Data-driven Cooperative Output Regulation Method, Device and Medium for Heterogeneous Unmanned Systems

Abstract

The present application discloses a heterogeneous unmanned system data-driven cooperative output regulation method, device and medium. The method includes offline collecting the regulation input sequence, regulation output sequence and internal model state sequence of each unmanned system monomer, and determining the control gain matrix of the unmanned system monomer based on the collected regulation input sequence, regulation output sequence and internal model state sequence; constructing an output feedback controller for the unmanned system monomer based on the control gain matrix to obtain a distributed output feedback controller, and performing coordinated output regulation on the cluster unmanned system through the distributed output feedback controller. The present application avoids relying on an accurate model of the system by using the offline collected input data and output data affected by noise, and constructs a distributed output feedback controller based on the input data and output data, and can realize the cooperative output regulation of an unknown cluster system only by using the data.

Description

Technical Field

[0001] This application relates to the technical field of cooperative control of cluster unmanned systems, and particularly relates to a cooperative output regulation method, device, and medium for heterogeneous unmanned systems driven by data. Background Art

[0002] With the rapid development of artificial intelligence, by promoting the deep integration of artificial intelligence and scientific research, the transformation of the scientific research paradigm and the improvement of capabilities are accelerated, and artificial intelligence is promoted to a new stage of high-quality application. Especially in the field of unmanned systems, unmanned systems represented by unmanned aerial vehicles, unmanned vehicles, unmanned ships, intelligent robots, etc. have become important carriers for artificial intelligence research, demonstrating the deep integration of artificial intelligence with cutting-edge disciplines such as big data, optimal decision-making, and cooperative control. A cluster unmanned system consists of a large number of unmanned system monomers, which complete complex tasks that cannot be independently completed by a single unmanned system through mutual interaction and cooperation, and has become a strategic focus of global scientific and technological competition.

[0003] Regarding the requirements of trajectory tracking tasks in an interference and countermeasure environment, the cooperative output regulation problem of cluster unmanned systems has attracted extensive attention. Existing solutions mainly rely on the accurate models of the systems, and it is necessary to construct a state space model based on mechanism or identification methods to determine the distributed control strategy, and perform cooperative regulation based on the distributed control strategy. However, due to the characteristics of large-scale distribution, heterogeneous node dynamics, and high model complexity of cluster unmanned systems, it is extremely difficult to construct accurate models, which limits the cooperative regulation through distributed control strategies in complex environments.

[0004] Therefore, the existing technology still needs to be improved. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a cooperative output regulation method, device, and medium for heterogeneous unmanned systems driven by data in view of the deficiencies of the existing technology.

[0006] To solve the above technical problem, the first aspect of this application provides a cooperative output regulation method for heterogeneous unmanned systems driven by data. Specifically, the cooperative output regulation method for heterogeneous unmanned systems driven by data specifically includes:

[0007] Construct an internal model system for each unmanned system monomer in the cluster unmanned system, and connect the unmanned system monomer with its corresponding internal model system, where the internal model system is constructed based on the evolution matrix of the external reference system of the cluster unmanned system;

[0008] Offline collect the adjustment input sequence, adjustment output sequence of each unmanned system unit in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system, and determine the control gain matrix of the unmanned system unit based on the collected adjustment input sequence, adjustment output sequence, and internal model state sequence;

[0009] Construct an output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit to obtain the distributed output feedback controller corresponding to the cluster unmanned system, and perform coordinated output regulation on the cluster unmanned system through the distributed output feedback controller.

[0010] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein the dynamic models of the unmanned system units in the cluster unmanned system are heterogeneous.

[0011] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein constructing an internal model system for each unmanned system unit in the cluster unmanned system specifically includes:

[0012] Construct a submatrix based on the minimal polynomial of the evolution matrix of the external reference system of the cluster unmanned system, and construct a block diagonal matrix with the submatrix as the submatrix on the diagonal to form the first internal model matrix;

[0013] Construct a column vector with the same column dimension as the column dimension of the submatrix, and construct a block diagonal matrix with the column vector as the submatrix on the diagonal to form the second internal model matrix, wherein the product of the submatrix and the column vector is controllable;

[0014] Construct an internal model system for each unmanned system unit based on the first internal model matrix, the second internal model matrix, and the virtual tracking difference of each unmanned system unit.

