodiment of the present invention.

[0022] Figure 7 The present invention provides a schematic diagram of the control process of ideal control input limiting, actual control input generation, and thruster execution. Detailed Implementation

[0023] The embodiments of the present invention will now be described with reference to the accompanying drawings. In this application, an unmanned surface vessel (USV) is an unmanned surface vessel; a follower USV is an USV that performs affine formation tracking control according to its own onboard controller; and a navigator USV is an USV that provides parameters for the desired affine formation trajectory or a preset mission trajectory. The term "unmanned surface vessel" will be used consistently thereafter.

[0024] In this embodiment, the communication topology, adjacency matrix, balance force matrix, coordinate rotation matrix, saturation function, arctangent function, Gaussian membership function, and the sign function and exponentiation in sliding mode control can all be implemented using existing mathematical tools in the art. These mathematical tools themselves are prior art; the improvement of this application lies not in the individual mathematical tools themselves, but in generating the composite desired state, composite weight matrix, observation estimate, input saturation error, auxiliary compensation vector, transformation error variable, and preset time sliding mode vector in a continuous processing chain, and thereby forming the actual control input for affine formation tracking of the follower unmanned surface vessel.

[0025] like Figure 1 As shown, the unmanned surface vessel (USV) has a geocentric coordinate system and a hull coordinate system. The geocentric coordinate system is used to represent the USV's planar position and yaw angle, while the hull coordinate system is used to represent the velocity, force, and torque under the action of the propellers. The coordinate rotation matrix is ​​used to transform control-related quantities between the geocentric and hull coordinate systems.

[0026] like Figure 2As shown, the preset-time anti-saturation affine formation control method of this embodiment is executed in a distributed manner by the onboard controllers of each follower UAV. After each follower UAV reads its measurable position and yaw angle information, neighborhood communication information, and input limiting range, it sequentially generates the composite desired state, composite weight matrix, observation estimate, input saturation error, auxiliary compensation vector, transformation error variable, preset-time sliding mode vector, ideal control input, and actual control input.

[0027] like Figure 3 As shown, the unmanned surface vessel (USV) formation includes a leader USV and follower USVs. The USV communication topology is represented by an undirected connected graph, where nodes represent USVs and edges represent communication connections between USVs. Neighborhood communication information includes the identifiers of neighboring USVs with which the current follower USV has a communication connection, estimated positions and yaw angles, estimated speeds, position observation errors, actual control inputs, and communication times. If the neighboring USVs do not directly transmit the estimated values, the neighborhood communication information includes equivalent information capable of generating the aforementioned neighborhood error terms. The input limiting range is determined by the rated output capabilities of the USV's thrusters in the longitudinal, lateral, and yaw directions, including upper and lower limits for longitudinal, lateral, and yaw inputs. When the input limiting range is invoked in the onboard controller, the directional components of the ideal control input are limited separately to obtain the actual control input.

[0028] S101, acquire the measurable position and yaw angle information of the follower unmanned surface vessel, neighborhood communication information, and input limiting range.

[0029] In this step, the processing objects are the follower UV's own measurement data, neighbor communication data, and thruster limiting data. Measurable position and yaw angle information consists of the follower UV's position and yaw angle vectors in the Earth coordinate system; the positioning device includes a Global Navigation Satellite System receiver, a real-time dynamic positioning device, or a combined navigation device; the attitude measurement device includes an inertial measurement unit, an electronic compass, or an attitude heading reference device; the positioning device outputs planar position information, the attitude measurement device outputs yaw angle information, and the onboard controller combines the planar position information and yaw angle information into measurable position and yaw angle information.

[0030] The neighborhood communication information includes at least the neighbor UAV identifier, the estimated position and yaw angle of the neighbor UAV, the estimated speed, the position observation error, the actual control input, and the communication time. When the neighbor UAV does not directly send the above estimates, the neighborhood communication information includes the measurable position and yaw angle information sent by the neighbor UAV, the control input, the observer parameters, and the communication time, so that the current follower UAV can generate the neighborhood error term required for subsequent auxiliary compensation vectors, transformation error variables, and compensation terms within its own control cycle.

[0031] In order to indicate the first The measurable position and yaw angle information of an unmanned surface vessel are obtained using the following formula: in, Indicates the first The position and yaw angle vector of the unmanned surface vessel; subscript Indicates the unmanned surface vessel's (USV) number; Indicates the first The position of the unmanned surface vessel in the first plane direction in the Earth coordinate system; Indicates the first The position of the unmanned surface vessel in the second plane direction in the Earth coordinate system; Indicates the first The yaw angle of an unmanned surface vessel; This indicates that the objects within the parentheses will be arranged in columns; This represents a three-dimensional real vector space, where each of the three dimensions corresponds to two planar position components and one yaw angle component.

[0032] In some implementations, to illustrate the process of forming measurable position and yaw angle information, for example, the first... The positioning device of the unmanned surface vessel outputs its position in the first plane direction during the current control cycle as follows: The position of the second plane direction is The attitude measurement device outputs a yaw angle of 1. The onboard controller will then combine the above three measurements into... The above values ​​are only used to illustrate how the measurement data is combined into measurable position and yaw angle information, and do not limit the actual navigation position or attitude range of the unmanned surface vessel.

[0033] In this step, the measurable position and yaw angle information are obtained by the unmanned surface vessel's positioning device and attitude measurement device; the velocity state is not used as a direct measurement input, but is generated by observation estimation in step S104.

[0034] Neighborhood communication information is read by the current follower UAV through the communication topology; neighboring UAVs in the communication topology are those with communication connections to the current follower UAV. Neighborhood communication information includes at least the neighboring UAV's identifier, estimated position and yaw angle, estimated velocity, position observation error, actual control input, and communication time. When the neighboring UAV does not directly transmit the above estimates, the neighborhood communication information includes the measurable position and yaw angle information, control input, observer parameters, and communication time transmitted by the neighboring UAV, enabling the current follower UAV to generate the necessary neighborhood error terms for subsequent auxiliary compensation vectors, transformed error variables, and compensation terms within its control cycle. The onboard controller reads neighborhood communication information whose communication times fall within a preset effective communication time range within a control cycle and uses this information for generating subsequent observation estimates and compensation terms.

