A distributed platoon control method based on group function

Abstract

This application relates to a distributed formation control method based on group functions. The method includes: when the moving object is not a leader object, calculating deterministic driving parameters of the moving object using a deterministic driving parameter calculation model and the moving object's mass matrix, drag matrix, rolling resistance matrix, control parameters, and the driving parameters, drag matrix, and rolling resistance matrix of the preceding moving object; calculating uncertain driving parameters of the moving object using an uncertain driving parameter calculation model, the moving object's mass matrix, acceleration matrix, and a second control parameter; when the moving object is a leader object, calculating the leader driving parameters of the moving object based on the moving object's mass matrix, acceleration, and trajectory constraint model; and performing driving control on multiple moving objects included in the distributed formation based on the deterministic driving parameters, uncertain driving parameters, and leader driving parameters to achieve efficient and precise driving control.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a distributed formation control method, apparatus, computer device, and storage medium based on group functions. Background Technology

[0002] The development of intelligent connected vehicles has effectively solved the problem of insufficient perception capabilities of single vehicles. Through communication between vehicles or between vehicles and roadside units, information perceived by different vehicles or intelligent units can be shared, enabling collaborative tasks to be completed. At the same time, platooning of vehicles can reduce wind resistance for the entire convoy and greatly reduce vehicle fuel consumption.

[0003] In related technologies, the simplest lead-vehicle following method is generally used. However, if the two-dimensional queue is too long, the delay in vehicle communication will cause vehicle collisions. Therefore, it is necessary to use a non-lead-vehicle following method for platooning. Thus, determining the drive control status of each vehicle in the non-lead-vehicle following method is an urgent problem to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a distributed formation control method, device, computer equipment, and storage medium based on group functions that can accurately control multiple moving objects to address the above-mentioned technical problems.

[0005] Firstly, this application provides a distributed formation control method based on group functions. The distributed formation includes multiple moving objects, and the method includes:

[0006] When the moving object is not a leading object, the mass matrix, wind resistance matrix, rolling resistance matrix, and first control parameter of the moving object are obtained, as well as the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object of the moving object.

[0007] The deterministic driving parameters of the moving object are calculated using a deterministic driving parameter calculation model, along with the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the preceding moving object.

[0008] The uncertainty driving parameters of the moving object are calculated using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object, and the second control parameter.

[0009] When the moving object is a navigation object, the mass matrix and acceleration of the moving object are obtained, and the navigation driving parameters of the moving object are calculated based on the mass matrix, acceleration and trajectory constraint model of the moving object.

[0010] Based on the deterministic driving parameters, the uncertain driving parameters, and the navigation driving parameters, the driving control is performed on the multiple moving objects included in the distributed formation.

[0011] In one embodiment, the method further includes:

[0012] Obtain the motion object communication model corresponding to the distributed formation, and determine the previous motion object of the motion object based on the motion object communication model.

[0013] In one embodiment, the method further includes:

[0014] Calculate the distance between the moving objects, and calculate the distance error based on the distance;

[0015] The second derivative of the spacing error is calculated to obtain the derivative result;

[0016] Based on the derivative results and the standard dynamic model, the error dynamic model of the moving object is determined. The error dynamic model includes the mass matrix, control matrix, wind resistance matrix, and rolling resistance matrix of the moving object.

[0017] In one embodiment, calculating the distance between the moving objects includes:

[0018] The spacing between the moving objects is obtained based on the displacement of the preceding moving object, the displacement of the moving object, and the length of the moving object. The spacing includes the spacing in a first direction and the spacing in a second direction.

[0019] In one embodiment, the method further includes:

[0020] Based on the spacing of the moving objects, determine the spacing constraints;

[0021] The spacing constraint is transformed to obtain the boundaryless state parameters;

[0022] The error dynamics model is updated based on the unbounded state parameters to obtain the target error dynamics model.

[0023] In one embodiment, the method further includes:

[0024] Based on a preset group function calculation model, the velocity constraints of the distributed formation are determined, and the velocity constraints are used to ensure that the relative displacement between each moving object is maintained.

[0025] Based on the speed constraint, calculate the constraint following error;

[0026] Based on the constraint following error and the target error dynamics model, the deterministic driving parameter calculation model is calculated.

[0027] In one embodiment, the method further includes:

[0028] Determine the uncertainty constraints of the mass matrix of the moving object;

[0029] The uncertainty boundary of the moving object is determined according to the adaptive law, and the time-varying function form of the uncertainty boundary of the moving object is determined.