[0015] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein determining the control gain matrix of the unmanned system unit based on the collected adjustment input sequence, adjustment output sequence, and internal model state sequence specifically includes:

[0016] For each unmanned system unit, determine the observability index of the unmanned system unit;

[0017] Construct a linear matrix inequality based on the observability index, the adjustment input sequence, the adjustment output sequence, and the internal model state sequence, and solve the linear matrix inequality to obtain the first gain parameter and the second gain parameter;

[0018] Determine the control gain matrix of the unmanned system unit based on the first gain parameter and the second gain parameter.

[0019] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein the linear matrix inequality is:

[0020] ,

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026] ,

[0027] ,

[0028] ,

[0029] ,

[0030] wherein, represents the first gain parameter; represents the second gain parameter; , , , , all represent the identity matrix; represents the first data matrix; represents the second data matrix; , , , , , , , , are all known matrices; represents the observability index of the unmanned system unit ; represents the transpose; represents the upper limit value of the noise of the unmanned system unit ; , , , , , , , , , All represent the all-zero matrix; , , All represent positive integers.

[0031] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein the expression of the control gain matrix is:

[0032] ,

[0033] ,

[0034] ,

[0035] wherein, represents the first gain parameter, represents the second gain parameter, represents the control gain matrix, represents the th eigenvalue of represents the Laplacian matrix of the graph structure of the communication network between each unmanned system unit in the cluster unmanned system and the external reference signal, represents the diagonal matrix with as the main diagonal elements, represents the intermediate matrix, represents the connection relationship between the unmanned unit

[0036] The heterogeneous unmanned system data-driven cooperative output regulation method, wherein constructing an output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit specifically includes:

[0037] For each unmanned system unit, construct the control difference of the unmanned system unit, and construct the unit data of the unmanned system unit based on the control difference and the internal model state of the unmanned system unit;

[0038] Construct an output feedback controller for the unmanned system unit based on the unit data of the unmanned system unit and the control gain matrix;

[0039] The expression of the control difference is:

[0040] , , ,

[0041] wherein, represents the control difference; The element at the th row and the th column of the adjacency matrix representing the graph structure of the communication network between each unmanned system unit in the cluster unmanned system and the external reference signal; Indicates the connectivity relationship between the unmanned unit and the external reference system; Indicates the observability index of the unmanned system unit ; And both represent intermediate variables; Represents the output data of the unmanned system unit at time ; Represents the system output of the external reference system at time ; Represents the transpose.

[0042] The second aspect of the present application provides a heterogeneous unmanned system data-driven cooperative output regulation device, wherein the heterogeneous unmanned system data-driven cooperative output regulation device specifically includes:

[0043] An internal model system construction module, configured to construct an internal model system for each unmanned system unit in the cluster unmanned system, and connect the unmanned system unit with its corresponding internal model system, wherein the internal model system is constructed based on the evolution matrix of the external reference system of the cluster unmanned system;

[0044] An offline collection module, configured to offline collect the adjustment input sequence, adjustment output sequence of each unmanned system unit in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system, and determine the control gain matrix of the unmanned system unit based on the collected adjustment input sequence, adjustment output sequence, and internal model state sequence;

[0045] A controller construction module, configured to construct an output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit to obtain the distributed output feedback controller corresponding to the cluster unmanned system, and an adjustment module, configured to perform coordinated output adjustment on the cluster unmanned system through the distributed output feedback controller.

[0046] The third aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in any one of the above-mentioned heterogeneous unmanned system data-driven cooperative output regulation methods.

[0047] The fourth aspect of the present application provides a server, which includes: a processor and a memory;

[0048] The memory stores a computer-readable program executable by the processor;

[0049] When the processor executes the computer-readable program, it implements the steps in any of the above-mentioned heterogeneous unmanned system data-driven cooperative output regulation methods.

[0050] Beneficial effects:

[0051] 1. Only using the input data and output data affected by noise collected offline, and constructing a distributed output feedback controller based on the input data and output data, avoiding the dependence on the accurate model of the system, and realizing the cooperative output regulation of the unknown cluster system only by using data.

[0052] 2. Avoiding the use of the state data of individual unmanned systems, and realizing cooperative output regulation only by using input data and output data, effectively solving the problem of unmeasurable state data caused by the privacy protection requirements during task execution.

[0053] 3. Only need to solve a low-complexity linear matrix inequality offline to realize the construction of the distributed output feedback controller, saving computing resources and energy consumption; and the constructed controller has good robustness, effectively solving the zero-deviation tracking of the output of each individual unmanned system and the external reference system under the condition of data interference. Description of the drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a flowchart of the heterogeneous unmanned system data-driven cooperative output regulation method provided by the embodiment of the present application.