[0035] The input limiting range is formed according to the upper and lower limits of the thruster's longitudinal thrust, lateral thrust, and yaw moment. The input limiting range can be determined from the thruster rated parameter table, the thruster calibration parameters pre-stored in the onboard controller, or the currently available output range fed back from the thruster control unit. For subsequent limiting processing, the first... The lower and upper limits of the input for each unmanned surface vessel are expressed as follows: in, Indicates the first The input lower bound vector of an unmanned surface vessel, where the underscore indicates the lower bound; Indicates the first The upper limit vector of input for each unmanned surface vessel, with the overline indicating the upper limit; and They represent the first The lower and upper limits of the longitudinal control input for an unmanned surface vessel; and They represent the first The lower and upper limits of the lateral control inputs for an unmanned surface vessel; and They represent the first The lower and upper limits of the yaw control input for an unmanned surface vessel; subscript , and These correspond to the longitudinal, lateral, and yaw directions, respectively.

[0036] In some implementations, to illustrate how the input limiting range is invoked, for example, the allowable range for longitudinal control input is... The allowable range of lateral control input is The allowable range of yaw control input is Then the lower bound vector of input is The input upper limit vector is The above values ​​are used to illustrate how the input limiting range is used as a comparison object for subsequent limiting processing, and do not constitute a limitation on the rated output capacity of the thruster.

[0037] This step outputs measurable position and yaw angle information, neighborhood communication information, and input limiting range; the measurable position and yaw angle information and neighborhood communication information are used by step S104 to generate observation estimates, and the input limiting range is used by step S105 to generate actual control input and input saturation error.

[0038] S102, generate a time-varying affine transformation based on the preset mission trajectory parameters, determine the desired plane position and desired yaw angle, and obtain the composite desired state.

[0039] In this step, the object of processing is the preset mission trajectory parameters. These parameters are formed from the mission planning results and include the nominal position configuration, time-varying affine transformation matrix, translation vector, and trajectory time parameters. The nominal position configuration defines the baseline geometry of the unmanned surface vessel (USV) formation. The time-varying affine transformation matrix generates scaling, rotation, and shearing. The translation vector generates the overall translation. The trajectory time parameters determine the affine transformation amounts at each moment. The preset mission trajectory parameters can be determined by the mission start point, mission end point, mission duration, desired formation scale change, desired rotation angle change, desired shearing change, and translation trajectory function, and are invoked in the onboard controller or navigator trajectory generation unit according to the mission planning results. The values ​​of these parameters affect the generation results of the desired position and desired yaw angle for each USV.

[0040] To generate the desired planar position from the nominal position configuration, let the nominal position configuration of the unmanned surface vessel be: in, Indicates the nominal positional configuration of all unmanned surface vessels; superscript Indicates the nominal formation or standard formation; Indicates the first The two-dimensional position of an unmanned surface vessel in the nominal formation; Indicates the total number of unmanned surface vessels; Indicates from the first unmanned surface vessel to the... The locations of the same type of unmanned surface vessel are arranged sequentially; Indicates by A real vector space consisting of two-dimensional position vectors.

[0041] The time-varying affine transformation matrix generated based on the preset task trajectory parameters is denoted as follows: The translation vector is denoted as ;in, In The parentheses represent the affine transformation matrix. Indicates the current time; In The parentheses represent the translation vector. This indicates the current time. At the current moment... The desired time-varying positional configuration is generated by the following formula: in, This indicates that all unmanned surface vessels are at the current moment. The desired planar position configuration; superscript Indicates the expected object; express An identity matrix of order 1; Indicates the Kronecker product; express A 1-dimensional vector; This indicates that a time-varying affine transformation is applied to the nominal position configuration. ； This indicates that the same translation is applied to all unmanned surface vessels; the inputs to this formula are the nominal position configuration, the time-varying affine transformation matrix, and the translation vector, and the output is the desired planar position configuration.

[0042] In some implementations, to illustrate the process of generating the desired planar position configuration, for example, the two-dimensional nominal position of a certain unmanned surface vessel in a nominal formation is... The time-varying affine transformation matrix and translation vector at the current moment are respectively: The expected planar position of the unmanned surface vessel at the current moment is: in, Represents the time-varying affine transformation matrix at the current moment. Acting on the The nominal plane position of an unmanned surface vessel in the nominal formation The resulting affine transformation position; Used to describe the rotation, scaling, and shearing changes of formations. Used to indicate the first The reference position of an unmanned surface vessel before performing a time-varying affine transformation; if a translation vector is further superimposed... Then we get the first The desired planar position of an unmanned surface vessel at the current moment.

[0043] The above examples demonstrate that the time-varying affine transformation matrix alters the relative geometric relationships within the nominal position configuration, while the translation vector shifts the overall position of the unmanned surface vessel (USV) formation. The numerical values ​​in the examples are solely for illustrating the calculation process of the affine transformation and do not limit the actual mission trajectory.

[0044] To ensure the yaw angle aligns with the direction of motion of the desired position configuration, the desired yaw angle is generated based on the direction of motion of the center of mass of the desired planar position configuration. Let the center of mass of the desired planar position configuration be: in, Indicates the current time The centroid of the desired planar position configuration; subscript Indicates the center of mass; This indicates the desired position of the center of mass in the direction of the first plane; This indicates the desired position of the center of mass in the direction of the second plane; for and Taking the time derivatives separately, we get... and The dotted symbol above indicates the derivative with respect to time. This represents the desired velocity component of the center of mass in the first plane direction; This represents the desired velocity component of the center of mass in the direction of the second plane.

[0045] The desired yaw angle is generated by the following formula: in, Indicates the current time The expected yaw angle; This represents the arctangent function used to determine the heading angle based on the velocity components in two planar directions; the first input... The second input is the velocity component in the second plane direction of the center of mass. Let be the velocity component in the first plane direction of the center of mass; when and When all values ​​are below the preset speed threshold, the current control cycle uses the expected yaw angle from the previous control cycle. The preset speed threshold is set based on the positioning device's speed resolution and the controller's sampling cycle.

