An unmanned aerial vehicle cluster system distributed time-varying formation tracking control method and system

Abstract

The present application relates to a kind of unmanned aerial vehicle cluster system distributed time-varying formation tracking control method and system, it is related to multi-robot cooperative control technical field.The method includes: establishing the dynamics model of unmanned aerial vehicle, using inner and outer ring control architecture, for unmanned aerial vehicle cluster establishment unmanned aerial vehicle cluster model for formation control;Unmanned aerial vehicle cluster includes leader unmanned aerial vehicle with unknown input and multiple follower unmanned aerial vehicles;According to the desired formation configuration of unmanned aerial vehicle cluster, construct distributed time-varying formation tracking controller;According to distributed time-varying formation tracking controller, distributed time-varying formation tracking control is carried out.The method of the present application constructs distributed time-varying formation tracking controller, so that multi-unmanned aerial vehicle system can track leader unmanned aerial vehicle with unknown external input while forming the desired time-varying formation configuration, realize the distributed formation tracking movement of multi-unmanned aerial vehicle cluster system, with distributed, strong scalability, high computing efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-robot cooperative control, in particular to a UAV cluster system distributed time-varying formation tracking control method and system. BACKGROUND

[0002] Formation control of cluster systems has been widely concerned in many fields. Formation control is used to make the state or output of a group of agents form a given formation. The advantage of cluster systems is that it can create favorable conditions and provide technical support for cooperative reconnaissance, detection and hunting tasks. Formation control has been a research hotspot in recent decades, and various classic formation control methods have been studied and applied, such as leader-follower based, behavior based and virtual structure based methods. Although these three typical control strategies have been applied to formation control problems for many years, the leader-follower based control method will lead to the collapse of the formation once the leader fails; the behavior based formation method relies on qualitative behavior rules, and it is difficult to establish a quantitative model of the entire system, and it cannot guarantee the stability of the entire system formation movement; the virtual structure based method needs a central node for centralized control and cannot be implemented in a distributed form. In the past few decades, consensus theory has attracted the attention of researchers. Since local interaction is also needed in formation control, control researchers have begun to consider formation control based on consensus problems, and the consensus based formation control method can overcome the shortcomings of the above three formation control methods to some extent. In addition, in practical applications, the formation shape often needs to be adjusted in real time according to the demand, that is, the formation shape is time-varying, and time-varying formation control needs to be applied.

[0003] In many practical applications, UAV formation control is crucial, and the controlled UAV formation system can complete many complex tasks that UAVs cannot complete, such as target hunting and area detection. A mature UAV system can greatly save working time and improve efficiency. However, how to apply control methods from theory to practical application is a challenging problem and is also a way to realize group intelligence in the control field. At present, the existing consensus based formation control method mainly aims at multi-UAV systems requiring a leader without external input or known external input, and does not have a physical experiment, but is mainly a digital simulation experiment. It is a technical problem to be solved to enable the multi-UAV system to track the leader UAV with unknown external input and carry out physical experiments to verify the tracking effect. SUMMARY

[0004] The purpose of the present application is to provide a UAV cluster system distributed time-varying formation tracking control method and system, so that the multi-UAV system can track the leader UAV with unknown external input while forming the desired time-varying formation configuration.

[0005] To achieve the above object, the present application provides the following scheme:

[0006] A UAV cluster system distributed time-varying formation tracking control method, comprising:

[0007] establishing a dynamic model of a UAV;

[0008] According to the dynamic model of the UAV, a UAV cluster model for formation control is established for a UAV cluster by using an inner-outer loop control architecture; the UAV cluster includes a leader UAV with unknown input and multiple follower UAVs;

[0009] obtaining a desired formation configuration of the UAV cluster;

[0010] According to the desired formation configuration, a distributed time-varying formation tracking controller is constructed for the UAV cluster;

[0011] According to the distributed time-varying formation tracking controller, distributed time-varying formation tracking control is performed on the UAV cluster.

[0012] Optionally, the dynamic model of the UAV is established, specifically comprising:

[0013] establishing a dynamic model of a UAV wherein x, y, z represent the position of the UAV in space; φ, θ, ψ represent the roll angle, pitch angle, and yaw angle; m represents the mass of the UAV; I xx , I yy , and I zz represent the moments of inertia about the x, y, and z axes, respectively; L represents the distance between the motor shaft and the center of the fuselage; g represents the acceleration of gravity; U1, U2, U3, and U4 represent the control input quantities of the system determined by the angular velocities of the four rotors.