[0056] Figure 2 It is an effect diagram of the output in the first dimension of a specific embodiment.

[0057] Figure 3 It is an effect diagram of the output in the second dimension of this specific embodiment.

[0058] Figure 4 It is a schematic block diagram of the principle of the heterogeneous unmanned system data-driven cooperative output regulation device provided by the embodiment of the present application.

[0059] Figure 5This is a schematic block diagram of the server provided by the embodiments of the present application. Detailed implementation manners

[0060] The embodiments of the present application provide a collaborative output regulation method, device and medium driven by heterogeneous unmanned system data. To make the objectives, technical solutions and effects of the present application clearer and more definite, the following further describes the present application in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] Those skilled in the art of the present technology can understand that unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0062] Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present application belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0063] It should be understood that the sequence numbers and magnitudes of the steps in this embodiment do not mean the order of execution is prior or posterior. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0064] It has been found through research that with the rapid development of artificial intelligence, by promoting the deep integration of artificial intelligence and scientific research, accelerating the transformation of scientific research paradigms and the improvement of capabilities, and driving artificial intelligence towards a new stage of high-quality applications. Especially in the field of unmanned systems, unmanned systems represented by drones, unmanned vehicles, unmanned ships, intelligent robots, etc. have become important carriers for artificial intelligence research, demonstrating the deep integration of artificial intelligence with frontier disciplines such as big data, optimal decision-making, and cooperative control. Cluster unmanned systems are composed of a large number of unmanned system monomers. By interacting and collaborating with each other, they can complete complex tasks that cannot be independently completed by a single unmanned system, becoming a strategic focus of global technological competition.

[0065] Aiming at the trajectory tracking task requirements in an interference and countermeasure environment, the cooperative output regulation problem of cluster unmanned systems has attracted extensive attention. Existing solutions mainly rely on the accurate model of the system, and it is necessary to construct a state space model based on mechanism or identification methods to determine the distributed control strategy and perform cooperative regulation based on the distributed control strategy. However, due to the characteristics of large-scale distribution, heterogeneous node dynamics, and high model complexity of cluster unmanned systems, it is extremely difficult to construct an accurate model, which limits the cooperative regulation through distributed control strategies in complex environments.

[0066] To solve the above problems, in the embodiments of the present application, an internal model system is constructed for each unmanned system monomer in the cluster unmanned system, and the unmanned system monomer is connected to its corresponding internal model system. The regulation input sequence, regulation output sequence of each unmanned system monomer in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system are collected offline, and based on the collected regulation input sequence, regulation output sequence, and internal model state sequence, the control gain matrix of the unmanned system monomer is determined; an output feedback controller is constructed for each unmanned system monomer based on the control gain matrix of each unmanned system monomer to obtain the corresponding distributed output feedback controller of the cluster unmanned system, and the cluster unmanned system is coordinately output-regulated through the distributed output feedback controller. The present application avoids relying on the accurate model of the system by using the offline collected input data and output data affected by noise, and can realize the cooperative output regulation of unknown cluster systems only by using the data.

[0067] The following further illustrates the application content through the description of embodiments in conjunction with the accompanying drawings.

[0068] This embodiment provides a method for cooperative output regulation driven by data of heterogeneous unmanned systems, as Figure 1 shown, the method includes:

[0069] S10. Construct an internal model system for each unmanned system unit in the cluster unmanned system, and connect the unmanned system unit to its corresponding internal model system.

[0070] Specifically, the cluster unmanned system includes several unmanned system units, among which the dynamic models of at least some of the unmanned system units are heterogeneous. In particular, the dynamic models of the unmanned system units in the cluster unmanned system are heterogeneous. For example, the cluster unmanned system is a drone cluster, which includes several quadrotor drones with heterogeneous dynamic models. The mathematical models of the unmanned system units in the cluster unmanned system are unknown, and their dynamic equations can be expressed as:

[0071] ,

[0072] where, represents the state data of the th unmanned system unit at time , and its dimension is ; represents the input data of the th unmanned system unit at time , and its dimension is ; represents the single - unit output data of the th unmanned system unit at time , and its dimension is ; represents an unknown real matrix of dimension ; is an unknown real matrix of dimension ; represents an unknown real matrix of dimension ; represents an external reference signal of dimension

[0073] ,

[0074] where, represents the system output of the external reference system at time , and its dimension is ; represents an unknown real matrix of dimension , and the eigenvalues of the evolution matrix have no negative real parts.

