Data-driven collaborative output adjustment method and device for heterogeneous unmanned system

Abstract

The invention belongs to the technical field of cluster unmanned system cooperative control, and particularly relates to a heterogeneous unmanned system data driving cooperative output adjusting method and device. The method specifically comprises the following steps: S100, setting that the heterogeneous unmanned system is composed of a plurality of unmanned monomers and follows an external system; introducing an inner membrane system, and establishing an expansion system of each unmanned monomer according to the kinetic model of the unmanned monomer; s200, open-loop track data of an expansion system of each unmanned single body is obtained through an offline experiment, and a data matrix is obtained; s300, based on the data matrix, calculating a distributed data driving control gain matrix; and S400, based on an inner membrane system state and the control gain matrix, obtaining a distributed dynamic state feedback controller, and realizing output adjustment of the unknown model heterogeneous unmanned system.

Description

Technical Field

[0001] The present invention belongs to the field of collaborative control of clustered unmanned systems, and in particular, relates to a data-driven collaborative output adjustment method and device for heterogeneous unmanned systems. Background Art

[0002] With the interweaving advancement of a new round of scientific and technological revolution, industrial revolution and new military revolution in the world, unmanned systems represented by unmanned vehicles, unmanned aerial vehicles and unmanned ships have become the best entry point and important starting point for studying artificial intelligence theories, methods and technologies. Heterogeneous cluster unmanned systems are composed of a large number of unmanned system monomers that interact with each other and have different dynamic characteristics. The various unmanned systems within the heterogeneous cluster unmanned system complete complex tasks that a single unmanned system cannot complete through mutual communication, cooperation and competition, thereby achieving a leapfrog improvement in productivity and efficiency. Facing the application requirements of heterogeneous cluster unmanned systems tracking a trajectory signal or suppressing a specific type of interference, the study of the problem of coordinated output regulation of heterogeneous cluster unmanned systems has certain theoretical and practical application value. In addition, the strong coupling between the internal systems of heterogeneous cluster unmanned systems, the complex coordination mechanism, and the incomplete and uncertain system information caused by the complex external environment have brought huge challenges to the research on the problem of coordinated output regulation of cluster systems and attracted the attention of a wide range of scholars.

[0003] However, existing research on the problem of coordinated output regulation of heterogeneous cluster unmanned systems all adopts a model-based control method, that is, it is necessary to establish a mathematical model of the controlled object based on the system mechanism or identification method. However, with the increase in the scale of heterogeneous unmanned cluster systems and the complexity of single models, obtaining accurate mathematical models has become an important bottleneck restricting the design of distributed control strategies and the realization of control objectives in complex environments. In recent years, data-driven control methods have emerged, that is, only using data to achieve the control and analysis of unknown systems. For the data-driven control problem of a single unknown system, many scholars have carried out in-depth and detailed research. However, for the problem of coordinated output regulation of multi-body systems, due to the coupling noise in the data collected in actual scenarios, the method of achieving the control target by solving the output regulation equation can only achieve approximate output regulation, that is, it is impossible to achieve zero deviation tracking. This is because the noise in the data will cause countless system models to match the collected data, resulting in the output regulation equation that satisfies countless systems having no solution, and it is impossible to construct a data-driven output regulation controller.

[0004] Therefore, how to use noise data to realize the planning and design of control strategies for heterogeneous cluster unmanned systems with unknown models under a distributed structure, and ultimately achieve the control goal of zero-bias collaborative output regulation, has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In response to the above problems, the present invention takes into account the differences and noise data brought by the structures and dynamic performances of different types of unmanned units, and proposes a data-driven collaborative output adjustment method and device for heterogeneous unmanned systems, which can achieve zero-deviation tracking of the outputs of heterogeneous unmanned systems with unknown models and external systems.