[0014] Optionally, according to the dynamic model of the UAV, a UAV cluster model for formation control is established for a UAV cluster by using an inner-outer loop control architecture, specifically comprising:

[0015] According to the dynamic model of the UAV, an inner-outer loop control architecture is used to model the i-th UAV in the UAV cluster F A ={1, 2, …, N} as The outer loop in the inner-outer loop control architecture is a position control loop, and the inner loop is an attitude control loop; wherein x i represents the position of the i-th UAV, i∈F A ; u i represents the control input vector of the i-th UAV;

[0016] After modeling all the unmanned aerial vehicles in the unmanned aerial vehicle cluster, the unmanned aerial vehicle cluster model for formation control is obtained.

[0017] Optionally, the distributed time-varying formation tracking controller is constructed for the unmanned aerial vehicle cluster according to the desired formation configuration, and specifically includes:

[0018] The leader unmanned aerial vehicle in the unmanned aerial vehicle cluster is added with a control constraint Wherein, x1 represents the state of the leader unmanned aerial vehicle; and u1 represents the control input vector of the leader unmanned aerial vehicle.

[0019] The action topological relationship between the multiple unmanned aerial vehicles in the unmanned aerial vehicle cluster is described by using algebraic graph theory, and a directed graph corresponding to the action topological relationship of the unmanned aerial vehicle cluster is obtained.

[0020] A time-varying vector is used to depict the desired formation configuration; wherein h i is the desired formation vector of the i-th unmanned aerial vehicle in the unmanned aerial vehicle cluster.

[0021] The distributed time-varying formation tracking controller is constructed for the unmanned aerial vehicle cluster according to the desired formation vector h i of the i-th unmanned aerial vehicle in the unmanned aerial vehicle cluster. Wherein e i (t) is the formation tracking error of the i-th unmanned aerial vehicle in the unmanned aerial vehicle cluster at time t; w i1 is the action strength of nodes 1 to i in the directed graph; x i (t) and x j (t) respectively represent the positions of the i-th and j-th unmanned aerial vehicles at time t; h i (t) and h j (t) respectively represent the desired formation vectors of the i-th and j-th unmanned aerial vehicles at time t; x1(t) represents the state of the leader unmanned aerial vehicle at time t; N is the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster; u i (t) represents the control input vector of the i-th unmanned aerial vehicle at time t; K represents a gain matrix; η and ξ are both normal numbers; v i (t) represents the time-varying formation tracking compensation input of the i-th unmanned aerial vehicle at time t.

[0022] Optionally, the distributed time-varying formation tracking control is performed on the unmanned aerial vehicle cluster according to the distributed time-varying formation tracking controller, and specifically includes:

[0023] The parameters of the distributed time-varying formation tracking controller are determined, so that and η≥γ; wherein γ is the upper limit of the control input of the leader unmanned aerial vehicle.

[0024] After the parameters are determined, the distributed time-varying formation tracking controller is used to perform distributed time-varying formation tracking control on the UAV cluster.

[0025] A distributed time-varying formation tracking control system of a UAV cluster system comprises:

[0026] A UAV dynamics model establishing module is configured to establish a dynamics model of a UAV;

[0027] A UAV cluster model establishing module is configured to, according to the dynamics model of the UAV, use an inner-outer loop control architecture to establish a UAV cluster model for formation control for a UAV cluster; the UAV cluster comprises a leader UAV with unknown input and multiple follower UAVs;

[0028] An expected formation configuration obtaining module is configured to obtain an expected formation configuration of the UAV cluster;

[0029] A distributed time-varying formation tracking controller constructing module is configured to, according to the expected formation configuration, construct a distributed time-varying formation tracking controller for the UAV cluster;

[0030] A distributed time-varying formation tracking control module is configured to perform distributed time-varying formation tracking control on the UAV cluster according to the distributed time-varying formation tracking controller.

[0031] Optionally, the UAV dynamics model establishing module specifically comprises:

[0032] A UAV dynamics model establishing unit is configured to establish a dynamics model of a UAV wherein x, y, and z represent the position of the UAV in space; φ, θ, and ψ represent the roll angle, the pitch angle, and the yaw angle; m represents the mass of the UAV; I xx ,I yy ,I zz represent the moment of inertia about the x, y, and z axes, respectively; L represents the distance between the motor shaft and the center of the fuselage; g represents the gravitational acceleration; and U1, U2, U3, and U4 represent the control input of the system determined by the angular velocity of the four rotors.