[0075] Each unmanned system unit in the cluster unmanned system communicates with an external reference system, and the communication network between each unmanned system unit in the cluster unmanned system and the external reference system is a directed spanning tree with an external reference signal as the root node. Here, this communication network is denoted as .

[0076] Therefore, when a given regulation output is provided, the closed-loop system formed by the cluster unmanned system and the external reference system can be expressed as:

[0077] .

[0078] The goal of cooperative output regulation in the embodiments of this application is to construct input data for each unmanned system unit such that when there is no external reference signal (i.e., is identically zero), the above-mentioned closed-loop system achieves exponential stabilization, and when there is an external reference signal (i.e., is non-zero), for any system initial state data , it can achieve that as time tends to infinity, the tracking error tends to zero.

[0079] Based on this, when performing cooperative output regulation on the cluster unmanned system, an internal model system will be constructed for each unmanned system unit in the cooperative output regulation. This internal model system is used for internal model compensation of the unmanned system unit. Among them, the internal model system is constructed based on the evolution matrix of the external reference system, and the external reference system is used to provide an external reference signal for the cluster unmanned system.

[0080] Exemplarily, constructing the internal model system for each unmanned system unit in the cluster unmanned system specifically includes:

[0081] Constructing a submatrix based on the minimal polynomial of the evolution matrix of the external reference system of the cluster unmanned system, and constructing a block diagonal matrix with the submatrix as the submatrix on the diagonal to form a first internal model matrix;

[0082] Constructing a column vector with the same column dimension as the column dimension of the submatrix, and constructing a block diagonal matrix with the column vector as the submatrix on the diagonal to form a second internal model matrix, where the product of the submatrix and the column vector is controllable;

[0083] Based on the first internal model matrix, the second internal model matrix, and the virtual tracking differences of each unmanned system unit, constructing an internal model system for each unmanned system unit.

[0084] Specifically, after obtaining the evolution matrix of the external reference system, first determine the minimal polynomial of the evolution matrix , and then construct a constant square matrix whose characteristic polynomial is the same as the minimal polynomial , and finally construct a block diagonal matrix with the constant square matrix as the submatrix on the diagonal to form the first internal mode matrix. Among them, the first internal mode matrix can be expressed as:

[0085] ,

[0086] where represents the first internal mode matrix; represents the constant square matrix whose characteristic polynomial is the same as the minimal polynomial, and its dimension is , represents a positive integer; represents the block diagonal matrix constructed with as the submatrix on the diagonal; represents that the block diagonal matrix has submatrices.

[0087] After obtaining the first internal mode matrix, construct the second internal mode matrix based on the first internal mode matrix, and make the product of the submatrix of the first internal mode matrix and the column vector of the second internal mode matrix controllable. Among them, the second internal mode matrix can be expressed as:

[0088] ,

[0089] where represents the second internal mode matrix; represents an arbitrarily selected constant column vector, and its dimension is , and is controllable; represents the block diagonal matrix constructed with as the column vector on the diagonal; represents that the block diagonal matrix has column vectors.

[0090] It should be noted that in practical applications, other methods can also be used to construct the first internal mode matrix and the second internal mode matrix. For example, directly use the coefficients of the minimal polynomial of the evolution matrix to construct the first internal mode matrix and the second internal mode matrix, etc.

[0091] After determining the first internal mode matrix and the second internal mode matrix, construct an internal mode system based on the first internal mode matrix, the second internal mode matrix, and the virtual tracking difference. Among them, the internal mode system can be expressed as:

[0092] ,

[0093] Among them, represents the internal model state at time represents the internal model state at time represents the first internal model matrix, represents the second internal model matrix, represents the tracking error.

[0094] Furthermore, the tracking error may include a first output difference between the individual output data of each unmanned system unit, or may include a second output difference between the individual output data of the unmanned system unit and the system output of the external signal system. In the embodiments of the present application, the tracking error includes the first output difference between the individual output data of each unmanned system unit and the second output difference between the individual output data of the unmanned system unit and the system output of the external signal system. Correspondingly, the tracking error can be expressed as:

[0095] ,

[0096] Among them, represents the tracking error, represents the communication network the adjacency matrix of the th row and the and the connectivity relationship with the external reference system, represents the unmanned system unit and the individual output data of represents the unmanned system unit and the individual output data of represents the system output of the external reference system, represents the number of unmanned system units.