[0006] In a first aspect, the present application embodiment provides a data-driven collaborative output adjustment method for a heterogeneous unmanned system, and the specific process is as follows:

[0007] S100, assuming that a heterogeneous unmanned system is composed of multiple unmanned monomers, which follow an external system; introducing an endomembrane system, and establishing an extended system of each unmanned monomer based on the dynamic model of the unmanned monomer;

[0008] S200, through offline experiments, obtain the open-loop trajectory data of the extended system of each unmanned unit and obtain a data matrix;

[0009] S300, calculating a distributed data driven control gain matrix based on the data matrix;

[0010] S400, based on the endomembrane system state and the control gain matrix, a distributed dynamic state feedback controller is obtained to achieve output regulation of the unknown model heterogeneous unmanned system.

[0011] Further, the endomembrane system of the present invention is:

[0012] Assume that the dynamic model of the external system is:

[0013]

[0014] y0(t)=-Fv(t)

[0015] Among them, v(t), and y0(t) are the state, state derivative and output of the external system, S and F are known real matrices; the above heterogeneous unmanned system and the external system can be uniformly regarded as an unmanned system with a leader-follower structure, in which the external system is the leader and N unmanned monomers are followers. The communication network between them is shown in the figure. To describe, It contains a directed spanning tree with the leader as the root node.

[0016] The endomembrane system is:

[0017]

[0018] Among them, z i (t) and is the state and state derivative of the internal model system, e vi (t) is the virtual tracking error;

[0019]

[0020] Among them, β is the dimension n β ×n β The constant square matrix whose characteristic polynomial is the same as the minimal polynomial of the matrix S, σ is a constant square matrix with dimension n β ×1, (β×σ) is controllable, and blockdiag(...) represents a block diagonal matrix constructed with sub-matrices on the diagonal; n y -tuple means the block diagonal matrix has n y sub-matrices.

[0021] Furthermore, the virtual tracking error of the present invention is:

[0022]

[0023] Among them, a ij It is a picture The adjacency matrix of The element in the i-th row and j-th column of i0 represents the connectivity between unmanned entity i and the external system, y i (t), y j (t) and y0(t) are the outputs of unmanned unit i, unmanned unit j and the external system respectively.

[0024] Furthermore, the present invention establishes an expansion system for each unmanned unit based on the dynamic model of the unmanned unit, and the specific process is as follows:

[0025] Assume that the dynamic model of each unmanned unit is:

[0026]

[0027] y i (t) = C i x i (t),i=1,2,...,N

[0028] Among them, x i (t) and is the state and state derivative of the unmanned cell i, u i (t) is the control input of unmanned cell i, y i (t) is the output of the unmanned unit i, v(t) is the state of the external system, and the matrix A i , B i 、E i and C i is an unknown real matrix;

[0029] The expansion system for each unmanned unit is established as follows:

[0030]

[0031] in,

[0032] Furthermore, the present invention introduces noise d in the expansion system i (t), run the extended system in open loop and obtain the data matrix.

[0033] Furthermore, the data matrix obtained in the present invention is specifically: applying control input U at T time points i- , the control input satisfies n xi +n z +1-order continuous excitation, n ui is the dimension of the control input of the unmanned unit i, n z is the state dimension of the internal model system, collecting the state X of the unmanned unit i i- and the state Z of the endomembrane system i- , the calculated state derivative X of the unmanned cell i i+ and the derivative Z of the endomembrane system state i+ ; The matrix is ​​constructed as follows:

[0034] U i- =[u i (t1)u i (t2)…u i (t T )]

[0035] X i- =[x i (t1)x i (t2)…x i (t T )]

[0036] Z i- =[z i (t1)z i (t2)…z i (t T )]

[0037]

[0038] Furthermore, the T of the present invention is xi +n z +1)×n ui +n xi +n z -1, where n xi is the state dimension of the unmanned unit i, n ui is the dimension of the control input of the unmanned unit i, n zis the state dimension of the internal model system.