[0033] Optionally, the UAV cluster model establishing module specifically comprises:

[0034] A UAV cluster model establishing unit is configured to, according to the dynamics model of the UAV, use an inner-outer loop control architecture to model the i-th UAV in the UAV cluster F A ={1, 2, …, N} as The outer loop in the inner-outer loop control architecture is a position control loop, and the inner loop is an attitude control loop; wherein x i represents the position of the i-th UAV, and i ∈ FA i represents the control input vector of the ith unmanned aerial vehicle; after modeling all unmanned aerial vehicles in the unmanned aerial vehicle cluster, the unmanned aerial vehicle cluster model for formation control is obtained.

[0035] Optionally, the distributed time-varying formation tracking controller construction module specifically comprises:

[0036] a leader control constraint unit configured to add control constraints to a leader unmanned aerial vehicle in the unmanned aerial vehicle cluster wherein x1 represents the state of the leader unmanned aerial vehicle; and u1 represents the control input vector of the leader unmanned aerial vehicle.

[0037] a directed graph generation unit configured to describe the action topological relationship between multiple unmanned aerial vehicles in the unmanned aerial vehicle cluster by using algebraic graph theory, to obtain a directed graph corresponding to the action topological relationship of the unmanned aerial vehicle cluster;

[0038] a desired formation configuration characterization unit configured to characterize the desired formation configuration by using a time-varying vector wherein h i is the desired formation vector of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster.

[0039] a distributed time-varying formation tracking controller construction unit configured to construct a distributed time-varying formation tracking controller for the unmanned aerial vehicle cluster according to the desired formation vector h i of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster. wherein e i (t) is the formation tracking error of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster at time t; w i1 is the action strength of node 1 to node i in the directed graph; x i (t) and x j (t) represent the positions of the ith and jth unmanned aerial vehicles at time t, respectively; h i (t) and h j (t) represent the desired formation vectors of the ith and jth unmanned aerial vehicles at time t, respectively; x1(t) represents the state of the leader unmanned aerial vehicle at time t; N is the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster; u i (t) represents the control input vector of the ith unmanned aerial vehicle at time t; K represents a gain matrix; η and ξ are both normal numbers; v i (t) represents the time-varying formation tracking compensation input of the ith unmanned aerial vehicle at time t.

[0040] Optionally, the distributed time-varying formation tracking control module specifically comprises:

[0041] a controller parameter determination unit configured to determine the parameters of the distributed time-varying formation tracking controller, so that​ and η ≥ γ; where γ is an upper bound of leader UAV control input;

[0042] A distributed time-varying formation tracking control unit is configured to perform distributed time-varying formation tracking control on the UAV cluster using the distributed time-varying formation tracking controller after the parameters are determined.

[0043] According to the embodiments of the present application, the following technical effects are provided:

[0044] The present application provides a kind of UAV cluster system distributed time-varying formation tracking control method and system, the method includes: the dynamics model of UAV is established;According to the dynamics model of the UAV, using inner-outer loop control architecture, the UAV cluster model for formation control is established for UAV cluster;The leader UAV with unknown input and multiple follower UAVs are included in the UAV cluster;The desired formation configuration of the UAV cluster is obtained;According to the desired formation configuration, a distributed time-varying formation tracking controller is constructed for the UAV cluster;The UAV cluster is controlled by the distributed time-varying formation tracking controller.The method and system of the present application design distributed time-varying formation tracking controller, so that multiple UAV systems can track the leader UAV with unknown external input while forming the desired time-varying formation configuration, realize the distributed formation tracking movement of multi-UAV cluster system, with distributed, strong scalability, high computing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0046] Figure 1 A flow chart of the UAV cluster system distributed time-varying formation tracking control method of the present application;

[0047] Figure 2 A principle diagram of the UAV cluster system distributed time-varying formation tracking control method of the present application;

[0048] Figure 3 A directed graph corresponding to the UAV cluster system action topology provided by the embodiments of the present application;

[0049] Figure 4 A simulation motion trajectory diagram of the UAV cluster system provided by the embodiments of the present application;

[0050] Figure 5 A UAV system formation tracking simulation error curve schematic diagram provided for an embodiment of the present application;

[0051] Figure 6 A UAV cluster system physical flight motion trajectory schematic diagram provided for an embodiment of the present application;

[0052] Figure 7 A UAV system formation tracking physical flight error curve schematic diagram provided for an embodiment of the present application;

[0053] Figure 8 A Tello-UWB physical experiment verification platform schematic diagram provided for an embodiment of the present application;

[0054] Figure 9 A physical experiment verification platform communication mode schematic diagram provided for an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0056] The purpose of the present application is to provide a UAV cluster system distributed time-varying formation tracking control method and system, so that the multi-UAV system can track the leader UAV motion with unknown external input while forming the desired time-varying formation configuration.

