Distributed adaptive unmanned aerial vehicle bearing formation control method and system, and electronic device

Abstract

The application discloses a kind of distributed adaptive unmanned aerial vehicle azimuth formation control method, system and electronic equipment, it is related to unmanned aerial vehicle flight control technical field, method includes according to task scene, the expected path and expected flight speed of pilot unmanned aerial vehicle in unmanned aerial vehicle formation are determined;Under the communication network topology structure of unmanned aerial vehicle formation and the dynamics model of each unmanned aerial vehicle, the pilot unmanned aerial vehicle in unmanned aerial vehicle formation is controlled to fly according to the expected path and expected flight speed designed, and the adaptive position controller and adaptive attitude controller based on azimuth information are used to drive follower unmanned aerial vehicle to complete the convergence of expected formation.The application can relax the requirement of unmanned aerial vehicle in unmanned aerial vehicle formation on perception and communication hardware and enhance the maneuverability of formation and improve the robustness and energy utilization rate of body control in unmanned aerial vehicle flight.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and in particular to a distributed adaptive UAV azimuth formation control method, system, and electronic equipment based on pure azimuth measurement. Background Technology

[0002] Multi-drone swarm control is gaining widespread attention due to its potential applications in both military and civilian fields. When multiple drones collaborate on a mission, even if some drones malfunction, the others can form a new swarm to complete the task. Therefore, compared to using traditional single drones, multi-drone swarms offer significantly greater mission completion capabilities and feasibility. Multi-rotor drones, as a typical category of drones, are particularly noteworthy for their hovering and agile maneuvering advantages.

[0003] Currently, most formation control methods require the use of relative positions, velocities, or other auxiliary information between UAVs, thus placing high demands on the UAVs' perception and communication capabilities. Furthermore, most displacement-based formation algorithms suffer from the drawback of being unable to change formation. In addition, many UAV formation control algorithms focus only on position control methods, neglecting the uncertainties in UAV parameters during flight and the back-around problem in attitude control, leading to UAV flight instability and low control energy efficiency. Summary of the Invention

[0004] To relax the requirements for UAVs in terms of sensing and communication hardware in UAV formations, enhance the formation's maneuverability, and improve the robustness of UAV control and energy utilization during flight, this invention proposes a distributed adaptive UAV azimuth formation control method, system, and electronic equipment based on pure azimuth measurement.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] In a first aspect, the present invention provides a distributed adaptive unmanned aerial vehicle (UAV) orientation formation control method, comprising:

[0007] Establish a dynamic model of the UAV;

[0008] Establish the communication network topology of the drone formation and identify the leader and follower drones;

[0009] Based on the mission scenario, determine the expected path and expected flight speed of the navigator drone in the drone formation;

[0010] Under the communication network topology of the UAV formation and the dynamic model of each UAV, the leader UAV in the UAV formation is controlled to fly along the designed desired path and desired flight speed, and the follower UAVs are driven to complete the convergence of the desired formation by an adaptive position controller and an adaptive attitude controller based on azimuth information.

[0011] Secondly, this invention provides a distributed adaptive UAV azimuth formation control system, comprising:

[0012] The dynamics model building module is used to build the dynamics model of the UAV;

[0013] The communication topology establishment module is used to establish the communication network topology of the drone formation and to identify the leader drone and the follower drones.

[0014] The Navigator UAV Information Determination Module is used to determine the Navigator UAV's expected path and expected flight speed in the UAV formation based on the mission scenario.

[0015] The formation control module is used to control the navigator drone in the drone formation to fly along the designed desired path and desired flight speed under the communication network topology of the drone formation and the dynamic model of each drone. It also uses an adaptive position controller and an adaptive attitude controller based on azimuth information to drive the follower drones to complete the convergence of the desired formation.

[0016] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to perform the distributed adaptive UAV orientation formation control method according to the first aspect.

[0017] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0018] 1. The position control algorithm proposed in this invention only uses the orientation information of the UAV formation and does not require interaction with other UAVs. Therefore, the information used by the UAV formation can be obtained directly by using a low-cost airborne camera, which relaxes the hardware requirements of the UAVs and is easier to implement in practical applications.

[0019] 2. The orientation vector remains unchanged during translation and scaling motions. Therefore, the adaptive position control method proposed in this invention enables UAV formations to have translation and scaling formation maneuvers, providing more flexible formation maneuvers. Furthermore, it does not require a precise model of the UAVs and is more robust in practical applications.