[0030] Based on the uncertainty constraint of the mass matrix of the moving object and the time-varying function form of the uncertainty boundary, the calculation model for the uncertainty driving parameters is determined.

[0031] Secondly, this application also provides a distributed formation control device based on a group function, wherein the distributed formation includes multiple moving objects, and the device includes:

[0032] The first acquisition module is used to acquire the mass matrix, wind resistance matrix, rolling resistance matrix, first control parameters of the moving object and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object when the moving object is a non-navigation object;

[0033] The first calculation module is used to calculate the deterministic driving parameters of the moving object by using a deterministic driving parameter calculation model and the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters of the moving object and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object of the moving object.

[0034] The second calculation module is used to calculate the uncertainty driving parameters of the moving object by using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object and the second control parameters;

[0035] The second acquisition module is used to acquire the mass matrix and acceleration of the moving object when the moving object is a navigation object, and to calculate the navigation driving parameters of the moving object based on the mass matrix, acceleration and trajectory constraint model of the moving object.

[0036] The drive control module is used to drive and control multiple moving objects included in the distributed formation based on the deterministic drive parameters, the uncertain drive parameters, and the navigation drive parameters.

[0037] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps described in the above embodiments.

[0038] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps described in the above embodiments.

[0039] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps described in the above embodiments.

[0040] The aforementioned distributed formation control method, apparatus, computer device, and storage medium based on group functions include the following steps: When the moving object is not a leader object, obtain the moving object's mass matrix, drag matrix, rolling resistance matrix, first control parameters, and the driving parameters, drag matrix, and rolling resistance matrix of the preceding moving object; calculate the deterministic driving parameters of the moving object using a deterministic driving parameter calculation model, the moving object's mass matrix, drag matrix, rolling resistance matrix, control parameters, and the preceding moving object's driving parameters, drag matrix, and rolling resistance matrix; calculate the uncertain driving parameters of the moving object using an uncertain driving parameter calculation model, the moving object's mass matrix, acceleration matrix, and second control parameters; when the moving object is a leader object, obtain the moving object's mass matrix and acceleration, and calculate the leader driving parameters of the moving object based on the moving object's mass matrix, acceleration, and trajectory constraint model; and perform drive control on multiple moving objects included in the distributed formation based on the deterministic driving parameters, uncertain driving parameters, and leader driving parameters. By employing this method, the driving force of each moving object can be accurately calculated, achieving efficient and precise drive control of the distributed formation. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a distributed formation control method based on group functions in one embodiment;

[0042] Figure 2a This is a schematic diagram of initially disordered vehicles in one embodiment;

[0043] Figure 2b This is a schematic diagram of a communication model for moving objects in one embodiment;

[0044] Figure 2c This is a flowchart illustrating the steps for determining the error dynamics model in one embodiment;

[0045] Figure 3 This is a flowchart illustrating the steps for determining the target error dynamics model in one embodiment;

[0046] Figure 4 This is a flowchart illustrating the steps of calculating the deterministic driving parameters calculation model in one embodiment;

[0047] Figure 5 This is a flowchart illustrating the steps of calculating the uncertainty-driving parameters in one embodiment.

[0048] Figure 6 This is a block diagram of a distributed formation control device based on group functions in one embodiment;

[0049] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] This application provides a distributed formation control method based on group functions. This embodiment illustrates the method's application to a terminal, but it's understood that the method can also be applied to a server, or a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The server can be a standalone server or a server cluster composed of multiple servers. In this embodiment, as... Figure 1 As shown, the distributed formation control method based on group functions includes multiple moving objects, and the method comprises the following steps:

[0052] Step 102: If the moving object is not the navigator, obtain the mass matrix, wind resistance matrix, rolling resistance matrix, first control parameter of the moving object, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object.

[0053] Among them, a moving object can be an object in motion, an object that moves based on an externally applied driving force, such as a vehicle, an airplane, etc. Moving objects can include various types, such as a leader object type and a non-leader object type. A leader object can be an object in a leader position, such as an object located in the first row or the first column of a distributed formation. A non-leader object can be any moving object in the distributed formation other than the leader object.

[0054] Specifically, a moving object can be denoted as (i, j), representing the moving object at the i-th row and j-th column position in the distributed formation. The distributed formation can include m rows and n columns. The mass matrix of the moving object (can be denoted as M) i,j This represents the mass parameters of a moving object, including the mass matrix (which can be denoted as...). and the uncertain mass matrix (which can be denoted as ΔM) i,j (x i,j ,σ i,j The mass matrix can be the mass of the moving object under unloaded conditions; the wind resistance matrix can be the wind resistance calculated based on the physical parameters of the moving object itself; the rolling resistance matrix can represent the frictional resistance calculated based on the physical parameters of the moving object itself; the first control parameter can be a pre-set control parameter. The previous moving object can be determined based on the distributed formation to which the moving object belongs.