[0057] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be described in detail below with reference to the drawings and specific embodiments.

[0058] The present application aims at the dynamic response problem of multi-UAV system with unknown external input. Considering that the leader UAV and the follower UAV are uncooperative, the input information of the leader UAV is unknown to its followers, a distributed time-varying formation tracking control method is proposed, which has the characteristics of distributed, strong scalability, high computational efficiency, etc. In addition, a cooperative experiment platform based on Tello-UWB positioning system is designed, and flight experiments of Tello UAV formation tracking control are given.

[0059] Figure 1 A flowchart of a UAV cluster system distributed time-varying formation tracking control method of the present application, Figure 2 A principle schematic diagram of a UAV cluster system distributed time-varying formation tracking control method of the present application.Figure 1 and Figure 2 The present application is a kind of unmanned aerial vehicle cluster system distributed time-varying formation tracking control method, comprising:

[0060] Step 101: establishing the dynamics model of the unmanned aerial vehicle.

[0061] The unmanned aerial vehicle studied in the present application is mainly a quadrotor aircraft, and its dynamics model is as follows:

[0062]

[0063] Wherein, x, y, z represent the position of the unmanned aerial vehicle in space; φ, θ, ψ represent the roll angle, pitch angle and yaw angle; m represents the mass of the unmanned aerial vehicle; I xx ,I yy ,I zz The moment of inertia of the unmanned aerial vehicle about the x, y, z axes respectively; L represents the distance between the motor shaft and the center of the fuselage; g represents the gravitational acceleration, U1, U2, U3, U4 represent the control input of the system determined by the angular velocity of the four rotors.

[0064] Wherein, the definition of control input U1, U2, U3, U4 is as follows:

[0065]

[0066] Wherein, b represents the lift coefficient; d represents the torque coefficient; ω1, ω2, ω3, ω4 represent the rotational speed of rotors 1, 2, 3, 4 respectively. U1 represents the total lift perpendicular to the fuselage direction, U2 represents the lift difference affecting the pitch motion of the unmanned aerial vehicle, U3 represents the lift difference affecting the roll motion of the unmanned aerial vehicle, and U4 represents the torque affecting the yaw motion of the unmanned aerial vehicle.

[0067] Step 102: according to the dynamics model of the unmanned aerial vehicle, adopting the inner-outer loop control architecture, establishing the unmanned aerial vehicle cluster model for formation control for the unmanned aerial vehicle cluster.

[0068] The unmanned aerial vehicle cluster studied in the present application includes a leader unmanned aerial vehicle with unknown input and multiple follower unmanned aerial vehicles. In the problem of time-varying formation tracking of multiple unmanned aerial vehicles, the relative position relationship of each unmanned aerial vehicle is focused on, therefore the control architecture of inner-outer loop is adopted. For the unmanned aerial vehicle, the outer loop is the position control loop, and the inner loop is the attitude control loop. Therefore, according to the quadrotor unmanned aerial vehicle model (1) established in step 101, the control architecture of inner-outer loop is adopted, and the unmanned aerial vehicle cluster model for formation control is established for the unmanned aerial vehicle cluster.

[0069] Specifically, considering the unmanned aerial vehicle cluster system composed of N unmanned aerial vehicles (N≥1), the unmanned aerial vehicle cluster is denoted as F A= {1, 2, …, N}. At the formation control level, the ith UAV (i e F A ) is modeled as follows:

[0070]

[0071] where x and u are the position and control input vector of the ith UAV, respectively.

[0072] After modeling all the UAVs in the UAV swarm, the UAV swarm model for formation control is obtained.

[0073] Step 103: Obtain the desired formation configuration of the UAV swarm.

[0074] The desired formation configuration of the UAV swarm is obtained, and the time-varying vector h is used to characterize the desired formation configuration, where h i is the desired formation vector of the ith UAV in the UAV swarm.

