Unmanned aerial vehicle cluster distributed output formation tracking control method and related device

Abstract

The invention discloses an unmanned aerial vehicle cluster distributed output formation tracking control method and a related device, and relates to the technical field of unmanned aerial vehicle collaborative optimization control, and the method comprises the steps: obtaining state parameters of each unmanned aerial vehicle in an unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster is a heterogeneous unmanned aerial vehicle cluster; establishing a heterogeneous unmanned aerial vehicle cluster dynamic model and a reference trajectory optimization model according to the state parameters of the unmanned aerial vehicles; the reference trajectory optimization model is a model formed based on a reference trajectory and a local time-varying cost function of the reference trajectory; based on the heterogeneous unmanned aerial vehicle cluster dynamics model and the reference trajectory optimization model, determining an output formation tracking control error, and when the output formation tracking control error converges, outputting an optimized unmanned aerial vehicle cluster tracking control instruction; and determining an optimal tracking control protocol of each unmanned aerial vehicle in the unmanned aerial vehicle cluster according to the unmanned aerial vehicle formation tracking control instruction. The cooperative control capability of the unmanned aerial vehicle cluster is improved.

Description

Technical Field

[0001] The present application relates to the technical field of collaborative optimization control of unmanned aerial vehicles (UAVs), and in particular to a distributed output formation tracking control method for a UAV cluster and related devices. Background Art

[0002] Formation tracking control of drone swarms has become an increasingly popular research topic due to its widespread applications in target interception, collaborative search, and other fields. Consensus-based formation tracking control, as a highly scalable distributed control system, has been well studied. However, most existing formation tracking control methods focus on predefined tracking trajectories. In complex environments or with unknown moving targets, the reference trajectory is unpredictable. In this context, some research has employed optimization methods to obtain the formation reference trajectory.

[0003] However, current research on the optimal tracking control of UAV swarm formations has not considered heterogeneous high-order linear swarm systems. At the same time, considering that the formation reference trajectory is time-varying optimization, the distributed output formation optimal tracking control of heterogeneous linear swarm systems based on distributed time-varying optimization remains a problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a distributed output formation tracking control method and related devices for a drone cluster, which can improve the collaborative control capability of the drone cluster.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a distributed output formation tracking control method for a swarm of drones, comprising:

[0007] Obtaining status parameters of each drone in a drone cluster; the drone cluster is a heterogeneous drone cluster;

[0008] According to the state parameters of each UAV, a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model are established; the reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory;

[0009] Based on the heterogeneous UAV swarm dynamics model and the reference trajectory optimization model, the output formation tracking control error is determined, and when the output formation tracking control error converges, the optimized UAV swarm tracking control instructions are output;

[0010] According to the UAV formation tracking control instructions, the optimal tracking control protocol for each UAV in the UAV cluster is determined.

[0011] Optionally, the formula expression of the heterogeneous UAV cluster dynamics model is:

[0012]

[0013] in, and Represents state, control input and output respectively, (A i ,B i ) means calm, (C i ,A i ) indicates detectable, A i , B i , C i represent the system matrix, input matrix, and output matrix respectively.

[0014] Optionally, the reference trajectory optimization model is expressed as follows:

[0015]

[0016] in, represents the reference trajectory, f i (θ(t),t) represents the local time-varying cost function of θ(t), Represents an inequality constraint.

[0017] Optionally, after establishing the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, the following steps are also included:

[0018] Determine the reference trajectory of each UAV, including:

[0019] According to the formula Determine the reference trajectory of each UAV.

[0020] Optionally, the local time-varying cost function f i The calculation method of (θ(t),t) is:

[0021] Determine f i (θ i The compensation cost function J (t), t) i (θ i (t),t);

[0022] Where γ(t) represents the compensation cost function, s(t) represents the relaxation function,

[0023] H i Indicates J i (θ i (t),t) about θ i Hessian matrix of (t); Indicates J i (θ i(t),t) about θ i The gradient of (t), and express The partial derivatives of t, s(t) and γ(t) respectively.

[0024] Optionally, the output formation tracking control error convergence formula expression is:

[0025]

[0026] in, Indicates the desired formation. represents the optimal reference trajectory, Indicates output.

[0027] Optionally, the optimal tracking control protocol specifically includes:

[0028]

[0029] in, and represents the gain matrix to be determined, τ i (t) represents the input compensation term to be determined, represents a constant to be determined positive, and σ ij Represents auxiliary variables.