[0046] No. The composite desired state of an unmanned surface vessel is generated by the following formula: in, Indicates the first An unmanned surface vessel at the current moment The composite expected state; Indicates the first The desired planar position of an unmanned surface vessel; Represents the desired yaw angle; based on the composite desired state at consecutive time points, or for... and Perform time differentiation to generate the time change of the composite desired state. ;in, Indicates the first The rate of change of the composite desired state of the unmanned surface vessel over time is used in step S106 to generate the velocity-type transformation error variable.

[0047] like Figure 6 As shown, time-varying affine transformation, when applied to the nominal position configuration, can generate the desired planar position configuration that changes over time, enabling the unmanned surface vessel formation to exhibit trajectory changes of translation, scaling, shearing, and rotation in a two-dimensional motion plane. Figure 6 The two-dimensional trajectory in the model is derived from the composite desired state generated in this step, where the desired planar position corresponds to the change in formation position in the two-dimensional trajectory, and the desired yaw angle corresponds to the change in formation movement direction.

[0048] This step outputs the composite desired state and the time change of the composite desired state; the composite desired state is used by step S106 to generate position-type transformation error variables, and the time change of the composite desired state is used by step S106 to generate velocity-type transformation error variables.

[0049] S103 is a composite weight matrix constructed from position affine weights and yaw angle adjacency weights.

[0050] In this step, the processing objects are the nominal position configuration, the communication topology adjacency relationship, and the affine formation relationship used when generating the composite desired state in step S102. The position affine weight is determined by the equilibrium force matrix corresponding to the nominal position configuration and is used to describe the affine constraint relationship between each unmanned surface vessel in the planar position direction. The yaw angle adjacency weight is determined by the communication topology adjacency matrix and is used to describe the communicable relationship of yaw angle information between each unmanned surface vessel. The position affine weight and the yaw angle adjacency weight are derived from the nominal formation geometry relationship and the communication topology connection relationship, respectively. The two are merged in the composite weight matrix so that the position configuration and the yaw angle configuration participate in subsequent processing in the same matrix object.

[0051] The position affine weights are formed as follows: Based on the two-dimensional nominal positions of each UAV in the nominal position configuration, a set of neighboring UAVs is determined for each follower UAV; within this set of neighboring UAVs, a weight value is calculated that allows the nominal position of the current follower UAV to be represented by a weighted average of the nominal positions of its neighboring UAVs; the obtained weight values ​​are combined into a balance force matrix, and the position affine weights are read from the balance force matrix. This process ensures a correspondence between the position affine weights and the nominal position configuration, rather than arbitrarily setting them.

[0052] Let the positional affine weight matrix be: in, This represents the positional affine weight matrix; Indicates the first The unmanned surface vessel and the first Affine weights of positions among unmanned surface vessels; subscript Indicates the ID of the other unmanned surface vessel participating in the current calculation; let the communication topology adjacency matrix be: in, Represents the adjacency matrix of the communication topology; subscript Represents a graph or communication topology; Indicates the first The unmanned surface vessel and the first The yaw angle adjacency weight between unmanned surface vessels (USVs); when there is a communication connection between two USVs. The value is taken as non-zero according to the communication topology; when there is no communication connection between the two unmanned surface vessels... Set to zero. The yaw angle adjacency weight can be set to 1, or it can be set to a non-zero positive value based on communication quality or the reliability of neighbor information.

[0053] The composite weight matrix is ​​constructed as follows: in, This represents a composite weight matrix, with the overline indicating that it is a composite matrix formed by the combination of position affine weights and yaw angle adjacency weights. The two values ​​of 1 indicate that the position affine weights are applied to the two planar position components, and the value of 0 indicates that they are not applied to the yaw angle component. The third value 1 indicates that the yaw angle adjacency weight is applied to the yaw angle component, and the value 0 indicates that it is not applied to the two plane position components.

[0054] For the The unmanned surface vessel and the first The composite weighted block matrix of the unmanned surface vessel is as follows: in, Represents the first element in the composite weight matrix. The unmanned surface vessel and the first Block matrix between unmanned surface vessels; This indicates that the objects within the brackets form a diagonal matrix; two These correspond to two planar position components, respectively. Corresponding yaw angle component.

[0055] In some implementations, to illustrate the construction of the composite weighted block matrix, for example, the first... The unmanned surface vessel and the first The positional affine weights between the unmanned surface vessels are: The adjacency weight of the yaw angle is The corresponding composite weighted block matrix is: The above example demonstrates that the position affine weights act on both planar position components simultaneously, while the yaw angle adjacency weights act only on the yaw angle component. The weight values ​​in the example are only used to illustrate the configuration of different state components in the composite weight matrix.

[0056] This step outputs a composite weight matrix. This composite weight matrix is ​​used in step S105 to generate the auxiliary compensation vector, in step S106 to generate the transformation error variable, and in step S107 to generate the ideal control input.

[0057] S104, generate observation estimates based on measurable position and yaw angle information, actual control input, coordinate rotation matrix, and position observation error. The observation estimates include position and yaw angle estimates, velocity estimates, and unknown lumped disturbance estimates.

[0058] In this step, the objects being processed are the measurable position and yaw angle information, neighborhood communication information, and the composite weight matrix obtained in step S101; the observation estimates refer to the state estimation results generated by the adaptive fuzzy local state observer and used for subsequent control, including position and yaw angle estimates, velocity estimates, and unknown lumped disturbance estimates; to distinguish between actual measurements and estimates, the cap above will uniformly represent the estimation object in the following text.

[0059] In order to obtain an estimated state that can be used for control when the velocity state cannot be directly measured, the first... The observer inputs for each follower unmanned surface vessel (USV) are the measurable position and yaw angle information of the USV, the current actual control input, and the neighborhood error information read based on a composite weight matrix. The position observation error is generated by the following formula: in, Indicates the first Position observation error of the follower unmanned surface vessel; letter Indicates error; subscript This indicates the position and yaw angle state corresponding to the error; This indicates the measurable position and yaw angle information obtained in step S101; Indicates the first Estimates of the position and yaw angle of each follower unmanned surface vessel.