[0039] Furthermore, in the present invention, with respect to step S300, the distributed data driven control gain matrix is ​​calculated based on the data matrix, specifically:

[0040] Solve the following matrix inequality for the unmanned monomer i:

[0041]

[0042] in, Y i and P i is the variable to be solved. is the maximum upper bound of the noise introduced by unmanned monomer i during the data collection stage, and I represents the identity matrix of appropriate dimension;

[0043] The control gain matrix is ​​designed as K i =Y i P i -1 / λ i , where λ i is the i-th eigenvalue of the matrix H, the matrix It is a connected graph consisting of external systems and unmanned systems. The Laplace matrix of .

[0044] Furthermore, in step S400 of the present invention, the distributed dynamic state feedback controller is designed as follows:

[0045]

[0046] Among them, a ij is the adjacency matrix The element in the i-th row and j-th column of i0 Represents the connectivity between the unmanned entity i and the external system.

[0047] In a second aspect, an embodiment of the present application provides a heterogeneous unmanned system data-driven collaborative output adjustment device, comprising the steps of:

[0048] Expand the system building module, assume that the heterogeneous unmanned system is composed of multiple unmanned monomers, which follow an external system; introduce the endomembrane system, and establish the expansion system of each unmanned monomer based on the dynamic model of the unmanned monomer;

[0049] The data acquisition module obtains the open-loop trajectory data of the extended system of each unmanned unit through offline experiments to obtain a data matrix;

[0050] A gain matrix calculation module, which calculates a distributed data driven control gain matrix based on the data matrix;

[0051] The control output module obtains a distributed dynamic state feedback controller based on the endomembrane system state and the control gain matrix to achieve output regulation of the unknown model heterogeneous unmanned system.

[0052] Beneficial Effects

[0053] First, the present invention only utilizes the state and input data collected in the open loop to design the distributed controller, eliminating the dependence of traditional control on the system model.

[0054] Second, the distributed data-driven control gain matrix design method proposed in the present invention only needs to solve a low-complexity linear matrix inequality offline, and there is no need to solve the optimization problem online, which saves computing resources and energy consumption to a certain extent.

[0055] Third, the data-driven collaborative output adjustment method for heterogeneous unmanned systems proposed in the present invention effectively solves the collaborative output adjustment problem under data interference, and for the first time realizes zero-deviation tracking of the output of each unmanned unit in the system and the output of the external system, with good robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 It is a flow chart of a heterogeneous unmanned system data-driven collaborative output adjustment method and device provided by the present invention;

[0058] Figure 2 This is a diagram of the collaborative output regulation effect in one embodiment of the present invention. DETAILED DESCRIPTION

[0059] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0060] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0061] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0062] like Figure 1 The flowchart of the present invention is a data-driven collaborative output adjustment method for heterogeneous unmanned systems, comprising the following steps:

[0063] S100, assume that a heterogeneous unmanned system is composed of multiple unmanned monomers, which follow an external system; introduce the endomembrane system, and establish an extended system for each unmanned monomer based on the dynamic model of the unmanned monomer.

[0064] In one embodiment, the heterogeneous unmanned system described in step S100 is composed of N unmanned units with different dynamic models. The dynamic model of each unmanned unit is assumed to be:

[0065]

[0066] y i (t) = C i x i (t),i=1,2,...,N

[0067] Among them, at time t>0, the state x of no one i i (t) and the derivative dimension of the state is n xi , the control input u of unmanned cell i i The dimension of (t) is n ui , the output y of unmanned cell i i The dimension of (t) is n y The dimension of the state of the external system v(t) is n v , generally including the reference signal to be tracked and the interference signal to be suppressed. Matrix A i Yes xi ×n xi An unknown real matrix of dimension B i Yes xi ×n ui The unknown real matrix of dimension E i Yes xi ×n vAn unknown real matrix of dimension C i Yes y ×n xi Real matrix of unknown dimension.

[0068] The dynamic model of the external system is:

[0069]

[0070] y0(t)=-Fv(t)

[0071] Among them, the state derivative of the external system and the dimensions of the output y0(t) are n v and n y , the matrix S is n v ×n v dimension known real matrix, and no eigenvalues ​​with negative real parts, F is n y ×n v A real matrix of known dimension.

