CN-121657486-B - Unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on directed support tree

CN121657486BCN 121657486 BCN121657486 BCN 121657486BCN-121657486-B

Abstract

The invention relates to an unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on a directed support tree, which comprises the steps of constructing a multi-agent system related to an unmanned aerial vehicle, defining the multi-agent system to interact on a directed communication graph, wherein the directed communication graph comprises a directed support tree, designing a deterministic equivalent controller for the multi-agent system, expanding the deterministic equivalent controller to time-varying coupling gain, performing parameter design of the time-varying coupling deterministic equivalent controller, finding the directed support tree from the directed communication graph by utilizing a breadth-first search algorithm, constructing an undirected version of the directed support tree, and circularly updating the system state through the directed communication graph and the undirected support tree to realize the unmanned aerial vehicle cluster flight attitude self-adaptive consistency control. According to the invention, multi-agent self-adaptive non-leader consistency is realized on the directed support tree, meanwhile, the difficulty brought by uncertainty in a model in a communication diagram is overcome, and unmanned aerial vehicle cluster flight attitude self-adaptive consistency control is realized.

Inventors

YUE DONGDONG
Geng Chaofan
SHI JIANTAO
CHEN CHUANG
BAO DAN

Assignees

南京工业大学

Dates

Publication Date: 20260508
Application Date: 20260206

Claims (9)

1. The unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on the directed support tree is characterized by comprising the following steps of: s1, constructing a roll gesture dynamic model of an unmanned aerial vehicle cluster, abstracting the model into a multi-agent network with parameter uncertainty, and constructing a multi-agent system; the multi-agent system is mathematically represented as follows: ; Wherein, the Is the first The system state of the unmanned aerial vehicle comprises a roll angle and a roll angle rate, As a matrix of states, In order to control the matrix, Is the first Unknown parameter array and symbol of unmanned aerial vehicle The transpose of the matrix is represented, Is a node The corresponding known bounded and lipsticks continuous regression function, Is the first The control input of the individual unmanned aerial vehicle, For the total number of unmanned aerial vehicles, The time of day is indicated as such, Represent the first A system state track of the unmanned aerial vehicle; s2, defining a multi-agent system to interact on a directed communication graph taking the unmanned aerial vehicle as a node, wherein the directed communication graph comprises a directed support tree; S3, designing a deterministic equivalent controller for the multi-agent system based on the S2 and the coupling gain condition, wherein the deterministic equivalent controller updates a control input based on an estimated parameter, and the estimated parameter is kept limited; S4, the coupling gain in the deterministic equivalent controller is popularized to the time-varying coupling gain to obtain the time-varying coupling deterministic equivalent controller, wherein the time-varying coupling deterministic equivalent controller updates the control input based on the estimated parameter and the time-varying coupling gain, and the estimated parameter and the time-varying coupling gain are kept in a limit; the time-varying coupled deterministic equivalent controller is expressed mathematically as: ; ; ; Wherein, the Is the first Control input of individual node, i.e. the first A control input of the personal unmanned aerial vehicle; is the coupling gain; For state feedback gain, sign The transpose of the matrix is represented, In order to control the matrix, Is a symmetric positive definite matrix and satisfies ; Representing nodes in a directed connectivity graph 、 Weighting of the edges; Representing nodes System state of (2); Is a node Is used for estimating parameters of the (a); Is a node A corresponding known bounded and lipsticks continuous regression function; Representing nodes Is used for estimating the derivative of the parameter; A gain matrix arbitrarily determined; Is a node A set of ingress neighbor nodes in the directed connectivity graph, Is a node A set of ingress neighbor nodes in a directed support tree; Is a node A set of outgoing neighbor nodes in the directed support tree; Representing nodes Entering neighbor nodes in the directed communication graph; Representing nodes Neighbor nodes in the directed support tree; representing the time-varying coupling gain of the coupler, Indicating the time of day Time-varying coupling gain of (a); representing connection nodes in directed connectivity graph 、 Is provided with a pair of side edges, Representing a set of edges in a directed support tree, Indicating the gain to be designed and, Representing the derivative of the time-varying coupling gain, Representing a pending gain matrix; s5, designing time-varying coupling gain based on coupling gain conditions, selecting any positive gain matrix, solving state feedback gain and symmetric positive gain matrix, and completing parameter design of the time-varying coupling deterministic equivalent controller; s6, utilizing a breadth-first search algorithm to find a directed support tree from the directed communication graph from the root node; S7, constructing an undirected version of the directed support tree, so that the nodes can mutually transmit information; S8, updating control input in the time-varying coupling deterministic equivalent controller through the directed communication graph, updating estimation parameters in the time-varying coupling deterministic equivalent controller through an undirected version of the directed support tree, realizing cyclic updating of the unmanned aerial vehicle system state, and realizing unmanned aerial vehicle cluster flight attitude self-adaptive consistency control.
2. The unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on the directed support tree according to claim 1, wherein the roll attitude dynamics model of the unmanned aerial vehicle cluster is expressed mathematically as follows: ; Wherein, the Is the transverse rolling angle of the unmanned aerial vehicle, Is the roll angle rate of the unmanned aerial vehicle, The non-linear dynamics may be parameterized, For the control input to be designed, Represents the track of the roll angle of the unmanned aerial vehicle, And (5) representing the roll angle rate track of the unmanned aerial vehicle.
3. The unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree according to claim 1, wherein the deterministic equivalent controller is mathematically expressed as follows: ; ; Wherein, the Is the first Control input of individual node, i.e. the first A control input of the personal unmanned aerial vehicle; is the coupling gain; Is a state feedback gain and satisfies Symbol, symbol The transpose of the matrix is represented, In order to control the matrix, Is a symmetric positive definite matrix and satisfies ; Representing nodes in a directed connectivity graph 、 Weighting of the edges; Representing nodes System state of (2); Is a node Is used for estimating parameters of the (a); Is a node A corresponding known bounded and lipsticks continuous regression function; Representing nodes Is used for estimating the derivative of the parameter; A gain matrix arbitrarily determined; Is a node A set of ingress neighbor nodes in the directed connectivity graph, Is a node A set of ingress neighbor nodes in a directed support tree; Is a node A set of outgoing neighbor nodes in the directed support tree; Representing nodes Entering neighbor nodes in the directed communication graph; Representing nodes Neighbor nodes in the directed support tree.
4. The unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree according to claim 3, wherein the multi-agent system is considered with the deterministic equivalent controller, and the unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree is characterized in that Wherein Is of the type Is to satisfy step S2 and the coupling gain Under the condition that all agents in the multi-agent system can be asymptotically consistent and all estimated parameters Remains bounded in that As a matrix of states, A laplacian matrix representing a directed-connectivity graph, Representation of Is used to determine the second small characteristic value of (c), The representation takes the real part of the component, Representation of The order-unit matrix is used for the data processing, Representing the order.
5. The unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree according to claim 4, wherein: Considering the time-varying coupled deterministic equivalent controller for the multi-agent system, wherein the instructions cause All agents in the multi-agent system achieve asymptotic agreement and all estimated parameters And time-varying coupling gain Remains bounded.
6. The unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree according to claim 5, wherein the solving the state feedback gain and the symmetric positive definite matrix comprises the following steps: The symmetrical positive definite matrix is solved by the following two modes And state feedback gain : ; 。
7. The unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on the directed support tree according to claim 1, wherein the constructing the undirected version of the directed support tree comprises: leading up to The strip edge, the undirected version thereof is obtained, Representing the total number of nodes, i.e. the total number of drones.
8. An electronic device comprising at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the directed support tree based unmanned aerial vehicle cluster flight attitude adaptive consistency control method of any of claims 1-7.
9. A computer readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the unmanned aerial vehicle cluster flight attitude adaptive consistency control method based on a directed support tree according to any one of claims 1 to 7 when executed.