[0060] The adaptive fuzzy local state observer generates the estimated state using the following formula: in, This represents the rate of change of the estimated position and yaw angle over time; Indicates the first Speed ​​estimates of the follower unmanned surface vessel; letters This indicates the velocity state in the Earth coordinate system; This represents the rate of change of the speed estimate over time; Indicates the observer feedback gain; Indicates by the first yaw angle of unmanned surface vessel A defined coordinate rotation matrix; Indicates the first The inertial matrix of an unmanned surface vessel; superscript Indicates the inverse of a matrix; Represents ideal control input The actual control input after processing the input limiting range; Indicates the first Unknown lumped disturbance estimate of a follower unmanned surface vessel; Indicates the first The observer output of a follower unmanned surface vessel; Indicates the first The estimated position and yaw angle of the follower unmanned surface vessel are used to form the position observation error with the actual measurable output.

[0061] Observer feedback gain The convergence rate of the observation error is set based on the allowable convergence rate of the observation error and the calculation cycle of the onboard controller, and is called every time the observer updates. Its value affects the strength of the position observation error in estimating the state update; the allowable convergence rate of the observation error can be determined based on the measurement error of the positioning device, the attitude measurement error, and the sampling cycle of the controller.

[0062] The unknown lumped disturbance estimate is used to characterize the equivalent disturbance caused by unmeasured velocity, ocean wind, wave, and current disturbances, and unmodeled dynamics. The unknown lumped disturbance estimate is generated by a fuzzy logic system. in, Indicates the first Unknown lumped disturbance estimate of a follower unmanned surface vessel; , and Representing the fuzzy weight estimation vectors for the longitudinal, lateral, and yaw directions, respectively; uppercase Greek letters Represents the fuzzy weight vector; superscript Indicates the transpose of a matrix or vector; , and Fuzzy basis function vectors representing the longitudinal, lateral, and yaw directions, respectively; uppercase Greek letters. Represents a fuzzy basis function vector; the parentheses contain... This indicates that the fuzzy basis function vector is input with the velocity estimate.

[0063] The fuzzy basis function uses a Gaussian membership function. This is to avoid feedback gain with the observer. Confusion, appearing alone Represents the observer feedback gain, with subscripts and independent variables. Indicates the first Gaussian membership functions: in, Indicates the first Gaussian membership functions; Indicates the fuzzy rule number; This represents the input variable of the fuzzy logic system, taken from the velocity estimate. The corresponding components; Represents an exponential function; Indicates the first The center of a Gaussian membership function; Indicates the width of the Gaussian function; The denominator represents the square of the difference between the input variable and the center; This represents the normalization scale determined by the width of the Gaussian function.

[0064] The centers of the Gaussian membership functions are set at equal intervals based on the preset range of velocity estimates, or based on the distribution range of velocity estimates in historical navigation data; the width of the Gaussian function is set according to the distance between adjacent centers, ensuring continuous coverage of adjacent fuzzy basis functions within the velocity estimate variation range; the number of fuzzy rules is determined based on the number of segments in the velocity estimate distribution range. The fuzzy weight attenuation parameter and the adaptive adjustment parameter corresponding to the fuzzy basis function terms are determined based on the controller sampling period, the allowable rate of change of disturbance estimates, and the allowable variation range of the fuzzy weight estimation vector, and participate in the update of the fuzzy weight estimation vector in the fuzzy system weight adaptive update law; the position observation error is used for the estimated state update in the adaptive fuzzy local state observer and is not used as a direct input to the fuzzy system weight adaptive update law.

[0065] In some implementations, to illustrate how the Gaussian membership function is set, for example, the estimated velocity value in a certain direction is preset to a range of values. Divide the range into 5 equally spaced centers to obtain The width of the Gaussian function can be set according to the distance between adjacent centers. When input variables At that time, each Gaussian membership function generates its corresponding membership value according to the above formula, and combines them into a fuzzy basis function vector for that direction. This example is only used to illustrate how the center and width of the fuzzy basis functions are formed, and does not limit the actual speed range.

[0066] like Figure 5 As shown in b, the position observation error enters a neighborhood defined by a preset observation error range over time. The preset observation error range is determined by the positioning device error, the attitude measurement device error, the controller sampling period, and the mission-allowed attitude error. Figure 5 In this step, 'b' corresponds to the process of generating the observation estimate from the measurable position and yaw angle information, the actual control input, and the position observation error. It is used to explain that the observer continuously updates the position and yaw angle estimates, the speed estimates, and the unknown lumped disturbance estimates when the speed state cannot be directly measured. The convergence result of this observation error is used to support the subsequent step S106 in reading the observation estimate to generate the transformation error variable, rather than forming a new control step on its own.

[0067] This step outputs observation estimates. The observation estimates include position and yaw angle estimates, velocity estimates, and unknown lumped disturbance estimates, which are used in step S105 to generate the auxiliary compensation vector, in step S106 to generate the transformation error variable, and in step S107 to generate the ideal control input.

[0068] S105 generates an input saturation error based on the ideal control input and the actual control input obtained through the input limiting range, and generates an auxiliary compensation vector based on the composite weight matrix and the input saturation error.

[0069] In this step, the objects being processed are the ideal control input generated in the previous control cycle, the input limiting range obtained in step S101, the composite weight matrix obtained in step S103, and the observation estimate obtained in step S104; the ideal control input refers to the control input generated in step S107 and requested to be executed by the thruster; the actual control input refers to the control input after the ideal control input has been processed by the input limiting range.

[0070] Let the first The ideal control input for a follower unmanned surface vessel is: in, Indicates the first Ideal control input for a follower unmanned surface vessel; , and These represent the components of the ideal control input in the longitudinal, lateral, and yaw directions, respectively.

[0071] Actual control input is limited according to each component: in, Indicates the first The actual control input of the follower unmanned surface vessel; This indicates retrieving the minimum value of the object within the parentheses; This indicates that the maximum value of the object within the parentheses is taken. The above limiting formula means that: if the ideal control input component is less than the corresponding lower limit, the actual control input is taken from the corresponding lower limit; if the ideal control input component is greater than the corresponding upper limit, the actual control input is taken from the corresponding upper limit; if the ideal control input component is between the corresponding upper and lower limits, the actual control input is taken from the ideal control input component.

[0072] Input saturation error is generated by the following formula: in, Indicates the first Input saturation error of a follower unmanned surface vessel; uppercase Greek letters Indicates the difference or change; Indicates the actual control input; This represents the ideal control input; the inputs to this formula are the ideal control input and the actual control input, and the output is the input saturation error.