[0055] Step 104: Calculate the deterministic driving parameters of the moving object using the deterministic driving parameter calculation model and the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object.

[0056] The deterministic driving parameters of the moving object can be the forces used to drive the moving object to move, calculated based on a determined mass matrix, wind resistance matrix, rolling resistance matrix, and control parameters. The deterministic driving parameters can include driving parameters in a first direction and driving parameters in a second direction. The first direction can be the horizontal direction (denoted as x) and the second direction can be the vertical direction (denoted as y).

[0057] In implementation, the deterministic driving parameter calculation model can be a constraint follower controller. This constraint follower controller is used to ensure that each moving object in the distributed formation strictly satisfies the constraints or returns to the constraints, whether there is an initial error or not, thus ensuring the stable driving of the distributed formation.

[0058] Specifically, the deterministic driving parameter calculation model can be described by the following formula:

[0059]

[0060] in, Represents deterministic driving parameters. i,j T represents the mass matrix of the moving object in the i-th row and j-th column. i,j This represents the time of the moving object in the i-th row and j-th column. This represents the drag matrix of the moving object in the i-th row and j-th column. This represents the rolling resistance matrix of the moving object in the i-th row and j-th column; This represents the driving parameters of the preceding moving object in the i-th row and j-th column. This represents the drag matrix of the moving object in the (i-1)th row and (j-1)th column. This represents the rolling resistance matrix of the moving object in the (i-1)th row and (j-1)th column. κ represents the deterministic mass matrix of the moving object in the i-th row and j-th column. i,j β represents the control parameters of the moving object in the i-th row and j-th column. i,j Indicates the constraint following error; This represents the mass matrix of the preceding moving object in the first direction, and the mass matrix of the preceding moving object in the second direction. This represents the result after taking the second derivative with respect to the unbounded state parameters, which will be explained in detail in the following method embodiments; S i,j The intermediate variable matrix can be calculated using, for example, the following formula:

[0061]

[0062]

[0063]

[0064] Substituting the above formula into the error dynamics model yields the following result.

[0065]

[0066] Step 106: Calculate the uncertainty driving parameters of the moving object using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object, and the second control parameter.

[0067] The acceleration matrix can be the acceleration of the moving object when it is in motion; the uncertainty driving parameter calculation model can be an adaptive robust controller used to determine the driving force of the moving object containing uncertainty.

[0068] In practice, the terminal can calculate the uncertainty driving parameters of the moving object through the uncertainty driving parameter calculation model and the specific values ​​of each parameter contained in the uncertainty driving parameter calculation model. The uncertainty driving parameters can include driving parameters in a first direction and driving parameters in a second direction. The first direction can be the horizontal direction (which can be denoted as x) and the second direction can be the vertical direction (which can be denoted as y).

[0069] Specifically, the calculation model for this uncertainty-driving parameter can be described by the following formula:

[0070]

[0071] μ i,j =β i,j Π i,j

[0072] Where (i, j) represents the moving object in the i-th row and j-th column. Represents the uncertainty-driving parameter; γ i,j This indicates the second control parameter. These are pre-configured design parameters that can be determined based on the actual application scenario. i,j This represents the boundary function of uncertainty.

[0073] Step 108: If the moving object is the navigator object, obtain the mass matrix and acceleration of the moving object. Based on the mass matrix, acceleration, and trajectory constraint model of the moving object, calculate the navigator driving parameters of the moving object.

[0074] In implementation, the terminal can determine the type of the moving object based on the identification information of the moving object. When the terminal determines that the moving object is a navigation object based on the identification information, the terminal can obtain the trajectory constraint model of the moving object, as well as the mass matrix and acceleration matrix of the moving object, and calculate the navigation driving parameters of the moving object, i.e., the constraint control force. The constraint control force includes the driving parameters in the first direction and the driving parameters in the second direction. The first direction can be the horizontal direction (which can be denoted as x), and the second direction can be the vertical direction (which can be denoted as y).

[0075] Specifically, the dynamic equations of the piloting motion object can be described by the following formula:

[0076]

[0077] a = M -1 Q

[0078] Where M represents the mass matrix of the pilot motion object, Q represents the initial driving parameters, and Q0 represents the mass matrix of the pilot motion object. cThis represents the navigation and driving parameters of the moving object, where 'a' represents the acceleration matrix of the object under unconstrained conditions, such as gravitational acceleration. This represents the acceleration matrix of a moving object in a real-world application scenario.