[0097] Furthermore, after constructing the internal model system for each unmanned system unit, each unmanned system unit and its corresponding internal model system are connected to each unmanned system unit, and the operating system connecting the internal model system is run in an open loop. Among them, the operating system can be expressed as:

[0098] ,

[0099] Among them, represents the input noise existing on the input side, represents the output noise existing on the output side, represents the measured interference-adjusted input of the unmanned system unit affected by noise, Represents the measured single unmanned system affected by noise The regulated output affected by interference, Represents the measured inner membrane system affected by noise At The inner model state at the moment, Represents the measured inner membrane system affected by noise At The inner model state at the moment.

[0100] S20. Offline collect the regulation input sequence, regulation output sequence of each single unmanned system in the cluster unmanned system and the inner model state sequence of its corresponding inner model system, and determine the control gain matrix of the single unmanned system based on the collected regulation input sequence, regulation output sequence and inner model state sequence.

[0101] Specifically, after running the above operating system, collect from 0 to At the moment of , , , To obtain the regulation input sequence, regulation output sequence and inner model state sequence. Among them, the collection process can be specifically: for any period of time To , randomly generate the input To the unknown single unmanned system , obtain the regulation output sequence Of the single unmanned system At the corresponding moment, randomly generate the noise sequences And , calculate the regulation input sequence And regulation output data Of the single unmanned system affected by noise , and record the input sequence And regulation output sequence At the corresponding moment; send the regulation output sequence To the inner membrane system, record the state data of the inner membrane system at the corresponding moment to obtain the inner model state sequence

[0102] Furthermore, after obtaining the regulation input sequence, regulation output sequence and inner model state sequence, determine the control gain matrix of the single unmanned system based on the regulation input sequence, regulation output sequence and inner model state sequence. Among them, the determination process of the control gain matrix specifically includes:

[0103] For each single unmanned system, determine the observability index of the single unmanned system;

[0104] Construct a linear matrix inequality based on the observability index, the regulated input sequence, the regulated output sequence, and the internal model state sequence, and solve the linear matrix inequality to obtain a first gain parameter and a second gain parameter;

[0105] Determine the control gain matrix of the unmanned system unit based on the first gain parameter and the second gain parameter.

[0106] Specifically, the observability index is the matrix pair in the dynamic model When observable, such that for and The rank of the target matrix reaches the state dimension of the unmanned system unit The minimum integer of Where the target matrix is:

[0107] ,

[0108] Among them, Represents the target matrix, Represents the transpose.

[0109] Furthermore, when constructing the linear matrix inequality, two intermediate data matrices can be constructed first based on the observability index, the regulated input sequence, the regulated output sequence, and the internal model state sequence, denoted as the first data matrix and the second data matrix respectively. Among them, the matrix dimensions of the first data matrix and the second data matrix are the same, and the number of columns of both = , and the number of rows = , and the first data matrix and the second data matrix can be expressed as:

[0110] ,

[0111] ,

[0112] Among them, Represents the observability index.

[0113] After determining the first data matrix and the second data matrix, set the given matrix As the noise upper limit value of the noise introduced by the unmanned system unit during the data collection phase , and this noise upper limit value includes input measurement noise, regulated output measurement noise, and the external reference system during the data collection phase. Then, based on the first data matrix, the second data matrix, and the noise upper limit value, construct the known matrices , And as intermediate data, where , And Are respectively expressed as:

[0114] ,

[0115] ,

[0116] ,

[0117] Among them, represents the first data matrix, represents the second data matrix, represents the noise upper limit value of the unmanned system unit of the unmanned system unit, represents the noise upper limit value of the unmanned system unit of the unmanned system unit, is a known matrix used as intermediate data, which can be expressed as:

[0118] ,

[0119] ,

[0120] Among them, the known matrix used as intermediate data, , both represent all-zero matrices, and both represent positive integers, , both represent identity matrices, , , both represent positive integers.

[0121] After obtaining , and , based on, , and a linear matrix inequality is constructed, where the linear matrix inequality is:

[0122] ,

[0123] ,

[0124] ,

[0125] ,

[0126] ,

[0127] Among them, represents the first gain parameter, represents the second gain parameter; , , all represent the identity matrix; , , , are all known matrices; represents the observability index of the individual unmanned system ; represents the transpose; , , , , , , , , all represent the all-zero matrix; , both represent positive integers.