[0075] Step 104: According to the desired formation configuration, a distributed time-varying formation tracking controller is constructed for the UAV swarm.

[0076] According to the UAV swarm model for formation control obtained in step 102, a time-varying formation tracking control problem is defined. In the ground inertial system O-XYZ, only the movement of the UAVs in the horizontal plane (XY plane) is considered, there is no movement in the Z-axis direction, and the height of each UAV can be controlled separately, therefore, only the formation tracking movement of the UAV swarm in the XY plane is considered.

[0077] The formation tracking control requires that the UAV swarm forms the desired formation configuration while the entire formation can track the trajectory movement of the leader UAV. Therefore, the following control is added to the leader UAV to constrain it:

[0078]

[0079] where x and u are the state and control input vector of the leader, respectively, and ||u1|| £ y, y is a normal number.

[0080] The action topological relationship between multiple UAVs in the UAV swarm is described using algebraic graph theory, and G is defined as the directed graph corresponding to the action topology of the swarm system, the UAVs are represented as nodes in the graph G, and w ij is the action strength from node j to node i. It is required that the directed graph G has a spanning tree and takes the leader as the root node. The leader has no neighbors, and the UAVs as followers have at least one neighbor. The Laplacian matrix corresponding to the graph G is denoted as L According to the above conditions, L can be divided into wherein

[0081] The time-varying vector is utilized The desired formation configuration obtained in step 103 is described. For any bounded initial state of each UAV, if there is a small positive number ε such that the following formula (5) is established:

[0082]

[0083] If the desired output time-varying formation tracking is achieved for the UAV cluster, and is defined as the formation tracking error.

[0084] According to the desired formation configuration, for the UAV cluster, under the leader formation action with unknown input, a distributed time-varying formation tracking controller is constructed as follows:

[0085]

[0086] wherein, wherein e i (t) is the formation tracking error of the i-th UAV in the UAV cluster at time t; w i1 is the action strength of node 1 to node i in the directed graph; x i (t) and x j (t) represent the positions of the i-th and j-th UAVs at time t, respectively; h i (t) and h j (t) represent the desired formation vectors of the i-th and j-th UAVs at time t, respectively; x1(t) represents the state of the leader UAV at time t; N is the number of UAVs in the UAV cluster; u i (t) represents the control input vector of the i-th UAV at time t; η is a positive number; K represents the gain matrix to be designed; ξ represents a small positive number; v i (t) represents the time-varying formation tracking compensation input of the i-th UAV at time t.

[0087] Step 105: According to the distributed time-varying formation tracking controller, distributed time-varying formation tracking control is performed on the UAV cluster.

[0088] According to the distributed time-varying formation tracking controller established in step 104, the parameters of the formation tracking controller are designed. For the i-th UAV (i = 2, …, N), first, a suitable time-varying formation tracking compensation input is designed to make:

[0089]

[0090] If there is a formation compensation input vi If the above formula is established, the given time-varying formation is feasible; otherwise, the desired formation is infeasible, and the formation vector h needs to be redefined i .

[0091] Then, design η ≥ γ, where γ is the upper bound of the leader UAV control input.

[0092] After the parameters of the distributed time-varying formation tracking controller are designed, so that After η ≥ γ is designed, the distributed time-varying formation tracking controller is used to perform distributed time-varying formation tracking control on the UAV cluster.

[0093] In addition, the method further includes: establishing a Tello-UWB physical experiment verification platform to experimentally verify the distributed time-varying formation tracking control performed by the distributed time-varying formation tracking controller on the UAV cluster.

[0094] Specifically, the Tello-UWB physical experiment verification platform is built to experimentally verify the distributed time-varying formation tracking controller proposed in the application. The physical experiment verification platform is composed of 5 Tello UAVs, a UWB positioning system and a ground station. Each Tello UAV is bound with a UWB positioning tag, and the ground station communicates with the UAVs at a frequency of 50Hz.

[0095] The application proposes a distributed time-varying formation tracking control method for a UAV cluster system based on consistency, so that the multi-UAV system can track the motion of a leader UAV with unknown external input while forming a desired time-varying formation configuration. The main advantages of the method are as follows: 1) The method can realize time-varying formation tracking of a heterogeneous cluster system composed of multiple UAVs, and the control input of the leader UAV is unknown to the followers. 2) The method can realize time-varying formation configuration, better cope with rapid changes in external environment and system tasks, and has strong flexibility and applicability. 3) The method only uses the relative action information of neighbor nodes to design a distributed formation controller, has a simple structure, good scalability and self-organization, and can effectively improve the computing efficiency.