[0079] The terminal can calculate the trajectory constraint model of a moving object using the following formula:

[0080]

[0081]

[0082] Where q represents the position information of the navigation object. The velocity information of the pilot motion object is represented by q, the time information is represented by t, the trajectory of the pilot motion object is represented by A(q,t), the other terms of the constraint equation are represented by c, A is the constraint matrix, and l is the l-th constraint.

[0083] In this way, the terminal can perform second-order derivative operations on the trajectory constraint model of the moving object, and the second-order form of the trajectory constraint model of the moving object can be:

[0084]

[0085] Where b represents the second derivative form of the other terms of the constraint equation.

[0086] In this way, the terminal can calculate the navigation drive parameters of the navigation motion object based on the following formula:

[0087] Q c =M -1 / 2 (AM 1 / 2 ) + (b-Aa)

[0088] Step 110: Drive control is performed on multiple moving objects in the distributed formation based on deterministic driving parameters, uncertain driving parameters, and navigation driving parameters.

[0089] In practice, the terminal can apply corresponding driving forces to each moving object based on the calculated deterministic driving parameters, non-deterministic driving parameters, and navigation driving parameters, so that each moving object in the distributed formation can move with the aforementioned driving parameters.

[0090] Optionally, the terminal can calculate the driving force corresponding to each driving parameter based on the calculated deterministic driving parameters, non-deterministic driving parameters, and navigation driving parameters corresponding to each moving object, as well as the pre-configured conversion relationship between driving parameters and driving force, and apply the corresponding driving force to each moving object so that each moving object in the distributed formation can move with the above driving parameters.

[0091] In the aforementioned distributed formation control method based on group functions, when the moving object is not the leader object, the method obtains the moving object's mass matrix, drag matrix, rolling resistance matrix, first control parameters, and the driving parameters, drag matrix, and rolling resistance matrix of the preceding moving object. Using a deterministic driving parameter calculation model, the method calculates the moving object's deterministic driving parameters based on the moving object's mass matrix, drag matrix, rolling resistance matrix, control parameters, and the preceding moving object's driving parameters, drag matrix, and rolling resistance matrix. Using an uncertain driving parameter calculation model, the moving object's mass matrix, acceleration matrix, and second control parameters, the method calculates the moving object's uncertain driving parameters. When the moving object is the leader object, the method obtains the moving object's mass matrix and acceleration, and calculates the leader driving parameters based on the moving object's mass matrix, acceleration, and trajectory constraint model. Based on the deterministic driving parameters, uncertain driving parameters, and leader driving parameters, the method performs driving control on multiple moving objects within the distributed formation. By employing this method, the driving force of each moving object can be accurately calculated, achieving efficient and precise driving control of the distributed formation.

[0092] In one embodiment, the group function-based distributed formation control method further includes:

[0093] Obtain the communication model of the moving objects corresponding to the distributed formation, and determine the previous moving object of the moving object based on the communication model of the moving objects.

[0094] Specifically, the terminal can number multiple moving objects in the initial unordered state. This number represents the moving object's position in the distributed formation. The numbering can be done in a two-dimensional format; for example, in (i,j), i represents the i-th row and j represents the j-th column, and (i,j) represents the moving object located in the i-th row and j-th column. The terminal can pre-configure multiple lead moving objects; for example, the moving objects in the first row and the first column can be configured as lead moving objects.

[0095] like Figure 2aAs shown, the moving object can be a vehicle. The terminal can pre-configure a two-dimensional vehicle formation of m rows and n columns, numbered in a global coordinate system. The top-left vehicle can be defined as (1,1). Starting from vehicle (1,1), the vehicles in the top row are defined sequentially as (1,2), (1,j), ..., (1,n). The vehicles in the leftmost column are defined sequentially as (2,1), (i,1), ..., (m,1). Based on this, the lead vehicle is defined, and then starting from (2,1), vehicles are defined to the right as (2,2), and so on until (2,n), thus obtaining the communication model of the moving object corresponding to the distributed formation. This communication model of the moving object (the vehicle communication topology model) can be as follows: Figure 2b As shown, each vehicle can obtain the status information of the vehicle in front and the vehicle on the left. That is, each vehicle obtains the longitudinal information (vertical direction, which can be denoted as the y direction) of the vehicle in front and the lateral information (horizontal direction, which can be denoted as the x direction) of the vehicle on the left.