[0128] After obtaining the linear matrix inequality, the first gain parameter and the second gain parameter can be determined by solving the linear matrix inequality. Among them, the solution method of the linear matrix inequality can adopt the existing method, which will not be elaborated here. In addition, after obtaining the first gain parameter and the second gain parameter, based on the first gain parameter and the second gain parameter, the control gain matrix is determined. The control gain matrix can be expressed as:

[0129] ,

[0130] ,

[0131] ,

[0132] where represents the first gain parameter, represents the second gain parameter, represents the control gain matrix, represents the th eigenvalue of represents the Laplacian matrix of the graph structure of the communication network between each individual unmanned system in the cluster unmanned system and the external reference signal, represents as the diagonal matrix with the main diagonal elements, represents the intermediate matrix, represents the unmanned individual and the connectivity relationship with the external reference system.

[0133] S30. Based on the control gain matrix of each unmanned system unit, an output feedback controller is constructed for each unmanned system unit to obtain the distributed output feedback controller corresponding to the swarm unmanned system, and the swarm unmanned system is coordinately output-regulated through the distributed output feedback controller.

[0134] Specifically, the control gain matrix is used to construct the output feedback controller. The output of each unmanned system unit in the swarm unmanned system is controlled through the respective output feedback controller of each unmanned system unit, so as to achieve exponential stabilization of the unknown swarm unmanned system when the external disturbance and external signal are 0, and to achieve that for any initial state of the system, the system output can tend to zero with time approaching infinity and the tracking deviation tends to zero when the external disturbance and external signal are not 0.

[0135] The output feedback controller is used to generate a control input based on the input data, unit output data, system output, and internal model state of the unmanned system unit, and send the control input back to the unknown unmanned system unit to achieve the coordinated output regulation of the swarm unmanned system with the unknown model. Among them, when generating the control input based on the input data, unit output data, system output, and internal model state of the unmanned system unit, it can be achieved by multiplying the control gain matrix with the unit data of the unmanned system unit determined according to the input data, unit output data, system output, and internal model state. That is, in the online operation stage, the unknown model unmanned system unit sends the current unit data to its corresponding output feedback controller at each moment, and at the same time the internal membrane system sends the current internal membrane state to the output feedback controller , and the output feedback controller generates a control input and sends the generated control input back to the unknown unmanned system unit to achieve the coordinated output regulation of the swarm unmanned system with the unknown model.

[0136] Therefore, the output feedback controller can be expressed as:

[0137] ,

[0138] where represents the internal model state, represents the control difference, represents the control gain matrix, represents the control input generated by the output feedback controller.

[0139] Exemplarily, constructing the output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit specifically includes:

[0140] For each individual unmanned system, construct the control difference of the individual unmanned system, and construct the individual data of the individual unmanned system based on the control difference and the internal model state of the individual unmanned system;

[0141] Construct an output feedback controller for the individual unmanned system based on the individual data of the individual unmanned system and the control gain matrix.

[0142] Specifically, the control difference includes a first output difference between the individual output data of each individual unmanned system and a second output difference between the individual output data of the individual unmanned system and the system output. The control difference can be expressed as:

[0143] , , ,

[0144] where, represents the control difference, represents the element in the th row and th column of the adjacency matrix of the graph structure of the communication network between each individual unmanned system in the cluster unmanned system and the external reference signal, represents the connectivity relationship between the individual unmanned and the external reference system, represents the observability index of the individual unmanned system , and both represent intermediate variables, represents the individual output data of the individual unmanned system at time , represents the system output of the external reference system at time , represents the input data of the individual unmanned system at time represents the transpose.

[0145] Of course, in practical applications, the control difference can take other forms. For example, it can include taking the mean of the output differences between the individual output data of each individual unmanned system as the first output difference, or weighting the output differences between the individual output data of each individual unmanned system to determine the first output difference, etc.

[0146] In summary, this embodiment provides a collaborative output regulation method driven by data of a heterogeneous unmanned system. The method constructs an internal model system for each unmanned system unit in the cluster unmanned system, connects the unmanned system unit with its corresponding internal model system, offline collects the regulation input sequence, regulation output sequence of each unmanned system unit in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system, and determines the control gain matrix of the unmanned system unit based on the collected regulation input sequence, regulation output sequence, and internal model state sequence; constructs an output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit to obtain the distributed output feedback controller corresponding to the cluster unmanned system, and performs coordinated output regulation on the cluster unmanned system through the distributed output feedback controller. This application avoids relying on an accurate model of the system by using offline collected input data and output data affected by noise, and can achieve collaborative output regulation of an unknown cluster system only by using data.

[0147] To further illustrate the effectiveness of the collaborative output regulation method driven by data of the heterogeneous unmanned system provided in this embodiment of the application, a specific simulation example is given below for illustration.