[0096] The effectiveness of the method proposed in the application is verified by an embodiment of a UAV cluster system formation control. The specific implementation steps of this embodiment are as follows:

[0097] (1) UAV cluster system setting

[0098] Consider a UAV cluster system composed of 1 leader UAV (numbered 1 in the figure) and 4 follower UAVs (numbered 2, 3, 4 and 5 in the figure), and its action topology is as follows Figure 3The UAVs perform a cooperative reconnaissance mission in the form of a rotating circular formation. The five UAVs fly at a constant altitude, so in this embodiment only the formation tracking control problem in the XY plane needs to be considered. The model parameters of the leader are set to x1(0) = [0.03, 0.03, 0] T The leader UAV will move in a straight line at a constant speed in the XY plane.

[0099] (2) Desired time-varying formation design

[0100] To depict the desired rotating circular formation configuration, for each UAV, the time-varying formation vector is set as follows:

[0101]

[0102]

[0103]

[0104]

[0105] If the UAV swarm system achieves the desired formation tracking, each UAV will rotate around the leader in the XY plane at a radius of r and an angular velocity of .

[0106] (3) Formation tracking controller parameter design

[0107] For all UAVs, it can be verified that the formation tracking feasibility conditions are established, and the formation compensation input v i is obtained as follows:

[0108]

[0109]

[0110]

[0111]

[0112] The gain matrix K = 1 is designed, and the normal numbers η = 0.2 and ζ = 0.01 are selected.

[0113] (4) Simulation condition setting and result analysis

[0114] In this embodiment, let r = 2 m, The initial positions of the UAVs in the XY plane are generated by random numbers between -3 and 3. The motion trajectory of the UAV swarm system is as shown in FIG. 6. Figure 4where t denotes the simulation time, the horizontal axis x(t) denotes the position along the X-axis direction, the vertical axis y(t) denotes the position along the Y-axis direction, and the legend "X" denotes the leader, and the other four legends denote the follower unmanned aerial vehicles 2, 3, 4, and 5 respectively. Let denote the time-varying formation tracking error of the unmanned aerial vehicle i, and its Euclidean norm is as shown in Figure 5 , Figure 5 the horizontal axis denotes the simulation time, and the vertical axis denotes the formation tracking error. From Figure 4 and Figure 5 it can be seen that the unmanned aerial vehicle cluster system realizes the expected rotating circular formation tracking, and this embodiment verifies the effectiveness of the method proposed in the application.

[0115] (5) Physical flight condition setting and result analysis

[0116] In this embodiment, let r = 2m, the motion trajectory of the unmanned aerial vehicle cluster system is as shown in Figure 6 , where the legend "*" denotes the leader (leader1), and the other four legends denote the follower unmanned aerial vehicles 2, 3, 4, and 5 (follower1, 2, 3, 4) respectively. Let denote the time-varying formation tracking error of the unmanned aerial vehicle i, and its Euclidean norm is as shown in Figure 7 . The experimental hardware platform and communication diagram are as shown in Figure 8 , 9 the horizontal axis denotes the simulation time, and the vertical axis denotes the formation tracking error. From Figure 6 and Figure 7 it can be seen that the unmanned aerial vehicle cluster system realizes the expected rotating circular formation tracking, and this embodiment verifies the effectiveness of the method proposed in the application.

[0117] Based on the method provided in the application, the application further provides a distributed time-varying formation tracking control system of an unmanned aerial vehicle cluster, and the system comprises:

[0118] an unmanned aerial vehicle dynamics model establishing module, configured to establish a dynamics model of an unmanned aerial vehicle;

[0119] an unmanned aerial vehicle cluster model establishing module, configured to, according to the dynamics model of the unmanned aerial vehicle, adopt an inner-outer loop control architecture, and establish an unmanned aerial vehicle cluster model for formation control for the unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster comprises a leader unmanned aerial vehicle with unknown input and multiple follower unmanned aerial vehicles;

[0120] an expected formation configuration obtaining module, configured to obtain an expected formation configuration of the unmanned aerial vehicle cluster;

[0121] a distributed time-varying formation tracking controller constructing module, configured to, according to the expected formation configuration, construct a distributed time-varying formation tracking controller for the unmanned aerial vehicle cluster;

[0122] A distributed time-varying formation tracking control module is used to perform distributed time-varying formation tracking control on the UAV cluster according to the distributed time-varying formation tracking controller.