[0072] The above heterogeneous unmanned systems and external systems can be uniformly regarded as an unmanned system with a leader-follower structure, where the external system is the leader and N unmanned units are followers. The communication network between them can be represented by the figure To describe, It contains a directed spanning tree with the leader as the root node.

[0073] Based on the definition of the dynamic model of the external system and the unmanned monomer system, the extended closed-loop system in step S100 is established by:

[0074] S111, introduction of the endomembrane system:

[0075]

[0076] Among them, the state z of the internal model system i (t) and state derivatives The dimension is n z The matrices G1 and G2 are the inner membrane matrices related to the matrix S of the external system and are designed as:

[0077]

[0078] Among them, β is the dimension n β ×n β The constant square matrix, whose characteristic polynomial is the same as the minimal polynomial of the matrix S, blockdiag(β,...,β) represents the block diagonal matrix constructed with β as the submatrix on the diagonal, n y -tuple indicates that the block diagonal matrix has n ysubmatrices, σ is a matrix with dimension n β ×1, as long as (β×σ) is controllable.

[0079] e vi (t) is the virtual tracking error, designed as:

[0080]

[0081] Among them, a ij It is a picture The adjacency matrix of The element in the i-th row and j-th column of i0 represents the connectivity between unmanned entity i and the external system, y i (t), y j (t) and y0(t) are the outputs of unmanned unit i, unmanned unit j and the external system respectively.

[0082] S112, define the extension vector Its dimension is n ξi =n xi +n z , and the extended system is constructed as:

[0083]

[0084] in,

[0085] S200, through offline experiments, the open-loop trajectory data of the extended system of each unmanned monomer is collected to obtain the input data matrix, the monomer state matrix and the inner membrane system input matrix.

[0086] In one embodiment, the data matrix in step S200 is constructed by:

[0087] S211, introducing noise d into the extended system in step S100 i (t), run the above extended system in open loop and collect noise data:

[0088]

[0089] Specifically, for any period of time from 0 to T1, random generation is performed in real time to input an unknown unmanned monomer i, and the state of the unmanned monomer and the endomembrane state during this period are obtained. In the period from 0 to T1, any T time points are sampled to satisfy 0<t1<t2<…<t T ≤T1,T=(n xi +n z +1)×n ui +n xi +n z-1. If the input sequence corresponding to T time points is to satisfy n xi +n z +1 order continuous excitation, then record these time points and this sequence input. Record the state data of the unmanned cell i at the corresponding time Endometrial status data When recording the state of the unmanned monomer i and the state of the endomembrane system at time τ, the state data x of the two moments that are very close to each other are i (τ-Δt), x i (τ+Δt), z i (τ-Δt) and z i (τ+Δt) is recorded, where Δt is as close to zero as possible. For each moment τ, the derivative of the unmanned cell i state is estimated to be The derivative of the state of the endomembrane system is

[0090] S212, constructing the input data matrix U of the unmanned monomer i using the applied control input, the collected state of the unmanned monomer i, the collected state of the endomembrane system, the calculated state derivative of the unmanned monomer i, and the calculated state derivative of the endomembrane system i- , the state data matrix X of the unknown physical process i- , the endomembrane system state data matrix Z i- , the state derivative data matrix X of the unknown physical process i+ , the endomembrane system state derivative data matrix Z i+ ,as follows:

[0091] U i- =[u i (t1)u i (t2)…u i (t T )]

[0092] X i- =[x i (t1)x i (t2)…x i (t T )]

[0093] Z i- =[z i (t1)z i (t2)…z i (t T )]

[0094]

[0095] S300, calculating a distributed data driven control gain matrix based on the data matrix.

[0096] In one embodiment, the distributed data driven control gain matrix design method in step S300 includes the following specific design steps:

[0097] S311, set a given normal number As the maximum upper bound of the noise introduced by unattended monomer i during the data collection phase, this noise includes the derivative estimation error and the external signal during the data collection phase. The following matrix is ​​constructed using the data matrix constructed in step S200: Augmented state data matrix Augmented state derivative data matrix as well as

[0098]

[0099] where I represents the identity matrix of appropriate dimension.