[0020] 3. This invention proposes a novel adaptive attitude controller. This method solves the singularity problem and back-wrap problem in attitude control, and can achieve global target attitude tracking. At the same time, it also improves the energy utilization rate in UAV body attitude control. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a distributed adaptive UAV azimuth formation control method based on pure azimuth measurement according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the desired formation and communication topology of the formation that meet the conditions of an embodiment of the present invention;

[0024] Figure 3 This is a three-dimensional trajectory curve diagram of eight drones in formation according to an embodiment of the present invention;

[0025] Figure 4 This is a position error curve of 6 follower drones during drone formation in an embodiment of the present invention;

[0026] Figure 5 This is a speed error curve of 6 follower drones during drone formation in an embodiment of the present invention;

[0027] Figure 6 These are the mass estimates and thrust diagrams of the six follower drones in this embodiment of the invention;

[0028] Figure 7 This is an attitude error curve diagram of two follower drones during drone formation in an embodiment of the present invention;

[0029] Figure 8 This is a graph showing the angular velocity error of two follower drones during drone formation in an embodiment of the present invention.

[0030] Figure 9 This is a schematic diagram showing the estimated inertial parameters of two follower drones in an embodiment of the present invention;

[0031] Figure 10 This is a schematic diagram comparing the rotation angle changes of two follower drones under two control methods in an embodiment of the present invention;

[0032] Figure 11This is a schematic diagram comparing the energy consumption of two follower drones under two control methods in an embodiment of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1

[0036] To address the problems of excessively high hardware requirements for UAVs, inflexible formation, over-reliance on precise UAV models, and low energy utilization caused by back-around issues in attitude control in existing formation control methods, this embodiment proposes a distributed adaptive UAV azimuth formation control method based on pure azimuth measurement.

[0037] like Figure 1 As shown in the figure, this embodiment provides a distributed adaptive UAV azimuth formation control method based on pure azimuth measurement, which includes the following steps.

[0038] Step 101: Establish the dynamic model of the UAV.

[0039] In this embodiment, the Euler-Newton formula is used to establish a six-degree-of-freedom UAV dynamics model, and the modified Rodrigues parameters are used to establish a three-degree-of-freedom attitude dynamics model.

[0040] An example: The drone is a quadcopter drone.

[0041] In this embodiment, the dynamic model of the UAV is as follows:

[0042]

[0043] in, These are modified Rodriguez parameters, used to represent the attitude of the UAV. It is an Euler axis, φ i ∈(-2π,2π) are Euler angles.

[0044] and For the i-th UAV in inertial coordinate system F I Given the position and velocity of the object, and the acceleration due to gravity g = 9.8 m / s², the object's position and velocity are given.2 ), From the body coordinate system F bi to inertial coordinate system F I The rotation matrix, It is the transpose of the current attitude of the UAV, I3∈R 3×3 It is the identity matrix, T i Let be the total thrust of the i-th drone. It is the control input command to be designed for the i-th UAV. It is a desired attitude. From the inertial coordinate system F I The rotation matrix to the desired body coordinate system.

[0045] ω i τ i The drone i in the body coordinate system F bi Lower angular velocity and the control torque to be designed; for x × =[0,-x3,x2;x3,0,-x1;-x2,x1,0] is a skew-symmetric matrix of column vectors x; The parameter m is the calculated attitude parameter of the UAV under the modified Rodrigues parameter notation. i J i ∈R 3×3 Let m be the mass and positive definite moment of inertia matrix of the UAV. Considering that the UAV parameters may be unknown in actual flight control, m is... i and J i None of them are known.

[0046] Step 102: Establish the communication network topology of the drone formation and determine the leader drone and follower drones. Preferably, the minimum azimuth stiffness theory is used to establish the communication network topology of the drone formation.

[0047] In this embodiment, step 102 specifically includes:

[0048] Step S1: Model the drone formation.

[0049] The drone formation is modeled using (G,p), where... It is n in the drone formation f One follower drone and n l The positions of the navigator drones are given by an undirected graph G = (V, E) representing the communication network topology among the n drones (hereinafter referred to as the communication topology). The node set V = {1, 2, ..., n} represents the n drones in the drone formation, and the edge set... This represents the communication relationship among drone nodes. When there exists an edge (i,j)∈E, drone i can obtain information from drone j. N is defined as... i ={j|(i,j)∈E} is the set of incoming neighbor nodes of node i. The orientation information used in UAV formation is defined as:

[0050]

[0051] Step S2: Identify the navigator drone and the follower drone.