[0096] In this embodiment, comprehensive information about the previous object can be obtained to achieve precise drive control, and stability can be increased in the presence of time delay, ensuring the effectiveness of distributed formation.

[0097] In one embodiment, such as Figure 2c As shown, this distributed formation control method based on group functions also includes:

[0098] Step 202: Calculate the distance between the moving objects and calculate the distance error based on the distance.

[0099] The spacing between moving objects includes a first-direction spacing and a second-direction spacing, which can specifically represent the distance to other adjacent moving objects in the first direction and the distance to other adjacent moving objects in the second direction.

[0100] In implementation, the terminal can calculate the spacing based on the displacement information of the preceding moving object in the first direction, the displacement information of the migrating object in the second direction, and the length of the moving object itself. Based on this, the terminal calculates the spacing error. The spacing error includes both the spacing error in the first direction and the spacing error in the second direction.

[0101] Specifically, the terminal can calculate the spacing using the following formula:

[0102]

[0103]

[0104] The target moving object can be denoted as (i,j). Indicates the distance in the first direction of the moving target object; x i-1,jThis represents the displacement information of the previous moving object in the first direction; x i,j This indicates the displacement information of the moving target object in the first direction; x This indicates the length of the moving object in the first direction; The second direction distance of the target moving object; y i,j-1 This indicates the displacement information of the previous moving object in the second direction; y i,j This indicates the displacement information of the moving target object in the second direction; y This indicates the length of the moving target object in the first direction.

[0105] The terminal can calculate the spacing error based on the following formula:

[0106]

[0107]

[0108] in, This indicates the spacing error of the target object in the first direction. This indicates the spacing error of the target object in the second direction. This represents the distance in the first direction of the desired moving target object. The second directional distance represents the desired target moving object.

[0109] Step 204: Perform second-order derivative calculation on the spacing error to obtain the derivative result.

[0110] Step 206: Based on the derivative results and the standard dynamic model, determine the error dynamic model of the moving object.

[0111] The error dynamics model includes the mass matrix, control matrix, wind resistance matrix, and rolling resistance matrix of the moving object.

[0112] The terminal can describe the standard dynamic model of the moving object using the following formula, that is, the dynamic model of the (i, j)th vehicle is as follows:

[0113]

[0114]

[0115] Where, m i,j This represents the mass of vehicle (i,j). Let represent the acceleration of vehicle (i,j) in the x-direction; This represents the control input to vehicle (i,j) in the x-direction. This represents the drag coefficient (drag) of vehicle (i,j) in the x-direction. This represents the velocity of vehicle (i,j) in the x-direction. The rolling resistance of vehicle (i,j) in the x-direction; x i,j This represents the displacement of vehicle (i,j) in the x-direction;

[0116] Let represent the acceleration of vehicle (i,j) in the y direction; This represents the control input to vehicle (i,j) in the y-direction. This represents the drag coefficient (drag) of vehicle (i,j) in the y-direction. This represents the velocity of vehicle (i,j) in the y-direction. The rolling resistance of vehicle (i,j) in the y direction; y i,j This represents the displacement of vehicle (i,j) in the y direction.

[0117] In this way, the terminal can rewrite the standard dynamic model based on the second-order derivative of the spacing error, and obtain the error dynamic model of the moving object:

[0118]

[0119]

[0120] in, This represents the derivative of the second-order difference of the spacing error in the first direction. This represents the result of taking the second derivative of the spacing error in the second direction.

[0121] Based on this, the terminal can also merge the error dynamics model in the first direction with the error dynamics model in the second direction to obtain the error dynamics model of the moving object:

[0122]

[0123] in, This is the rewritten mass matrix; The rewritten control matrix; This is the rewritten drag matrix. It is the rewritten rolling resistance matrix.

[0124] In this embodiment, the dynamic model (dynamic calculation model) of the moving object can be reconstructed by combining the spacing and the spacing error, so as to achieve more precise drive control.

[0125] In one embodiment, the specific processing steps for the step "calculating the distance between moving objects" include:

[0126] The spacing between the moving objects is obtained based on the displacement of the preceding moving object, the displacement of the moving object, and the length of the moving object. The spacing includes the spacing in the first direction and the spacing in the second direction.

[0127] In practice, the terminal can calculate the spacing based on the displacement information of the preceding moving object in the first direction, the displacement information of the migrating object in the second direction, and the length of the moving object itself.