[0100] S312, based on the above data matrix, solve the following matrix inequality for each unmanned monomer i:

[0101]

[0102] Among them, Y i and P i is the variable to be solved, the matrix P i n ξi ×n ξi The positive definite real symmetric matrix of dimension i n ui ×n ξi Dimensional matrix. Therefore, the control gain matrix is ​​designed as K i =Y i P i -1 / λ i , where λ i is the i-th eigenvalue of the matrix H, the matrix It is a picture The Laplace matrix of , Λ is given by a i0 , i=1,2,…,N is a diagonal matrix, that is where a i0 Represents the connection relationship between unmanned monomer i and the external system. If unmanned monomer i can obtain information from the external system, then a i0 =1, otherwise 0. .

[0103] S400, based on the endomembrane system state in S100 and the control gain matrix obtained in S300, a distributed dynamic state feedback controller is obtained to realize the output regulation of the unknown model heterogeneous unmanned system.

[0104] In one embodiment, the distributed dynamic state feedback controller in step S400 is designed as follows:

[0105]

[0106] During the online operation phase, the unmanned cell i of the unknown model sends the current state x to the above distributed dynamic state feedback controller at every moment. i (t), and at the same time the inner membrane system sends the current inner membrane state z to the distributed dynamic state feedback controller i (t), the above distributed dynamic state feedback controller generates the control input u i (t) Send back the unknown unmanned cell i to realize the output y of the unknown unmanned cell i i (t) asymptotically tracks the output y0(t) of the external system, i.e., realizes the output regulation of the heterogeneous unmanned system with the unknown model.

[0107] In one embodiment, a simulation experiment is performed to verify the feasibility and effectiveness of the data driven output synchronization strategy design method of the present invention.

[0108] like Figure 2 The figure shows the effect of running the method of the present invention for 50 seconds in an embodiment on a multi-robot system. The corresponding system matrix is:

[0109]

[0110] Where i = 1, 2, 3, 4, ω1 = π / 5, ω2 = 1. According to step S100, the inner membrane matrices G1 and G2 are designed as follows:

[0111]

[0112] Select parameter T = 20, The four sub-graphs in the figure represent the coordinated output adjustment effect diagrams of the four robots. In each sub-graph, the solid line is the output of the external system that is expected to be tracked, the dotted line is the output of the robot, and the dotted line is the tracking error. It can be seen that the output of each robot can track the output of the external system without deviation, that is, the output adjustment of the multi-robot system is realized, which proves the effectiveness of the proposed data-driven coordinated output adjustment method.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A data-driven collaborative output adjustment method for heterogeneous unmanned systems, characterized in that: Includes steps: S100, assuming that a heterogeneous unmanned system is composed of multiple unmanned monomers, which follow an external system; introducing an endomembrane system, and establishing an extended system of each unmanned monomer based on the dynamic model of the unmanned monomer; S200, through offline experiments, obtain the open-loop trajectory data of the extended system of each unmanned unit and obtain a data matrix; S300, calculating a distributed data driven control gain matrix based on the data matrix; S400, based on the endomembrane system state and the control gain matrix, a distributed dynamic state feedback controller is obtained to achieve output regulation of the heterogeneous unmanned system.

2. According to claim 1, the method for adjusting the data-driven collaborative output of heterogeneous unmanned systems is characterized in that: The endomembrane system is: Assume that the dynamic model of the external system is: y0(t)=-Fv(t) Among them, v(t), and y0(t) are the state, state derivative and output of the external system, S and F are known real matrices; The endomembrane system is: Among them, z i (t) and is the state and state derivative of the internal model system, e vi (t) is the virtual tracking error; Among them, β is the dimension n β ×n β The constant square matrix whose characteristic polynomial is the same as the minimal polynomial of the matrix S, σ is a constant square matrix with dimension n β ×1, blockdiag(...) represents a block diagonal matrix constructed with sub-matrices on the diagonal; n y -tuple means the block diagonal matrix has n y sub-matrices.