[0052] Based on the task requirements, the desired formation can be designed, and based on the desired formation (G, p) * It can obtain the desired orientation information of the drone formation. * indicates expectation. In this embodiment, the azimuth Laplacian matrix is ​​used. Describe the communication topology of the UAV formation, when (i,j)∈E Otherwise L ij =0, where It is a projection matrix, in which case L can be decomposed into:

[0053] The communication topology of the UAV formation is determined based on the azimuth Laplace matrix L. The choice of communication topology should satisfy the condition: rank(L) = 3n - 4. This ensures that the UAV formation converges to the desired formation. Furthermore, the selection of the navigator UAV should satisfy the condition: the number of navigator UAVs is n. l ≥2. At this point, the desired position of the follower drone. With expected flight speed The location of the Navigator drone can be determined by p l and velocity v l The only certainty:

[0054] Set the desired formation as follows: Figure 2 The diagram shows a cuboid, with drones 7-8 designated as navigator drones and drones 1-6 as follower drones. The communication topology within the drone formation is also as shown. Figure 2 As shown, the communication topology of the drone formation satisfies the condition rank(L) = 3n - 4, and the number of leader drones also satisfies the condition n. l ≥2.

[0055] In this desired formation, the desired orientation information is:

[0056] The body parameters and initial state of the follower drone are shown in Tables 1 and 2.

[0057] Table 1. Body Parameters of Follower Drone

[0058]

[0059] Table 2 Initial State of Follower Drones

[0060]

[0061]

[0062] Step 103: Based on the mission scenario, determine the expected path and expected flight speed of the navigator drone in the drone formation.

[0063] In this embodiment, based on the mission scenario and the initial state and constant speed of the navigator drone as shown in Table 3, the expected path and expected flight speed of the navigator drone in the drone formation are determined.

[0064] Table 3. Initial State and Constant Speed ​​Settings for the Navigator Drone

[0065] drone number initial position / m Constant velocity (m / s) 7 <![CDATA[[10;10;10] T ]]> <![CDATA[[0.2;0.1;0.2] T ]]> 8 <![CDATA[[0;10;10] T ]]> <![CDATA[[0.2;0.1;0.2] T ]]>

[0066] Step 104: Under the communication network topology of the UAV formation and the dynamic model of each UAV, control the navigator UAV in the UAV formation to fly along the designed desired path and desired flight speed, and use an adaptive position controller and adaptive attitude controller based on azimuth information to drive the follower UAVs to complete the convergence of the desired formation.

[0067] In this embodiment, the adaptive position controller based on orientation information is:

[0068]

[0069] Among them, u i This is the control input command for the drone i, where tanh is the tangent function, and g... ij , These are the azimuth vectors of the current drone formation and the target drone formation, respectively. k s k p k1, k2, and k3 are positive constants. ξ i , χ i It is an auxiliary variable in the auxiliary system. Representing variable ξ i Differentiation operation, Is the mass of the drone m i The estimated value, v i For the i-th UAV in inertial coordinate system F I The speed at which it descends.

[0070] The adaptive update rate is:

[0071]

[0072] in, γ i >0, and m i These are the mass m of the drone. i The upper and lower bounds, the quality estimate In the interval Internal updates are implemented, and the designed controller ensures that the control input thrust T is guaranteed. i =||u i The boundedness of ||.

[0073] An example, auxiliary variable ξ i , χ i The initial value of k is randomly selected within the interval (0,1), and k s =1, k p =1, k1=0.1, k2=4, k3=4. For i=1,2,…,6, set γ i =1, initial value of quality estimate

[0074] Depend on Figure 3 It can be seen that drone formations can form the desired drone formation using only orientation information. Figure 4 , Figure 5 These are the position and speed errors of the follower drones in a drone formation, respectively. Figure 4 and Figure 5 It can be seen that the error eventually tends to 0 under the designed adaptive position controller. Figure 6 Part (a) is the quality estimate. Figure 6 Part (b) is the thrust T i It can be seen that the estimated quality value will eventually tend to the true value. Therefore, the adaptive position controller based on orientation information proposed in this embodiment can still be effective when the quality parameters are unknown.

[0075] In this embodiment, the desired attitude of the UAV is extracted based on the UAV's control force. When the UAV swarm performs formation control, in order to respond to the position control function, the UAVs need to track the desired attitude determined by the control force to ensure the effectiveness of each UAV in the formation during formation flight.