[0073] In some implementations, to illustrate the process of generating input saturation error, for example, the ideal control input is... The input lower bound vector is The input upper limit vector is The actual control input obtained after limiting is: The input saturation error is: The above example illustrates that the input saturation error is not an externally measured quantity, but is obtained by subtracting the ideal control input from the actual control input after processing the input limit range, and is called in the subsequent auxiliary compensation vector update.

[0074] To propagate the input saturation error to subsequent error transformation and control input generation processes, auxiliary compensation vectors are constructed based on the composite weight matrix. These auxiliary compensation vectors include position-type and velocity-type auxiliary compensation vectors. The position-type auxiliary compensation vector is denoted as... lowercase Greek letters Represents the auxiliary compensation vector, subscript This indicates the position and yaw angle state corresponding to the auxiliary compensation vector, with the subscript indicating the position and yaw angle state. Indicates the first A follower unmanned surface vessel. The velocity-type auxiliary compensation vector is denoted as... subscript This indicates the velocity state corresponding to the auxiliary compensation vector.

[0075] The auxiliary compensation vector is updated using the following formula: in, This represents the rate of change of the location-based auxiliary compensation vector over time. This represents the rate of change of the velocity-type auxiliary compensation vector over time. and To assist the system in convergence gain; Applying to position-class auxiliary compensation vectors, It applies to velocity-type auxiliary compensation vectors; Indicates the number to Summing the relevant terms for the unmanned surface vessel; Represents the first element in the composite weight matrix. The unmanned surface vessel and the first Block matrix between unmanned surface vessels; Indicates the first Position observation error of the unmanned surface vessel; Indicates the first The total weight matrix corresponding to each follower unmanned surface vessel; , and The meaning is consistent with the above; Used to adjust the strength of the neighborhood position observation error term entering the velocity class auxiliary compensation vector update process.

[0076] The convergence gain of the auxiliary system is set based on the allowable compensation rate of the input saturation error and the controller sampling period. The allowable compensation rate of the input saturation error is determined by the thruster's rated response time, the input limiting range, and the mission's allowable tracking error. When the rate of change of the input saturation error exceeds the preset compensation response requirement, the convergence gain of the auxiliary system is increased to enhance the response strength of the auxiliary compensation vector to the input saturation error. The preset compensation response requirement is jointly determined by the allowable duration of the input saturation error and the controller sampling period.

[0077] like Figure 4 As shown, in the case of thruster input saturation, if the auxiliary compensation vector is not generated and invoked, the input saturation error cannot enter the subsequent conversion error variable, and the convergence process of the preset time sliding mode vector is affected by the thruster limiting. In this embodiment, the input saturation error generated in this step enters the conversion error variable in step S106 via the auxiliary compensation vector, and further enters the preset time sliding mode vector and ideal control input generation process in step S107, so that... Figure 4The convergence trajectory of the sliding mode vector in the figure reflects the compensating effect of the auxiliary compensation vector on the input saturation effect; Figure 4 The technical processing results corresponding to the input saturation error and auxiliary compensation vector generated in this step are used to explain the calling relationship of the results in the subsequent preset time sliding mode control.

[0078] This step outputs the input saturation error and the auxiliary compensation vector. The input saturation error is used to generate the auxiliary compensation vector, which is then used in step S106 to generate the transformation error variable and in step S107 to generate the ideal control input.

[0079] S106, based on the observation estimate, neighborhood communication information, composite weight matrix and the auxiliary compensation vector, the estimated state difference between the current follower unmanned surface vessel and the neighboring unmanned surface vessel is weighted and compensated to generate a transformation error variable.

[0080] In this step, the objects processed are the composite desired state generated in step S102, the time change of the composite desired state generated in step S102, the observation estimate generated in step S104, the composite weight matrix generated in step S103, and the auxiliary compensation vector generated in step S105; the transformation error variable refers to the error object formed by unifying the affine formation configuration constraints, neighborhood communication relationships, observation estimates, and input saturation compensation quantities under the same error coordinate, including position-type transformation error variables and velocity-type transformation error variables.

[0081] The positional transformation error variable is generated by the following formula: The speed-type conversion error variable is generated by the following formula: in, Indicates the first The positional transformation error variable for each follower unmanned surface vessel; letters Indicates the transformed error variable; subscript This indicates the corresponding position and yaw angle status; Indicates the first The speed-class conversion error variable of the follower unmanned surface vessel; This represents the total number of unmanned surface vessels participating in the composite weighting calculation; Indicates the relationship between the composite weight matrix and the first... The unmanned surface vessel and the first The weight item corresponding to each unmanned surface vessel; subscript Indicates the corresponding speed state; Indicates the first Estimated position and yaw angle of the unmanned surface vessel; Indicates the first Speed ​​estimates for the unmanned surface vessel; Indicates the first Speed ​​estimates of the follower unmanned surface vessel; and These are the position-type auxiliary compensation vector and the velocity-type auxiliary compensation vector generated in step S105, respectively.

[0082] In the two formulas above, Used to indicate the current follower unmanned surface vessel and the [number]th [unit]. The difference in estimated position and yaw angle between the unmanned surface vessels. Used to indicate the current follower unmanned surface vessel and the [number]th [unit]. The speed estimation difference between individual unmanned surface vessels; through After weighting the above estimated differences, the corresponding auxiliary compensation vector is subtracted to obtain the position-type transformation error variables and velocity-type transformation error variables, which are then used to construct the preset time sliding mode vector.

[0083] In some implementations, to illustrate the generation process of the transformation error variable, for example, the current follower UAV corresponds to two neighboring UAVs, and the estimated state differences between the current follower UAV and the two neighboring UAVs on a certain state component are the first estimated state difference and the second estimated state difference, respectively. The corresponding composite weight components are the first composite weight component and the second composite weight component, respectively. The value of the position class auxiliary compensation vector on this state component is the corresponding compensation component. Then, the position class transformation error corresponding to this state component is the sum of the product of the first composite weight component and the first estimated state difference, the product of the second composite weight component and the second estimated state difference, and then the corresponding compensation component is subtracted.

[0084] S107: Construct a preset time sliding mode vector based on the transformation error variable, and generate an ideal control input based on the preset time sliding mode vector, preset time parameters, unknown lumped disturbance estimate and auxiliary compensation vector. Limit the ideal control input to the actual control input according to the input limit range, and control the follower unmanned surface vessel to perform affine formation tracking.