[0128] Specifically, the terminal can calculate the spacing using the following formula:

[0129]

[0130]

[0131] The target moving object can be denoted as (i,j). Indicates the distance in the first direction of the moving target object; x i-1,j This represents the displacement information of the previous moving object in the first direction; x i,j This indicates the displacement information of the moving target object in the first direction; x This indicates the length of the moving object in the first direction; The second direction distance of the target moving object; y i,j-1 This indicates the displacement information of the previous moving object in the second direction; y i,j This indicates the displacement information of the moving target object in the second direction; y This indicates the length of the moving target object in the first direction.

[0132] In this embodiment, the distance information of the moving object can be accurately calculated, providing accurate data support for subsequent calculation of distance error and speed constraint.

[0133] In one embodiment, such as Figure 3 As shown, this distributed formation control method based on group functions also includes:

[0134] Step 302: Determine the spacing constraints based on the spacing of the moving objects.

[0135] Among them, the spacing constraint can be in the form of an inequality.

[0136] In implementation, to ensure that moving objects do not collide with each other, the terminal determines that each spacing is greater than or equal to 0, which can be described by the following formula:

[0137]

[0138]

[0139] Step 304: Perform a state transformation on the spacing constraint to obtain the boundaryless state parameters.

[0140] In implementation, the terminal can perform state transformations on the spacing constraints in the first direction and the spacing constraints in the second direction respectively to obtain the unbounded state parameters in the first direction (which can be denoted as...). ) and the unbounded state parameters in the second direction (which can be denoted as ) ):

[0141]

[0142]

[0143] Specifically, the unbounded state parameters in the first direction and the unbounded state parameters in the second direction can be rewritten to obtain the combined unbounded state parameters q in the first and second directions used to characterize the moving object (i, j). i,j .

[0144] Step 306: Update the error dynamics model based on the unbounded state parameters to obtain the target error dynamics model.

[0145] In implementation, the terminal can update the error dynamics model based on the determined unbounded state parameters in multiple directions to obtain the target error dynamics model. The specific target error dynamics model can be determined based on the following formula:

[0146]

[0147] In one embodiment, such as Figure 4 As shown, this distributed formation control method based on group functions also includes:

[0148] Step 402: Determine the speed constraints of the distributed formation based on the preset group function calculation model.

[0149] Among them, velocity constraints are used to ensure that the relative displacement between moving objects is maintained.

[0150] In implementation, the terminal can calculate the velocity constraints that each moving object in the distributed formation needs to satisfy based on a preset group function calculation model, that is, the velocity constraints that moving object (i,j) needs to satisfy, for example, as shown in the following formula:

[0151]

[0152]

[0153] in, The expression represents the result of differentiating the unbounded state parameters of the moving object (i,j), where m represents the number of rows of moving objects in the distributed formation containing the moving object (i,j), and n represents the value of the moving object (i,j).

[0154] The distributed formation contains the number of columns of the moving objects, where k represents the k-th row, p represents the p-th column, and q k,p Table 5 shows the unbounded state parameters of the moving object (k,p), q i,j The unbounded state parameter representing the moving object (i,j)

[0155] number, Indicates q i,j Find the partial derivative; G (i,j)(k,p) Let (i,j) and (k,p) represent the moving objects.

[0156] The constraint function between the moving object and the unbounded state parameter of the moving object is in a state that is neither too large nor too small, and z can represent the parameter determined based on the actual application scenario.

[0157] Step 404: Calculate the constraint following error based on the velocity constraint.

[0158] In implementation, the terminal can calculate the velocity constraints of the moving object (i,j) and perform binary search on the velocity constraints.

[0159] Taking the second-order derivative, we obtain the second-order velocity constraint, as follows:

[0160]

[0161] Based on this, the terminal can calculate the constraint following error of the moving object (i,j) using the following formula.

[0162] denoted as β i,j ):

[0163]

[0164] Step 406: Based on the constraint following error and the target error dynamics model, calculate the deterministic driving parameter calculation model.

[0165] In implementation, the terminal can describe the deterministic driving parameter calculation model using the following formula:

[0166]

[0167] Where, β i,j This indicates the constraint following error.

[0168] In this embodiment, stable motion of the system can be achieved, ensuring that all moving objects within the system strictly satisfy the constraints, thus realizing stable control of distributed formation.

[0169] In one embodiment, such as Figure 5 As shown, this distributed formation control method based on group functions also includes:

[0170] Step 502: Determine the uncertainty constraints of the mass matrix of the moving object.

[0171] In implementation, the terminal can determine the uncertainty of the mass of the moving object based on the actual application scenario of the moving object, i.e., the uncertainty constraint:

[0172]

[0173] Where, σ i,j E represents an uncertainty function; i,j Indicates intermediate variables; Indicates the transpose of an intermediate variable; The design parameters can be determined based on the actual application scenario.