3. According to claim 2, the method for adjusting the data-driven collaborative output of heterogeneous unmanned systems is characterized in that: The virtual tracking error is: Among them, a ij It is a picture The adjacency matrix of The element in the i-th row and j-th column of i0 represents the connectivity between unmanned entity i and the external system, y i (t), y j (t) and y0(t) are the outputs of unmanned cell i, unmanned cell j and the external system respectively; A network diagram to describe the communication between heterogeneous unmanned systems and external systems.

4. According to claim 1, the method for adjusting the data-driven collaborative output of heterogeneous unmanned systems is characterized in that: According to the dynamic model of the unmanned unit, the expansion system of each unmanned unit is established. The specific process is as follows: Assume that the dynamic model of each unmanned unit is: y i (t)=C i x i (t),i=1,2,...,N Among them, x i (t) and is the state and state derivative of the unmanned cell i, u i (t) is the control input of unmanned cell i, y i (t) is the output of the unmanned unit i, v(t) is the state of the external system, and the matrix A i , B i 、E i and C i is a real matrix; The expansion system for each unmanned unit is established as follows: in, 5. According to claim 4, the method for adjusting the data-driven collaborative output of heterogeneous unmanned systems is characterized in that: The noise introduced in the extended system d i (t), run the extended system in open loop and obtain the data matrix.

6. The method for adjusting heterogeneous unmanned system data-driven collaborative output according to claim 5, characterized in that: The specific method of obtaining the data matrix is: applying control input U at T time points i- , the control input satisfies n xi +n z +1-order continuous excitation, n ui is the dimension of the control input of the unmanned unit i, n z is the state dimension of the internal model system, collecting the state X of the unmanned unit i i- and the state Z of the endomembrane system i- , the calculated state derivative X of the unmanned cell i i+ and the derivative Z of the endomembrane system state i+ ; The matrix is ​​constructed as follows:

7. The method for adjusting heterogeneous unmanned system data-driven collaborative output according to claim 6, characterized in that: The T=(n xi +n z +1)×n ui +n xi +n z -1, where n xi is the state dimension of the unmanned unit i, n ui is the dimension of the control input of the unmanned unit i, n z is the state dimension of the internal model system.

8. The method for adjusting heterogeneous unmanned system data-driven collaborative output according to claim 6, characterized in that: For step S300, the distributed data driven control gain matrix is ​​calculated based on the data matrix, specifically: Solve the following matrix inequality for the unmanned monomer i: in, Y i and P i is the variable to be solved; is the maximum upper bound of the noise introduced by unmanned monomer i during the data collection stage, and I represents the identity matrix of appropriate dimension; The control gain matrix is ​​designed as K i =Y i P i -1 / λ i , where λ i is the i-th eigenvalue of the matrix H, the matrix It is a connected graph consisting of external systems and unmanned systems. The Laplace matrix of .

9. The method for adjusting heterogeneous unmanned system data-driven collaborative output according to claim 1, characterized in that: Step S400, the distributed dynamic state feedback controller is designed as follows: Among them, K i is the control gain matrix, a ij is the adjacency matrix The element in the i-th row and j-th column of i0 Represents the connectivity between the unmanned entity i and the external system.

10. A data-driven collaborative output adjustment device for heterogeneous unmanned systems, characterized in that: Includes steps: Expand the system building module, assume that the heterogeneous unmanned system is composed of multiple unmanned monomers, which follow an external system; introduce the endomembrane system, and establish the expansion system of each unmanned monomer based on the dynamic model of the unmanned monomer; The data acquisition module obtains the open-loop trajectory data of the extended system of each unmanned unit through offline experiments to obtain a data matrix; A gain matrix calculation module, which calculates a distributed data driven control gain matrix based on the data matrix; The control output module obtains a distributed dynamic state feedback controller based on the endomembrane system state and the control gain matrix to achieve output regulation of the heterogeneous unmanned system.

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