[0085] In this step, the objects processed are the transformation error variable generated in step S106, the unknown lumped disturbance estimate generated in step S104, the auxiliary compensation vector generated in step S105, and the preset time parameters set by the user according to task requirements; the preset time parameters are denoted as... ,in Indicates time, Indicates a preset; The upper bound of the time used to constrain formation error to enter the preset error neighborhood threshold range; the preset time parameter is set according to the maximum formation allowed by the task and is called in the preset time sliding vector and the preset time sliding control law; its value directly affects the strength of the convergence term in the control law.

[0086] The preset error neighborhood threshold is determined by the mission-allowed formation error, positioning device error, attitude measurement device error, and actuator minimum control resolution. When the absolute value of each element of the transformation error variable or the preset time sliding mode vector is not greater than the corresponding preset error neighborhood threshold, the formation error is determined to have entered the preset error neighborhood threshold range.

[0087] To ensure that the transformation error variables are incorporated into a control structure with preset time convergence, a preset time sliding mode vector is constructed based on the position-type and velocity-type transformation error variables: in, Indicates the first A preset time sliding mode vector for a follower unmanned surface vessel; This indicates a signed exponentiation function; when used with a vector, it represents element-wise calculation. This indicates that a signed square operation is performed on the velocity-type transformation error variable; This indicates that a signed first-order exponentiation is performed on the position-type transformation error variable; Indicates that the absolute values ​​of each element of the position-type transformation error variable are raised to the power of three; superscript This indicates that the elements within the parentheses are raised to the power of one-half.

[0088] Figure 5 As shown in a, under different initial conditions, after the conversion error variable generated in step S106 is constructed into a preset time sliding mode vector in this step, the preset time sliding mode vector enters the range limited by the preset error neighborhood threshold with time. Figure 5 The 'a' in this step corresponds to the running and changing process of the preset time sliding mode vector. It is used to explain the constraint effect of the preset time parameter on the upper bound of the sliding mode vector convergence time after participating in the construction of the sliding mode vector and the generation of the ideal control input. Different initial conditions only change the initial value of the transformation error variable, and do not change the way the preset time parameter is called in the control law.

[0089] Before generating the ideal control input based on the preset time sliding mode vector, first construct the input gain matrix: in, Indicates the first Input gain matrix of each follower unmanned surface vessel; letters This represents the gain relationship between the control input and the error dynamics; From the composite weight matrix, representing the first... The total weight matrix corresponding to each follower unmanned surface vessel; Determined by the currently measurable yaw angle; Indicates the first The inverse matrix of the unmanned surface vessel's inertial matrix; before generating the ideal control input, the input gain matrix is ​​checked for full rank; if the input gain matrix does not meet the preset full rank condition, the onboard controller will not reconstruct the composite weight matrix in the current control cycle, but will stop updating the ideal control input in the current control cycle and use the actual control input after input limiting range processing in the previous control cycle; in another embodiment, the onboard controller calls a pre-generated backup composite weight matrix, which is formed based on the same nominal position configuration, the same balance force matrix construction rule and the same communication topology constraint as the current composite weight matrix, and has passed the affine balance constraint check; the backup composite weight matrix is ​​used to generate the ideal control input for the current control cycle only when the input gain matrix corresponding to the backup composite weight matrix meets the preset full rank condition.

[0090] A preset set of available neighbors is used to screen for communication validity and state availability before generating the backup composite weight matrix. The backup composite weight matrix is ​​pre-formed based on the same nominal position configuration, the same balance force matrix construction rules, and the same communication topology constraints, and has passed affine balance constraint verification. When the full-rank verification fails, the onboard controller does not temporarily reselect neighbors and reconstruct the composite weight matrix in the current control cycle. Instead, it uses the actual control input after input limiting range processing from the previous control cycle, or, when the input gain matrix corresponding to the backup composite weight matrix meets the preset full-rank condition, it calls the backup composite weight matrix to generate the ideal control input for the current control cycle.

[0091] In this embodiment, the preset time sliding mode control law generates the ideal control input according to the following formula: in, Indicates the first The ideal control input for a follower unmanned surface vessel; the negative sign indicates that the direction of the control input is opposite to the direction of the sliding mode error convergence. The matrix representing the inverse of the input gain matrix; This represents the diagonal adjustment matrix constructed from the transformation error variables. This represents the inverse of the diagonal adjustment matrix; Used to adjust the scale of different error components in the control law; This represents the overall control gain of the preset time convergence term; Indicates the preset time parameter; This indicates the scaling relationship between the preset time parameter and the strength of the convergence term; This represents the sign function for each element of the preset time sliding mode vector; and This represents the control gain of two power-order convergent terms; and Indicates the power exponent parameter; and These represent taking the absolute value of each element of the preset time sliding mode vector and then performing... Power and Exponentiation; It represents the compensation term consisting of the unknown lumped disturbance estimate, the auxiliary compensation vector, and the actual control input in the neighborhood.

[0092] The generation method is as follows: first, generate auxiliary vectors based on position-type transformation error variables and velocity-type transformation error variables. ,in The signed square term of the velocity-type transformation error variable, the signed first power term of the position-type transformation error variable, the signed cubic term of the position-type transformation error variable, and the preset positive small quantity. Combined to form; then, based on the velocity category, transform each element of the error variable and... The ratio between the square roots of the corresponding elements is used to generate a diagonal matrix. ; the third-order identity matrix With diagonal matrix Adding them together yields the diagonal adjustment matrix. .

[0093] The generation method is as follows: At that time, first read the intermediate aggregated terms in the dynamics of the conversion error. Speed-type conversion error variables Position conversion error Location-type auxiliary compensation vector Auxiliary system convergence gain and auxiliary vectors Then Error variables transformed from velocity type and The resulting compensation component, along with the nonlinear compensation component generated by the derivative of the sliding surface, is combined to obtain... .