[0076] First, a hierarchical control framework is used, based on the position control force of the UAV. Obtain the desired attitude of the drone and expected angular velocity

[0077]

[0078] in, γ i,1 =(T i / m i ), After obtaining the desired attitude and desired angular velocity, the attitude error of the UAV is defined as follows: Angular velocity error is

[0079] An example: Desired pose Desired angular velocity

[0080] Secondly, based on the desired attitude of the drone and expected angular velocity Design an adaptive attitude controller for a drone.

[0081] Define the inertial matrix of the UAV as follows

[0082] To facilitate the design of adaptive attitude controllers for UAVs, a linear operator Υ(x) is introduced to represent the UAV's attitude model for any vector.

[0083]

[0084] The attitude model of the UAV can now be represented as follows:

[0085]

[0086] Where θ i =[J i,11 J i,22 J i,33 J i,23 J i,13 J i,12 ] T It is a column vector composed of unknown elements in the inertia matrix.

[0087] Considering the uncertainty of the inertial parameters of a UAV, an adaptive attitude controller for the UAV is designed as follows:

[0088]

[0089] Where, τ i (xi ) is the control torque designed. For attitude error, For angular velocity error, ζ is the oblique-symmetric matrix variable of angular velocity given in the UAV model description. i These are introduced auxiliary variables. Called σ i The contrast of the images Is when x i ∈D i The state of the closed-loop system, G(x) i ) indicates that in x i ∈D i The mapping function for system state.

[0090] For θ i The estimated value, k ω k σ l1 and l2 are positive constants. It is the attitude error rotation matrix, Υ(Λ) i -ζ i ), Υ(ω i The linear operator Υ(x) introduced in this step simplifies the expression of the control torque, R. T For rotation matrix, It is the attitude error of the UAV The image is in contrast.

[0091] In addition, C i and D i These are the set of streams and the set of skips, defined as follows:

[0092]

[0093]

[0094] Where δ i >0 is called the hysteresis constant. The attitude singularity problem in attitude control is solved by setting the flow set and the jump set.

[0095] An example: auxiliary variable ζ i The initial value is randomly selected within the interval (0,1), and the initial value of the inertial parameter is... k ω =0.2, k σ =0.2, l1=1, l2=2 are positive constants.

[0096] Inertial parameter estimates The adaptive update rate is:

[0097]

[0098] Where μ i >0.

[0099] This adaptive module makes the attitude control method independent of the precise model of the UAV, thus making the UAV more robust in flight.

[0100] An example, μ i =1.

[0101] The proposed adaptive attitude controller is used to simulate the attitude control of a follower UAV. Without loss of generality, the effectiveness of the designed adaptive attitude controller is demonstrated by the attitude control results of follower UAV 1 and follower UAV 2.

[0102] Figure 7 Part (a) is the attitude error of the follower drone 1. Figure 7 Part (b) is the attitude error of the follower drone 2. Figure 8 Part (a) is the angular velocity error of the follower drone 1. Figure 8 Part (b) is the angular velocity error of the follower drone 2. Both figures show that the error of the follower drone can quickly converge to 0 when the drones are in formation.

[0103] Figure 9 The left and right vertical axes represent the estimated values ​​of the inertial parameters, respectively. It is evident that the estimated values ​​of the inertial parameters will eventually converge to the true values. Therefore, the adaptive attitude controller proposed in this embodiment is suitable for situations where the inertial parameters are unknown.

[0104] The adaptive attitude controller (HAC) proposed in this embodiment can effectively solve the singularity problem and the unwinding problem in attitude control compared with the traditional attitude controller (TAC). Figure 10 Parts (a) and (b) in the text represent two control methods, respectively. The change in rotation angle, Figure 11 Parts (a) and (b) in the figure represent the energy used by follower UAV 1 and follower UAV 2 under the two attitude control methods, respectively. Figure 10 As can be seen, the adaptive attitude controller proposed in this embodiment solves the attitude singularity problem and the unwinding problem in attitude control. The rotation angle in attitude control is smaller, and therefore the energy consumed by attitude control is also less. Figure 11 The simulation results also verify this point.

[0105] When drones fly in formation, the leader drone is guaranteed to fly along the designed desired path and at the desired flight speed. Based on the position controller and attitude controller mentioned above, the follower drones can quickly achieve convergence of the desired formation using only the orientation information.

[0106] Example 2

[0107] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a distributed adaptive UAV azimuth formation control system based on pure azimuth measurement is provided below.

[0108] This distributed adaptive UAV orientation formation control system includes:

[0109] The dynamics model building module is used to build dynamics models of UAVs.

[0110] The communication topology establishment module is used to establish the communication network topology of the drone formation and to identify the leader drone and the follower drones.