[0123] Specifically, the UAV dynamics model establishment module includes:

[0124] The UAV dynamics model building unit is used to build the dynamics model of the UAV. Where x, y, z represent the UAV's position in space; φ, θ, ψ represent the roll angle, pitch angle, and yaw angle; m represents the UAV's mass; I xx ,I yy ,I zz Let U1, U2, U3, and U4 represent the moments of inertia about the x, y, and z axes, respectively; L represents the distance between the motor shaft and the center of the machine body; g represents the acceleration due to gravity; and U1, U2, U3, and U4 represent the control inputs of the system determined by the angular velocities of the four rotors.

[0125] The drone swarm model establishment module specifically includes:

[0126] The UAV swarm model building unit is used to establish the UAV swarm F based on the UAV's dynamic model and using an inner and outer loop control architecture. A The i-th UAV in {1,2,…,N} is modeled as follows: In the described inner and outer loop control architecture, the outer loop is the position control loop, and the inner loop is the attitude control loop; where x i Let F represent the position of the i-th drone, i∈F A ;u i Let represent the control input vector of the i-th UAV; after modeling all UAVs in the UAV cluster, the UAV cluster model used for formation control is obtained.

[0127] The distributed time-varying formation tracking controller construction module specifically includes:

[0128] The leader control constraint unit is used to add control constraints to the leader drone in the drone swarm. Where, x l Indicates the status of the leader's drone; u l This represents the control input vector of the leader drone;

[0129] A directed graph generation unit is used to describe the interaction topology between multiple drones in the drone swarm using algebraic graph theory, and to obtain a directed graph corresponding to the interaction topology of the drone swarm.

[0130] Desired formation configuration characterization unit, used to utilize time-varying vectors Describe the desired formation configuration; where hi is a desired formation vector of the i-th unmanned vehicle in the unmanned vehicle cluster;

[0131] a distributed time-varying formation tracking controller construction unit, configured to construct a distributed time-varying formation tracking controller for the unmanned vehicle cluster according to the desired formation vector h i of the i-th unmanned vehicle in the unmanned vehicle cluster where e i (t) is a formation tracking error of the i-th unmanned vehicle in the unmanned vehicle cluster at time t; w i1 is an action strength of node 1 to node i in the directed graph; x i (t) and x j (t) respectively represent positions of the i-th and j-th unmanned vehicles at time t; h i (t) and h j (t) respectively represent desired formation vectors of the i-th and j-th unmanned vehicles at time t; x1(t) represents a state of a leader unmanned vehicle at time t; N is a number of unmanned vehicles in the unmanned vehicle cluster; u i (t) represents a control input vector of the i-th unmanned vehicle at time t; K represents a gain matrix; η and ξ are both normal numbers; v i (t) represents a time-varying formation tracking compensation input of the i-th unmanned vehicle at time t.

[0132] The distributed time-varying formation tracking control module specifically comprises:

[0133] a controller parameter determination unit, configured to determine parameters of the distributed time-varying formation tracking controller, so that and η≥γ; where γ is an upper limit of a control input of the leader unmanned vehicle;

[0134] a distributed time-varying formation tracking control unit, configured to, after the parameters are determined, perform distributed time-varying formation tracking control on the unmanned vehicle cluster by using the distributed time-varying formation tracking controller.

[0135] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between various embodiments can be referred to each other.