[0030] In a second aspect, the present application provides a distributed output formation tracking control device for a drone cluster, comprising:

[0031] A parameter acquisition module is used to obtain the status parameters of each drone in the drone cluster; the drone cluster is a heterogeneous drone cluster;

[0032] A model building module is used to establish a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model based on the state parameters of each UAV; the reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory;

[0033] The output module is used to determine the output formation tracking control error based on the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, and output the optimized UAV cluster tracking control instruction when the output formation tracking control error converges;

[0034] The protocol determination module is used to determine the optimal tracking control protocol for each drone in the drone cluster based on the drone formation tracking control instructions.

[0035] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for distributed output formation tracking and control of a drone cluster.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for distributed output formation tracking and control of a drone cluster.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0038] This application provides a distributed output formation tracking control method and related device for a swarm of unmanned aerial vehicles (UAVs). First, by acquiring the state parameters of each UAV, a dynamic model and a reference trajectory optimization model are established, and then the output formation tracking control error is determined. When the control error converges, the optimized UAV swarm tracking control instruction is output to achieve collaborative optimization control of the UAV swarm. Then, based on the reference trajectory and the local time-varying cost function of the reference trajectory, the reference trajectory optimization model is constructed, which improves the accuracy and robustness of the UAV swarm tracking control. Finally, based on the UAV formation tracking control instruction, the optimal tracking control protocol for each UAV in the swarm is determined, thus achieving distributed output formation tracking control of the UAV swarm and improving the collaborative control capability of the UAV swarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is an application environment diagram of a distributed output formation tracking control method for a UAV cluster in one embodiment of the present application;

[0041] Figure 2 A flowchart of a distributed output formation tracking control method for a drone swarm provided in one embodiment of the present application;

[0042] Figure 3 An interactive topology diagram provided in an embodiment of the present application;

[0043] FIG4 is a graph showing the local reference trajectory and the optimal reference trajectory of a UAV on the X-axis and Y-axis according to an embodiment of the present application;

[0044] Figure 5 A graph of a constraint function and a relaxation function provided in an embodiment of the present application;

[0045] Figure 6 A snapshot diagram of four drones achieving optimal tracking of an output formation according to an embodiment of the present application;

[0046] Figure 7 This is a graph showing the optimal tracking error of the output formation of four drones provided in one embodiment of the present application;

[0047] Figure 8 A schematic diagram of the functional modules of a distributed output formation tracking control device for a swarm of drones provided in one embodiment of the present application;

[0048] Figure 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0051] The distributed output formation tracking control method for drone clusters provided in the embodiments of the present application can be applied to Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be set up separately, integrated with server 104, or located in the cloud or on another server. Terminal 102 can send the state parameters of each drone to be processed to server 104. After receiving the state parameters of each drone to be processed, server 104 establishes a heterogeneous drone cluster dynamics model and a reference trajectory optimization model based on the state parameters of each drone. The reference trajectory optimization model is a model based on the reference trajectory and a local time-varying cost function of the reference trajectory. Based on the heterogeneous drone cluster dynamics model and the reference trajectory optimization model, an output formation tracking control error is determined. When the output formation tracking control error converges, an optimized drone cluster tracking control instruction is output. Based on the drone formation tracking control instruction, the optimal tracking control protocol for each drone in the drone cluster is determined. Server 104 can provide feedback on the obtained optimal tracking control protocol for each drone to terminal 102. In addition, in some embodiments, the distributed output formation tracking control method of the drone cluster can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the state parameters of each drone to be processed, or the server 104 can obtain the state parameters of each drone to be processed from the data storage system and process the state parameters of each drone to be processed.

[0052] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0053] In an exemplary embodiment, Figure 2 As shown, a method for tracking and controlling a distributed output formation of a UAV cluster is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 204.

[0054] Step 201: obtaining status parameters of each drone in a drone cluster; the drone cluster is a heterogeneous drone cluster;

[0055] Step 202: Establish a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model based on the state parameters of each UAV; the reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory;

[0056] Step 203: Determine an output formation tracking control error based on the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, and output an optimized UAV cluster tracking control instruction when the output formation tracking control error converges.

[0057] Step 204 : Determine the optimal tracking control protocol for each UAV in the UAV cluster based on the UAV formation tracking control instruction.