[0111] The Navigator UAV Information Determination Module is used to determine the Navigator UAV's expected path and expected flight speed in the UAV formation based on the mission scenario.

[0112] The formation control module is used to control the navigator drone in the drone formation to fly along the designed desired path and desired flight speed under the communication network topology of the drone formation and the dynamic model of each drone. It also uses an adaptive position controller and an adaptive attitude controller based on azimuth information to drive the follower drones to complete the convergence of the desired formation.

[0113] Example 3

[0114] This invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute a distributed adaptive UAV azimuth formation control method based on pure azimuth measurement, as described in Embodiment 1.

[0115] Alternatively, the aforementioned electronic device may be a server.

[0116] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a distributed adaptive UAV azimuth formation control method based on pure azimuth measurement according to Embodiment 1.

[0117] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0118] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A distributed adaptive UAV azimuth formation control method, characterized in that, include: Establish a dynamic model of the UAV; Establish the communication network topology of the drone formation and identify the leader and follower drones; Based on the mission scenario, determine the expected path and expected flight speed of the navigator drone in the drone formation; Under the communication network topology of the UAV formation and the dynamic model of each UAV, the leader UAV in the formation is controlled to fly along the designed desired path and desired flight speed, and the follower UAVs are driven to converge to the desired formation using an adaptive position controller and an adaptive attitude controller based on azimuth information; the adaptive position controller based on azimuth information is as follows: ； in, It is a drone The control input command, where tanh is the tangent function. , These are the azimuth vectors of the current drone formation and the target drone formation, respectively. , It is a positive number. , , It is an auxiliary variable in the auxiliary system. Is it the quality of the drone? The estimated value, For the first The velocity of a drone in an inertial coordinate system.

2. The distributed adaptive UAV azimuth formation control method according to claim 1, characterized in that, Establishing a dynamic model of the UAV, specifically including: A six-degree-of-freedom UAV dynamic model is established using the Euler-Newton formula, and a three-degree-of-freedom attitude dynamic model is established using modified Rodrigues parameters.

3. The distributed adaptive UAV azimuth formation control method according to claim 1, characterized in that, Establishing the communication network topology for drone formations includes: The communication network topology of the UAV formation is established using the minimum azimuth stiffness theory.

4. The distributed adaptive UAV azimuth formation control method according to claim 1, characterized in that, Based on the mission scenario, determine the expected path and expected flight speed of the navigator drone within the drone formation, specifically including: Based on the mission scenario, as well as the initial state and constant speed of the navigator drone, determine the expected path and expected flight speed of the navigator drone in the drone formation.

5. The distributed adaptive UAV azimuth formation control method according to claim 1, characterized in that, The adaptive attitude controller is: ； in, It is the control torque of the design, the variable For drones The attitude error, For drones angular velocity error, For drones angular velocity, It is the skew-symmetric matrix variable of angular velocity given in the UAV model description. These are introduced auxiliary variables. It is the attitude of the drone. The contrast of the images Inertial parameters The estimated value, , , , For positive integers, For linear operators; Indicates in The mapping function of system state; Is when The state of the closed-loop system; , It is the attitude error rotation matrix. It is a rotation matrix. This is the desired attitude of the drone. It is the expected angular velocity. Representing variables Differentiation operation, It is the attitude error of the UAV The image is in contrast.

6. A distributed adaptive unmanned aerial vehicle (UAV) orientation formation control system, characterized in that, include: The dynamics model building module is used to build the dynamics model of the UAV; The communication topology establishment module is used to establish the communication network topology of the drone formation and to identify the leader drone and the follower drones. The Navigator UAV Information Determination Module is used to determine the Navigator UAV's expected path and expected flight speed in the UAV formation based on the mission scenario. The formation control module is used to control the leader drone in the drone formation to fly along a pre-designed desired path and speed, under the communication network topology of the drone formation and the dynamic model of each drone. It also uses an adaptive position controller and an adaptive attitude controller based on azimuth information to drive the follower drones to converge to the desired formation. The adaptive position controller based on azimuth information is as follows: ； in, It is a drone The control input command, where tanh is the tangent function. , These are the azimuth vectors of the current drone formation and the target drone formation, respectively. , It is a positive number. , , It is an auxiliary variable in the auxiliary system. Is it the quality of the drone? The estimated value, For the first The velocity of a drone in an inertial coordinate system.

7. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program and the processor runs the computer program to enable the electronic device to perform a distributed adaptive UAV orientation formation control method according to any one of claims 1 to 5.

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