[0148] As Figure 2 and Figure 3 shown is the effect diagram of an embodiment running for 80 seconds using the method proposed in the present invention on a discrete-time heterogeneous multi-agent system. Among them, the system matrix in this embodiment is:

[0149] ,

[0150] ,

[0151] ,

[0152] ,

[0153] ,

[0154] ,

[0155] Among them, , , .

[0156] Correspondingly, the internal membrane matrices and are:

[0157] ,

[0158] , .

[0159] From Figure 2 The monomer output of the four unmanned system monomers represented by And the system output of the external reference system The first-dimensional output of Figure 3 The monomer output of the four unmanned system monomers represented by And the system output of the external reference system As can be seen from the effect diagram of the second-dimensional output, the output of each unmanned system monomer can track the output of the external reference system without deviation, that is, the collaborative output regulation of the multi-agent system is realized, which proves the effectiveness of the heterogeneous unmanned system data-driven collaborative output regulation method.

[0160] Based on the above heterogeneous unmanned system data-driven collaborative output regulation method, this embodiment provides a heterogeneous unmanned system data-driven collaborative output regulation device, as Figure 4 Shown, the heterogeneous unmanned system data-driven collaborative output regulation device specifically includes:

[0161] The internal model system construction module 100 is used to construct an internal model system for each unmanned system monomer in the cluster of unmanned systems and connect the unmanned system monomer with its corresponding internal model system, wherein the internal model system is constructed based on the evolution matrix of the external reference system of the cluster of unmanned systems;

[0162] The offline collection module 200 is used to offline collect the adjustment input sequence, adjustment output sequence of each unmanned system monomer in the cluster of unmanned systems and the internal model state sequence of its corresponding internal model system, and determine the control gain matrix of the unmanned system monomer based on the collected adjustment input sequence, adjustment output sequence and internal model state sequence;

[0163] The controller construction module 300 is used to construct an output feedback controller for each unmanned system monomer based on the control gain matrix of each unmanned system monomer to obtain the distributed output feedback controller corresponding to the cluster of unmanned systems, and the adjustment module is used to perform coordinated output adjustment on the cluster of unmanned systems through the distributed output feedback controller.

[0164] Based on the above heterogeneous unmanned system data-driven collaborative output regulation method, this embodiment provides a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the heterogeneous unmanned system data-driven collaborative output regulation method as described in the above embodiment.

[0165] Based on the above heterogeneous unmanned system data-driven cooperative output regulation method, the present application also provides a server, as Figure 5 shown, which includes at least one processor 20; a display screen 21; and a memory 22. It may also include a communication interface 23 and a bus 24. Among them, the processor 20, the display screen 21, the memory 22, and the communication interface 23 can communicate with each other through the bus 24. The display screen 21 is set to display a user guidance interface preset in the initial setting mode. The communication interface 23 can transmit information. The processor 20 can call the logical instructions in the memory 22 to execute the method in the above embodiments.

[0166] In addition, when the logical instructions in the above-mentioned memory 22 can be implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0167] The memory 22, as a computer-readable storage medium, can be set to store software programs and computer-executable programs, such as the program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 22, that is, to implement the methods in the above embodiments.

[0168] The memory 22 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the server, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs can also be transient storage media.

[0169] In addition, the specific processes of loading and executing multiple instructions by the above-mentioned storage medium and the instruction processor in the server have been described in detail in the above method and will not be repeated here one by one.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data-driven cooperative output regulation method for heterogeneous unmanned systems, characterized in that, The described heterogeneous unmanned system data-driven cooperative output regulation method specifically includes: Construct an internal model system for each unmanned system unit in the cluster unmanned system, and connect the unmanned system unit with its corresponding internal model system, where the internal model system is constructed based on the evolution matrix of the external reference system of the cluster unmanned system; Offline collect the regulation input sequence, regulation output sequence of each unmanned system unit in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system; For each unmanned system unit, determine the observability index of the unmanned system unit; Based on the observability index, the regulation input sequence, the regulation output sequence and the internal model state sequence, construct a linear matrix inequality, and solve the linear matrix inequality to obtain the first gain parameter and the second gain parameter; Based on the first gain parameter and the second gain parameter, determine the control gain matrix of the unmanned system unit; Based on the control gain matrices of each unmanned system unit, construct an output feedback controller for each unmanned system unit to obtain the distributed output feedback controller corresponding to the cluster unmanned system, and perform coordinated output regulation on the cluster unmanned system through the distributed output feedback controller; The linear matrix inequality is: ， ， ， ， ， ， ， ， ， Among them, represents the first gain parameter; represents the second gain parameter; , , , , all represent the identity matrix; represents the first data matrix; represents the second data matrix; , , , , , , , , are all known matrices; represents the observability index of the unmanned system unit ; represents the transpose; represents the noise upper limit value of the unmanned system unit ; , , , , , , , , , all represent the all-zero matrix; , , all represent positive integers.