[0058] In an exemplary embodiment, when executing steps 201-204, the specific steps may be as follows:

[0059] This embodiment considers n drones and constructs a heterogeneous drone cluster dynamics model based on the state parameters of the n drones. Specifically,

[0060]

[0061] Where, and Represents state, control input and output respectively. (A i ,B i ) indicates stabilization, and (C i ,A i ) indicates detectable. rank(B i )=q i .

[0062] Among them, the constructed reference trajectory optimization model is specifically:

[0063]

[0064] in represents the reference trajectory, f i (θ(t), t) represents the local time-varying cost function with respect to θ(t), represents the inequality constraint, θ * (t) represents the optimal solution of the optimization problem (2).

[0065] After obtaining the reference optimized trajectory model, define the local reference trajectory θ for each UAV i i (t), then formula (2) can be equivalent to the following optimization problem:

[0066]

[0067] Definition about fi (θ i The compensation cost function J (t), t) i (θ i (t),t), the specific formula is as follows:

[0068]

[0069] Where γ(t) represents the compensation cost function and s(t) represents the relaxation function, which satisfies the following:

[0070]

[0071] H i Indicates J i (θ i (t),t) about θ i Hessian matrix of (t); Indicates J i (θ i (t),t) about θ i The gradient of (t). as well as express The partial derivatives of t, s(t) and γ(t) respectively.

[0072] Among them, based on the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, the output formation tracking control error is determined, and when the output formation tracking control error converges, the optimized UAV cluster tracking control instruction is output. The specific details can be as follows:

[0073] If the output formation optimal tracking error converges, that is,

[0074]

[0075] Then the heterogeneous UAV cluster realizes output affine formation maneuver. Indicates the desired formation. represents the optimal reference trajectory.

[0076] Then, when the error converges, the optimal tracking control protocol for the output formation is designed for each follower according to the conditions satisfied by the control parameters.

[0077] Specifically, consider the following output formation optimal tracking control protocol:

[0078]

[0079] Where h i (t) satisfies h Hi (t) = C i h i(t) is used to ensure that the output of the UAVs can achieve the desired formation. and represents the gain matrix to be determined, τ i (t) represents the input compensation term to be determined.

[0080] Represents a constant to be determined positive.

[0081] Specifically, the parameters that need to be determined in the output formation optimal tracking control protocol (7) can be designed according to the following steps:

[0082] 1) Choose a positive constant And μ1,μ2 make

[0083] 2) There exists a reversible matrix in Make and Select h i (t) satisfies the following constraints:

[0084]

[0085] Among them, τ i (t) is calculated according to the following formula:

[0086]

[0087] 3) Select Make Hurwitz.

[0088] choose Satisfies the linear matrix equation, the formula is as follows:

[0089]

[0090] choose

[0091] In addition, this application also provides a specific embodiment, which uses a numerical simulation example to illustrate the effectiveness of the proposed output formation optimal tracking control method. The method is applied to four heterogeneous drones in a two-dimensional plane. The interaction topology is as follows Figure 3 As shown. Among them, the expected formation is:

[0092]

[0093] make Represents the local reference trajectory of each UAV i. Local time-varying cost function f i (θ i(t),t) and inequality constraint functions Expressed as Choose γ(t) = e 0.05t , s(t)=10e -2t .

[0094] Figure 4 shows the local reference trajectory θ i (t) curve. It can be seen from the figure that all local reference trajectories converge to the optimal reference trajectory θ * (t), Figure 4 (a) is a graph of the local reference trajectory of the UAV and the optimal reference trajectory on the Y axis, and Figure 4 (b) is a graph of the local reference trajectory of the UAV and the optimal reference trajectory on the Y axis. Figure 5 It can be seen from the inequality Always satisfied. Figure 6 The process of four UAVs completing the output formation optimal tracking is shown, where squares represent UAVs and five-pointed stars represent the optimal reference trajectory. Figure 7 The convergence curve of the output formation optimal tracking error is shown. This simulation example shows that under the output formation optimal tracking control method proposed in this embodiment, a heterogeneous UAV swarm can form a formation while simultaneously tracking the optimal reference trajectory.

[0095] Based on the same inventive concept, the present application also provides an apparatus for implementing the aforementioned method for tracking and controlling a distributed output formation of a swarm of unmanned aerial vehicles. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more apparatus embodiments provided below can be found in the aforementioned limitations on the method for tracking and controlling a distributed output formation of a swarm of unmanned aerial vehicles, and will not be further elaborated here.