[0094] in, Indicates the first The intermediate aggregation variables of the follower unmanned surface vessel in the preset time sliding mode control law are used to generate the ideal control input; Indicates the first The dynamic aggregation term of the conversion error corresponding to each follower unmanned surface vessel is used to gather the unknown lumped disturbance estimate, auxiliary compensation vector and neighborhood communication information to form the compensation amount after participating in the control law calculation. Indicates the first The speed-class conversion error variable of the follower unmanned surface vessel; Indicates the first The position-class transformation error variable of the follower unmanned surface vessel; The absolute value squared term of the velocity-type transformation error variable, the signed first-order term of the position-type transformation error variable, the absolute value cubic term of the position-type transformation error variable, and the preset positive small quantity. Combination and formation; Indicates the first Position-class auxiliary compensation vectors for each follower unmanned surface vessel; This represents the convergence gain of the auxiliary system corresponding to the position-type auxiliary compensation vector; It represents a three-dimensional vector of uniformity; This represents element-wise multiplication. , and These represent element-wise absolute value operations on the corresponding vectors; Indicates to Squaring the absolute values ​​of each element; Indicates to The absolute values ​​of each element are each raised to the power of one-half. This indicates the number of the currently following unmanned surface vessel.

[0095] In this formula, the first term The first term is used to introduce the basic compensation amount in the dynamic conversion error; the second term is used to adjust the conversion error variables according to the velocity class. Nonlinear scaling compensation is performed; the third term is used to generate the nonlinear compensation component by differentiating the sliding surface based on the position-type transformation error variable, the velocity-type transformation error variable, and the position-type auxiliary compensation vector; by combining the above three types of components, the intermediate aggregate variable is obtained. This is used to generate ideal control inputs for subsequent preset time sliding mode control laws.

[0096] The power exponent parameter satisfies: Among them, the values ​​0 and 1 are used to limit the range of the power exponent parameter; A value less than 1 is used to enhance convergence in small error intervals; A value greater than 1 is used to enhance convergence over large error ranges; the control gain parameter is set according to the input limiting range, preset time parameter, and allowable convergence speed of the transformation error variable; the control gain parameter includes... , and All are positive values; their values ​​ensure that the ideal control input has convergence strength to drive the preset time sliding mode vector into the preset error neighborhood threshold range before amplitude limiting; compensation term The unknown lumped disturbance estimate is obtained from step S104, the auxiliary compensation vector is obtained from step S105, and the actual control input of the neighborhood is obtained through the neighborhood communication information in step S101.

[0097] In some implementations, to illustrate the setting method of the preset time parameter and the preset error neighborhood threshold, for example, search and rescue missions require the formation to... Once the formation adjustment is completed within the specified time, the preset time parameter will be set to... If the task allows for a planar position error of ; The positioning device error is Then, the preset error neighborhood threshold for the planar position direction can be set to a value that is not less than the positioning device error and not greater than the allowable planar position error of the task; the above example is used to illustrate the source of the preset time parameter and the preset error neighborhood threshold, and does not limit the time requirements and error requirements of specific tasks.

[0098] After generating the ideal control input, the ideal control input is limited to the actual control input according to the input limiting method in step S105: in, Indicates the first The actual control input of the follower unmanned surface vessel; superscript Indicates the actual object to be executed; This represents the result of processing the ideal control input after limiting its amplitude; the onboard controller sends the actual control input to the corresponding thruster, enabling the follower unmanned surface vessel to perform affine formation tracking based on the composite desired state.

[0099] like Figure 7 As shown, the ideal control input is first limited in the longitudinal, lateral and yaw directions according to the input limiting range to generate the actual control input; the actual control input is sent to the thruster by the onboard controller; the thruster outputs longitudinal thrust, lateral thrust and yaw moment according to the actual control input; the input limiting process generates input saturation error at the same time, and the input saturation error is returned to step S105 to update the auxiliary compensation vector; Figure 7 This corresponds to the closed-loop process of ideal control input limiting, actual control input generation, and thruster execution in this step.

[0100] Further integration Figure 6In this step, after the ideal control input is limited to the actual control input and sent to the thruster, the follower unmanned surface vessel tracks the composite desired state generated in step S102 according to the actual control input. Figure 6 The two-dimensional motion trajectory in the image reflects the continuous call results between the composite desired state, composite weight matrix, transformation error variable, preset time sliding mode vector and actual control input, which is used to illustrate that the follower unmanned surface vessel completes affine formation tracking during navigation.

[0101] At this point, the measurable position and yaw angle information, neighborhood communication information, and input limiting range output in step S101; the composite desired state output in step S102; the composite weight matrix output in step S103; the observation estimate output in step S104; the input saturation error and auxiliary compensation vector output in step S105; the transformation error variable output in step S106; and the preset time sliding mode vector, ideal control input, and actual control input output in step S107 form a continuous processing chain. The results generated in the previous step are all called upon in subsequent steps and are ultimately used to control the follower unmanned surface vessel for affine formation tracking.

[0102] It should be noted that, in this document, the terms "comprising," "including," and any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Specific examples have been used in this document to illustrate the principles and implementation methods of the present invention. These examples are merely for the purpose of helping to understand the method and core ideas of the present invention. The above descriptions are only preferred embodiments of the present invention. It should be pointed out that, due to the limitations of written expression and the objective existence of infinite specific structures, those skilled in the art can make several improvements, modifications, or variations without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner. These improvements, modifications, variations, or combinations, or the direct application of the concept and technical solution of the present invention to other situations without modification, should all be considered within the scope of protection of the present invention.

Claims

1. A preset-time affine formation control method for unmanned surface vessels, characterized in that, include: Acquire the measurable position and yaw angle information, neighborhood communication information, and input limiting range of the follower UAV. The neighborhood communication information includes the neighbor UAV's identifier, estimated position and yaw angle, estimated speed, position observation error, actual control input, and communication time. When the neighbor UAV does not directly transmit the above estimates, the neighborhood communication information includes the measurable position and yaw angle information, control input, observer parameters, and communication time transmitted by the neighbor UAV, enabling the current follower UAV to generate the necessary neighborhood error terms for subsequent auxiliary compensation vectors, transformation error variables, and compensation terms within its own control cycle. A time-varying affine transformation is generated based on the preset mission trajectory parameters to determine the desired plane position and desired yaw angle, thus obtaining the composite desired state. A composite weight matrix is ​​constructed using position affine weights and yaw angle adjacency weights; Based on the measurable position and yaw angle information, the actual control input obtained through the input limiting range, and the coordinate rotation matrix, observation estimates are generated. The observation estimates include position and yaw angle estimates, velocity estimates, and unknown lumped disturbance estimates. The input saturation error is generated based on the ideal control input and the actual control input obtained through the input limiting range, and an auxiliary compensation vector is generated based on the composite weight matrix and the input saturation error. Based on the observed estimate, the neighborhood communication information, the composite weight matrix, and the auxiliary compensation vector, the estimated state difference between the current follower UAV and the neighboring UAV is weighted and compensated to generate a transformation error variable. A preset time sliding mode vector is constructed based on the conversion error variable, and an ideal control input is generated based on the preset time sliding mode vector, preset time parameters, unknown lumped disturbance estimates, and auxiliary compensation vector. The ideal control input is then limited to the actual control input according to the input limit range to control the follower unmanned surface vessel to perform affine formation tracking.

2. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Acquire the measurable position and yaw angle information of the follower unmanned surface vessel, neighborhood communication information, and input limiting range, including: The planar position information is obtained through the positioning device of the follower unmanned surface vessel, and the yaw angle information is obtained through the attitude measurement device. The planar position information and yaw angle information are combined into measurable position and yaw angle information. Based on the communication topology, the estimated position and yaw angle, speed, position observation error, actual control input, and communication time of neighboring unmanned surface vessels can be read, or equivalent information that can generate the above-mentioned neighborhood error terms can be read to obtain neighborhood communication information. The input limiting range is formed based on the upper and lower limits of the rated output of the thruster in the longitudinal, lateral, and yaw directions.

3. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Based on the preset mission trajectory parameters, a time-varying affine transformation is generated to determine the desired plane position and desired yaw angle, resulting in a composite desired state, including: Generate the desired planar position configuration based on the nominal position configuration, the time-varying affine transformation matrix, and the translation vector; The desired yaw angle is generated based on the direction of the center of mass velocity of the desired planar position configuration; The desired planar position and desired yaw angle corresponding to each unmanned surface vessel are combined into a composite desired state, and the time change of the composite desired state is generated based on the composite desired state at consecutive time points.

4. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, A composite weight matrix is ​​constructed using position affine weights and yaw angle adjacency weights, including: The position affine weights are determined based on the equilibrium force matrix corresponding to the nominal position configuration, and the yaw angle adjacency weights are determined based on the communication topology adjacency matrix. The position affine weights are assigned to the two components corresponding to the planar position, and the yaw angle adjacency weights are assigned to the components corresponding to the yaw angle, thus obtaining the composite weight matrix.

5. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Generate observation estimates, including: The position observation error is generated based on the measurable position and yaw angle information and the estimated position and yaw angle values. The position observation error, the actual control input obtained by inputting the amplitude limit range, and the coordinate rotation matrix are input into the adaptive fuzzy local state observer to generate position and yaw angle estimates and velocity estimates; The speed estimate is used to call the fuzzy logic system to generate an unknown lumped disturbance estimate, so that the position and yaw angle estimates, speed estimates, and unknown lumped disturbance estimates together form the observation estimate.

6. The unmanned surface vessel preset time affine formation control method according to claim 5, characterized in that, Fuzzy logic systems generate estimates of unknown lumped disturbances, including: Using the corresponding components of the velocity estimate as input variables for the Gaussian membership function, a fuzzy basis function vector is generated based on the preset center and preset width. Based on the fuzzy weight estimation vector of the previous control cycle, the fuzzy basis function vector of the current control cycle, the fuzzy weight decay parameter, and the adaptive adjustment parameter corresponding to the fuzzy basis function term, the fuzzy weight change is generated, and the fuzzy weight change is used to update the fuzzy weight estimation vector of the previous control cycle to obtain the fuzzy weight estimation vector of the current control cycle. Multiplying the fuzzy weight estimation vector with the fuzzy basis function vector yields the unknown lumped disturbance estimation components for the translational and yaw directions, which are then combined into the unknown lumped disturbance estimation value.

7. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Generate input saturation error and auxiliary compensation vector, including: The actual control input is obtained by comparing each directional component of the ideal control input with the corresponding upper and lower limits in the input limiting range. Subtracting the actual control input from the ideal control input yields the input saturation error; Based on the input saturation error, composite weight matrix, neighborhood position observation error term, observer feedback gain, and auxiliary system convergence gain, update the position-type auxiliary compensation vector and the velocity-type auxiliary compensation vector, respectively.

8. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Generate transformation error variables, including: Based on the composite weight matrix, the difference between the estimated position and yaw angle between the current follower UAV and the neighboring UAV is weighted and compensated according to the position-type auxiliary compensation vector to generate a position-type transformation error variable. Based on the composite weight matrix, the speed estimate difference between the current follower UAV and the neighboring UAV is weighted and compensated according to the speed-type auxiliary compensation vector to generate a speed-type transformation error variable.

9. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Constructing a preset time sliding mode vector includes: Read the position-type conversion error variable and the velocity-type conversion error variable; perform a signed square operation on the velocity-type conversion error variable to obtain the velocity error nonlinear term; The position-type transformation error variable is subjected to signed first-order and signed third-order powers, and the sum of the two is subjected to element-wise first-order powers to obtain the position error nonlinear term. The velocity-type conversion error variable, the velocity error nonlinear term, and the position error nonlinear term are added together to generate a preset time sliding mode vector; wherein, the preset time parameter is called in the preset time sliding mode control law that generates the ideal control input.

10. The unmanned surface vessel preset time affine formation control method according to claim 1, characterized in that, Generating ideal control inputs includes: The input gain matrix is ​​generated based on the composite weight matrix, coordinate rotation matrix, and inertia matrix, and the input gain matrix is ​​then subjected to full-rank verification. When the input gain matrix satisfies the preset full rank condition, an ideal control input is generated based on the preset time sliding mode vector, preset time parameter, control gain parameter, power exponent parameter, unknown lumped disturbance estimate, auxiliary compensation vector and neighborhood communication information. When the input gain matrix does not meet the preset full rank condition, the ideal control input update of the current control cycle is stopped, and the actual control input after input limiting range processing in the previous control cycle is used, or the ideal control input is generated by calling the spare composite weight matrix that has been verified by affine balance constraints in advance.

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