2. The collaborative output regulation method driven by heterogeneous unmanned system data according to claim 1, wherein The dynamic models of the unmanned system units in the cluster unmanned system are heterogeneous.

3. The collaborative output regulation method driven by heterogeneous unmanned system data according to claim 1, characterized in that The specific process of constructing an internal model system for each unmanned system unit in the cluster unmanned system includes: Construct a submatrix based on the minimal polynomial of the evolution matrix of the external reference system of the cluster unmanned system, and construct a block diagonal matrix with the submatrix as the submatrix on the diagonal to form the first internal model matrix; Construct a column vector with the same column dimension as the column dimension of the submatrix, and construct a block diagonal matrix with the column vector as the submatrix on the diagonal to form the second internal model matrix, where the product of the submatrix and the column vector is controllable; Based on the first internal model matrix, the second internal model matrix and the virtual tracking difference of each unmanned system unit, construct an internal model system for each unmanned system unit.

4. The heterogeneous unmanned system data-driven collaborative output regulation method according to claim 1, wherein The expression of the control gain matrix is: ， ， ， Among them, represents the first gain parameter, represents the second gain parameter, represents the control gain matrix, represents the th eigenvalue of represents the Laplacian matrix of the graph structure of the communication network between each unmanned system unit in the cluster unmanned system and the external reference signal, represents a diagonal matrix with as the main diagonal elements, represents the intermediate matrix, represents the connectivity relationship between the unmanned unit and the external reference system.

5. The collaborative output regulation method driven by heterogeneous unmanned system data according to claim 1, characterized in that The specific process of constructing an output feedback controller for each unmanned system unit based on the control gain matrices of each unmanned system unit includes: For each unmanned system unit, construct the control difference of the unmanned system unit, and construct the unit data of the unmanned system unit based on the control difference and the internal model state of the unmanned system unit; Based on the unit data of the unmanned system unit and the control gain matrix, construct an output feedback controller for the unmanned system unit; The expression of the control difference is: ， ， ， Among them, represents the control difference; represents the element in the th row and th column of the adjacency matrix of the graph structure of the communication network between each unmanned system unit in the cluster unmanned system and the external reference signal; represents the connectivity relationship between the unmanned unit and the external reference system; represents the observability index of the unmanned system unit ; and both represent intermediate variables; represents the output data of the unmanned system unit at time ; represents the system output of the external reference system at time ; represents the input data of the unmanned system unit at time ; represents the transpose.

6. A heterogeneous unmanned system data-driven cooperative output regulation device, characterized in that The described heterogeneous unmanned system data-driven cooperative output regulation device specifically includes: An internal model system construction module, which is used to construct an internal model system for each unmanned system unit in the cluster unmanned system, and connect the unmanned system unit with its corresponding internal model system, where the internal model system is constructed based on the evolution matrix of the external reference system of the cluster unmanned system; An offline collection module is used to offline collect the adjustment input sequence, adjustment output sequence of each unmanned system unit in the cluster unmanned system, and the internal model state sequence of its corresponding internal model system. For each unmanned system unit, determine the observability index of the unmanned system unit, construct a linear matrix inequality based on the observability index, the adjustment input sequence, the adjustment output sequence and the internal model state sequence, and solve the linear matrix inequality to obtain a first gain parameter and a second gain parameter. Based on the first gain parameter and the second gain parameter, determine the control gain matrix of the unmanned system unit; A controller construction module is used to construct an output feedback controller for each unmanned system unit based on the control gain matrix of each unmanned system unit to obtain the distributed output feedback controller corresponding to the cluster unmanned system, and an adjustment module is used to perform coordinated output adjustment on the cluster unmanned system through the distributed output feedback controller; The linear matrix inequality is as follows: ， ， ， ， ， ， ， ， ， Among them, represents the first gain parameter; represents the second gain parameter; , , , , all represent the identity matrix; represents the first data matrix; represents the second data matrix; , , , , , , , , are all known matrices; represents the observability index of the unmanned system unit ; represents the transpose; represents the noise upper limit value of the unmanned system unit ; , , , , , , , , , all represent the all-zero matrix; , , all represent positive integers.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the heterogeneous unmanned system data-driven cooperative output adjustment method according to any one of claims 1-5.

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