[0174] Specifically, the terminal can determine the unidirectional limit range of the uncertainty of the mass of the moving object.

[0175] Step 504: Determine the uncertainty boundary of the moving object according to the adaptive law, and determine the time-varying function form of the uncertainty boundary of the moving object.

[0176] In implementation, the terminal can determine the adaptive law based on the constraint following error:

[0177]

[0178] in, The derivative of the adaptive law is represented. This represents the adaptive law. as well as All of these represent design parameters.

[0179] In this way, the terminal can determine the uncertainty boundary of the moving object based on the adaptive law, and determine the time-varying functional form of the uncertainty boundary of the moving object:

[0180]

[0181] Where, ΔC i,j Describing the uncertainty of wind resistance, ΔF i,j Represents uncertain rolling resistance

[0182]

[0183] Step 506: Based on the uncertainty constraints of the mass matrix of the moving object and the time-varying function form of the uncertainty boundary, determine the calculation model for the uncertainty driving parameters.

[0184] In implementation, the terminal can rewrite the standard dynamic model after considering uncertainties, as follows:

[0185]

[0186] Where, σ i (t) represents the uncertainty of the moving objects contained in the system.

[0187] The terminal can decompose each parameter into a nominal part and an uncertain part, as follows:

[0188]

[0189]

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] in, This indicates that the moving object travels x times in time t. i,j The determined mass matrix under the given state, ΔM i,j (x i,j ,σ i,j (t) represents the distance traveled by an object in time t, where x is the distance traveled. i,j The nondeterministic mass matrix under the given state; This indicates that the moving object travels x times in time t. i,j Determine the drag matrix ΔC under the given conditions. i,j (x i,j ,v i,j ,σ i,j (t) represents the distance traveled by an object in time t, where x is the distance traveled. i,j The nondeterministic drag matrix under the given conditions; This indicates that the moving object travels x times in time t. i,j Determine the rolling resistance matrix ΔF under the given conditions. i,j (x i,j ,vi,j ,σ i,j (t) represents the distance traveled by an object in time t, where x is the distance traveled. i,j The nondeterministic rolling resistance matrix under the given conditions; D represents the reciprocal of the mass matrix. i,j (x i,j ,σ i,j ,t) denotes the reciprocal of the nondeterministic mass matrix; E i,j (x i,j ,σ i,j ,t) is an intermediate variable; I i,j These are design parameters.

[0197] In this way, the terminal can calculate the uncertainty-driving parameter calculation model based on the nondeterministic mass matrix and time:

[0198]

[0199] In this embodiment, the driving force of each moving object can be accurately calculated.

[0200] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0201] Based on the same inventive concept, this application also provides a group function-based distributed formation control device 600 for implementing the above-mentioned group function-based distributed formation control method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more group function-based distributed formation control device embodiments provided below can be found in the limitations of the group function-based distributed formation control method described above, and will not be repeated here.

[0202] In one embodiment, such as Figure 6 As shown, a distributed formation control device 600 based on group functions is provided. The distributed formation includes multiple moving objects. The distributed formation control device 600 based on group functions includes:

[0203] The first acquisition module 601 is used to acquire the mass matrix, wind resistance matrix, rolling resistance matrix, first control parameters, and driving parameters, wind resistance matrix, and rolling resistance matrix of the moving object when the moving object is not a leading object.

[0204] The first calculation module 602 is used to calculate the deterministic driving parameters of the moving object by using a deterministic driving parameter calculation model and the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters of the moving object, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object.

[0205] The second calculation module 603 is used to calculate the uncertainty driving parameters of the moving object by using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object and the second control parameters;

[0206] The second acquisition module 604 is used to acquire the mass matrix and acceleration of the moving object when the moving object is a navigation object, and to calculate the navigation driving parameters of the moving object based on the mass matrix, acceleration and trajectory constraint model of the moving object.

[0207] The drive control module 605 is used to drive and control multiple moving objects in a distributed formation based on deterministic drive parameters, uncertain drive parameters, and navigation drive parameters.

[0208] Each module in the aforementioned distributed formation control device 600 based on group functions can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0209] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores the driving data for the distributed formation. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a group function-based distributed formation control method.