[0096] In an exemplary embodiment, Figure 8 As shown, a distributed output formation tracking control device for a UAV cluster is provided, comprising:

[0097] The parameter acquisition module 801 is used to obtain the status parameters of each drone in the drone cluster; the drone cluster is a heterogeneous drone cluster;

[0098] Model building module 802, for establishing a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model based on the state parameters of each UAV; the reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory;

[0099] The output module 803 is used to determine the output formation tracking control error based on the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, and output the optimized UAV cluster tracking control instruction when the output formation tracking control error converges;

[0100] The protocol determination module 804 is used to determine the optimal tracking control protocol for each drone in the drone cluster according to the drone formation tracking control instruction.

[0101] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the optimal tracking control protocol of each drone in the drone cluster. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distributed output formation tracking control method for a drone cluster is implemented.

[0102] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0103] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0104] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0106] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0107] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0108] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0109] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A distributed output formation tracking control method for a swarm of unmanned aerial vehicles, characterized in that: include: Obtaining status parameters of each drone in a drone cluster; the drone cluster is a heterogeneous drone cluster; According to the state parameters of each UAV, a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model are established; the reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory; Based on the heterogeneous UAV swarm dynamics model and the reference trajectory optimization model, the output formation tracking control error is determined, and when the output formation tracking control error converges, the optimized UAV swarm tracking control instructions are output; According to the UAV formation tracking control instructions, the optimal tracking control protocol for each UAV in the UAV cluster is determined.

2. The distributed output formation tracking control method for a UAV swarm according to claim 1, characterized in that: The formula expression of the heterogeneous UAV cluster dynamics model is: in, and Represents the state, control input and output respectively, (A i ,B i ) means calm, (C i ,A i ) indicates detectable, A i , B i , C i represent the system matrix, input matrix, and output matrix respectively.

3. The distributed output formation tracking control method for a UAV swarm according to claim 1, characterized in that: The formula expression of the reference trajectory optimization model is: in, represents the reference trajectory, f i (θ(t),t) represents the local time-varying cost function of θ(t), Represents an inequality constraint.

4. The distributed output formation tracking control method for a UAV swarm according to claim 1, characterized in that: After establishing the heterogeneous UAV cluster dynamics model and reference trajectory optimization model, it also includes: Determine the reference trajectory of each UAV, including: According to the formula Determine the reference trajectory of each UAV.

5. The distributed output formation tracking control method for a UAV swarm according to claim 4, characterized in that: Local time-varying cost function f i The calculation method of (θ(t),t) is: Determine f i (θ i The compensation cost function J (t), t) i (θ i (t),t); Where γ(t) represents the compensation cost function, s(t) represents the relaxation function, H i Indicates J i (θ i (t),t) about θ i Hessian matrix of (t); Indicates J i (θ i (t),t) about θ i The gradient of (t), and express The partial derivatives of t, s(t) and γ(t) respectively.

6. The distributed output formation tracking control method for a UAV swarm according to claim 1, characterized in that: The formula for outputting the formation tracking control error convergence is: in, Indicates the desired formation. represents the optimal reference trajectory, Indicates output.

7. The distributed output formation tracking control method for a UAV swarm according to claim 1, characterized in that: The optimal tracking control protocol specifically includes: in, and Y i represents the gain matrix to be determined, τ i (t) represents the input compensation term to be determined, μ1, μ2 are constants to be determined, φ i 、 η i , p1, p2 and σ ij Represents auxiliary variables.

8. A distributed output formation tracking control device for a drone cluster, characterized in that: include: A parameter acquisition module is used to obtain the status parameters of each drone in the drone cluster; the drone cluster is a heterogeneous drone cluster; The model building module is used to establish a heterogeneous UAV cluster dynamics model and a reference trajectory optimization model based on the state parameters of each UAV; The reference trajectory optimization model is a model based on the reference trajectory and the local time-varying cost function of the reference trajectory; The output module is used to determine the output formation tracking control error based on the heterogeneous UAV cluster dynamics model and the reference trajectory optimization model, and output the optimized UAV cluster tracking control instruction when the output formation tracking control error converges; The protocol determination module is used to determine the optimal tracking control protocol for each drone in the drone cluster based on the drone formation tracking control instructions.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a distributed output formation tracking control method for a drone cluster 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 a distributed output formation tracking control method for a drone cluster according to any one of claims 1 to 7.

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