Description

Unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on directed support tree Technical Field The invention relates to the technical field of self-adaptive control, in particular to an unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on a directed support tree. Background With the rapid development of modern society, multi-agent systems have presented tremendous application and development prospects in many fields, and multi-agent consistency has gradually become a popular research and development in the field of automatic control. Unmanned aerial vehicle technology begins to have wide application in fields such as farmland seeding, forest search and rescue. The unmanned aerial vehicle unit consistency control is an important part of the autonomous cooperative control of the multi-agent system, the unmanned aerial vehicle unit starts from an initial gesture, a certain control algorithm is adopted, respective state update is realized through information communication among unmanned aerial vehicles, and then the unmanned aerial vehicle unit is gradually adjusted to form expected gesture consistency so as to cope with different environments. Non-leadership consistency in multi-agent systems is a classical problem, and all agents must coordinate entirely autonomously due to the lack of a unified leader. In general, graphs of a non-leadership consistency algorithm when implemented in a multi-agent system are generally assumed to be undirected structures, meaning that the communication flows have symmetry. However, in real-world networks, there is a ubiquitous flow of directional information in scenes such as web pages, social networks, unidirectional traffic networks, and the like. Directed graphs take undirected graphs as a specific example thereof, and each undirected edge is disassembled into two directed edges to describe the characteristics. Uncertainty systems refer to systems that contain uncertainty information or cannot describe factors in a certain amount, and the problem of adaptive consistency control of a multi-agent system with uncertainty on a directed graph remains a considerable problem because of the challenges presented by uncertainty propagating in an asymmetric manner in a communication graph. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a directional support tree-based unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method, which solves the self-adaptive consistency control challenge of an uncertain multi-agent system on a directed graph caused by the asymmetric propagation of uncertainty in the communication graph by a method for constructing the multi-agent system in a traditional non-leader scene. In order to achieve the technical aim, the invention provides the following technical scheme that the unmanned aerial vehicle cluster flight attitude self-adaptive consistency control method based on the directed support tree comprises the following steps: s1, constructing a roll gesture dynamic model of an unmanned aerial vehicle cluster, abstracting the model into a multi-agent network with parameter uncertainty, and constructing a multi-agent system; s2, defining a multi-agent system to interact on a directed communication graph taking the unmanned aerial vehicle as a node, wherein the directed communication graph comprises a directed support tree; S3, designing a deterministic equivalent controller for the multi-agent system based on the S2 and the coupling gain condition, wherein the deterministic equivalent controller updates a control input based on an estimated parameter, and the estimated parameter is kept limited; S4, the coupling gain in the deterministic equivalent controller is popularized to the time-varying coupling gain to obtain the time-varying coupling deterministic equivalent controller, wherein the time-varying coupling deterministic equivalent controller updates the control input based on the estimated parameter and the time-varying coupling gain, and the estimated parameter and the time-varying coupling gain are kept in a limit; s5, designing time-varying coupling gain based on coupling gain conditions, selecting any positive gain matrix, solving state feedback gain and symmetric positive gain matrix, and completing parameter design of the time-varying coupling deterministic equivalent controller; s6, utilizing a breadth-first search algorithm to find a directed support tree from the directed communication graph from the root node; S7, constructing an undirected version of the directed support tree, so that the nodes can mutually transmit information; S8, updating control input in the time-varying coupling deterministic equivalent controller through the directed communication graph, updating estimation parameters in the time-varying coupling deterministic equivalent controller through an undirected version of t