[0210] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0211] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0212] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0213] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0214] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0215] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0216] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A distributed formation control method based on group functions, characterized in that, The distributed formation includes multiple moving objects, and the method includes: When the moving object is not a leading object, the mass matrix, wind resistance matrix, rolling resistance matrix, and first control parameter of the moving object are obtained, as well as the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object of the moving object. The deterministic driving parameter calculation model is used to calculate the deterministic driving parameters of the moving object using the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object. The deterministic driving parameter calculation model is a constraint-following controller, which is used to ensure that each moving object in the distributed formation strictly satisfies the constraints or can return to the constraints, whether or not there is an initial error. The uncertainty driving parameters of the moving object are calculated using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object, and the second control parameter; the uncertainty driving parameter calculation model is described by the following formula: m i,j =b i,j P i,j Where (i, j) represents the moving object in the i-th row and j-th column. Represents the uncertainty-driving parameter; γ i,j This indicates the second control parameter. These are pre-configured design parameters, Π i,j Represents the boundary function of uncertainty; When the moving object is a navigation object, the mass matrix and acceleration of the moving object are obtained, and the navigation driving parameters of the moving object are calculated based on the mass matrix, acceleration and trajectory constraint model of the moving object. Based on the deterministic driving parameters, the uncertain driving parameters, and the navigation driving parameters, the driving control is performed on the multiple moving objects included in the distributed formation.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the motion object communication model corresponding to the distributed formation, and determine the previous motion object of the motion object based on the motion object communication model.

3. The method according to claim 1, characterized in that, The method further includes: Calculate the distance between the moving objects, and calculate the distance error based on the distance; The second derivative of the spacing error is calculated to obtain the derivative result; Based on the derivative results and the standard dynamic model, the error dynamic model of the moving object is determined. The error dynamic model includes the mass matrix, control matrix, wind resistance matrix, and rolling resistance matrix of the moving object.

4. The method according to claim 3, characterized in that, The calculation of the distance between the moving objects includes: The spacing between the moving objects is obtained based on the displacement of the preceding moving object, the displacement of the moving object, and the length of the moving object. The spacing includes the spacing in a first direction and the spacing in a second direction.

5. The method according to claim 3, characterized in that, The method further includes: Based on the spacing of the moving objects, determine the spacing constraints; The spacing constraint is transformed to obtain the boundaryless state parameters; The error dynamics model is updated based on the unbounded state parameters to obtain the target error dynamics model.

6. The method according to claim 5, characterized in that, The method further includes: Based on a preset group function calculation model, the velocity constraints of the distributed formation are determined, and the velocity constraints are used to ensure that the relative displacement between each moving object is maintained. Based on the aforementioned speed constraint, the constraint following error is calculated; Based on the constraint following error and the target error dynamics model, the deterministic driving parameter calculation model is calculated.

7. The method according to claim 1, characterized in that, The method further includes: Determine the uncertainty constraints of the mass matrix of the moving object; The uncertainty boundary of the moving object is determined according to the adaptive law, and the time-varying function form of the uncertainty boundary of the moving object is determined. Based on the uncertainty constraint of the mass matrix of the moving object and the time-varying function form of the uncertainty boundary, the calculation model for the uncertainty driving parameters is determined.

8. A distributed formation control device based on group functions, characterized in that, The distributed formation includes multiple moving objects, and the device includes: The first acquisition module is used to acquire the mass matrix, wind resistance matrix, rolling resistance matrix, first control parameters of the moving object and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object when the moving object is a non-navigation object; The first calculation module is used to calculate the deterministic driving parameters of the moving object using a deterministic driving parameter calculation model and the mass matrix, wind resistance matrix, rolling resistance matrix, control parameters, and the driving parameters, wind resistance matrix, and rolling resistance matrix of the previous moving object. The deterministic driving parameter calculation model is a constraint follower controller, which is used to ensure that each moving object in the distributed formation strictly satisfies the constraints or can return to the constraints, whether or not there is an initial error. The second calculation module is used to calculate the uncertainty driving parameters of the moving object by using the uncertainty driving parameter calculation model, the mass matrix and acceleration matrix of the moving object and the second control parameters; The second acquisition module is used to acquire the mass matrix and acceleration of the moving object when the moving object is a navigation object, and to calculate the navigation driving parameters of the moving object based on the mass matrix, acceleration, and trajectory constraint model of the moving object; the calculation model of the uncertainty driving parameters is described by the following formula: m i,j =b i,j P i,j Where (i, j) represents the moving object in the i-th row and j-th column. Represents the uncertainty-driving parameter; γ i,j This indicates the second control parameter. These are pre-configured design parameters, Π i,j Represents the boundary function of uncertainty; The drive control module is used to drive and control multiple moving objects included in the distributed formation based on the deterministic drive parameters, the uncertain drive parameters, and the navigation drive parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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