[0136] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A distributed time-varying formation tracking and control method for an unmanned aerial vehicle (UAV) swarm system, characterized in that, include: Establish a dynamic model of the UAV; Based on the dynamic model of the UAV, an inner and outer loop control architecture is adopted to establish a UAV swarm model for formation control; the UAV swarm includes a leader UAV with unknown input and multiple follower UAVs. Consider a drone swarm system consisting of N drones, denoted as F. A ={1,2,…,N}; At the formation control level, the i-th UAV is modeled as follows: in, Let F represent the position and control input vector of the i-th UAV, respectively; N≥1; i∈F A ; After modeling all the drones in the drone swarm, the drone swarm model used for formation control is obtained; Obtain the desired formation configuration of the drone swarm; Based on the desired formation configuration, a distributed time-varying formation tracking controller is constructed for the UAV swarm. The step of constructing a distributed time-varying formation tracking controller for the UAV swarm based on the desired formation configuration specifically includes: Add control constraints to the leader drone in the drone swarm. Where x1 represents the state of the leader drone; u1 represents the control input vector of the leader drone; Algebraic graph theory is used to describe the interaction topology among multiple drones in the drone swarm, resulting in a directed graph corresponding to the interaction topology of the drone swarm. Using time-varying vectors Describe the desired formation configuration; where h i Let be the desired formation vector of the i-th drone in the drone swarm; Based on the expected formation vector h of the i-th drone in the drone swarm i A distributed time-varying formation tracking controller is constructed for the aforementioned UAV swarm. Where e i (t) represents the formation tracking error of the i-th UAV in the UAV cluster at time t; w i1 x represents the strength of the interaction between node 1 and node i in the directed graph. i (t) and x j (t) represents the positions of the i-th and j-th UAVs at time t, respectively; h i (t) and h j x(t) represents the expected formation vectors of the i-th and j-th drones at time t, respectively; x1(t) represents the state of the leader drone at time t; N is the number of drones in the drone cluster; u i (t) represents the control input vector of the i-th UAV at time t; K represents the gain matrix; η and ξ are both positive constants; v i (t) represents the time-varying formation tracking compensation input of the i-th UAV at time t; The distributed time-varying formation tracking controller is used to perform distributed time-varying formation tracking control on the UAV cluster. The step of performing distributed time-varying formation tracking control on the UAV cluster according to the distributed time-varying formation tracking controller specifically includes: Determine the parameters of the distributed time-varying array tracking controller such that... And η≥γ; where γ is the upper bound of the leader drone control input; Once the parameters are determined, the distributed time-varying formation tracking controller is used to perform distributed time-varying formation tracking control on the UAV cluster. The method also includes: establishing a Tello-UWB physical experimental verification platform to conduct experimental verification of the distributed time-varying formation tracking control of the distributed time-varying formation tracking controller on the UAV cluster; Specifically, a Tello-UWB physical experimental verification platform was built to conduct experimental verification of the proposed distributed time-varying formation tracking controller. The physical experimental verification platform consists of 5 Tello UAVs, a UWB positioning system and a ground station. Each Tello UAV is equipped with a UWB positioning tag, and the ground station communicates with the UAV at a frequency of 50Hz. (1) Setting up a drone swarm system Consider a drone swarm system consisting of one leader drone and four follower drones; the drones perform collaborative reconnaissance missions in a rotating circular formation; the five drones fly at a fixed altitude, so only the formation tracking control problem in the XY plane needs to be considered; the model parameters of the leader are set to x1(0) = [0.03, 0.03, 0]. T The leader drone will then move in uniform linear motion within the XY plane; (2) Desired time-varying formation design To characterize the desired rotating circular formation configuration, for each UAV, the time-varying formation vector is... The settings are as follows: If the drone swarm system achieves the desired formation tracking, each drone will move in the XY plane with a radius of r and an angular velocity of... Revolve around the leader; (3) Formation tracking controller parameter design For all drones, the feasibility conditions for formation tracking can be verified, and the formation compensation input v can be obtained. i for: Design the gain matrix K=1, and choose positive constants η=0.2 and ζ=0.01; (4) Simulation conditions setting and result analysis Let r = 2m, The initial positions of each UAV in the XY plane are generated by random numbers between -3 and 3; in the motion trajectory of the UAV swarm system, t represents the simulation time, the horizontal axis x(t) is the position along the X-axis, and the vertical axis y(t) is the position along the Y-axis; let The time-varying formation tracking error of UAV i is represented by , where i = 2, 3, 4, 5. The x-axis of its Euclidean norm is the simulation time, and the y-axis is the formation tracking error. The UAV swarm system achieves the desired rotating circular formation tracking.

2. The method according to claim 1, characterized in that, The establishment of the dynamic model of the UAV specifically includes: Establish a dynamic model of the UAV Where x, y, z represent the UAV's position in space; φ, θ, ψ represent the roll angle, pitch angle, and yaw angle; m represents the UAV's mass; I xx ,I yy ,I zz Let U1, U2, U3, and U4 represent the moments of inertia about the x, y, and z axes, respectively; L represents the distance between the motor shaft and the center of the machine body; g represents the acceleration due to gravity; and U1, U2, U3, and U4 represent the control inputs of the system determined by the angular velocities of the four rotors.

3. A distributed time-varying formation tracking and control system for an unmanned aerial vehicle (UAV) swarm system, used to implement the distributed time-varying formation tracking and control method for an UAV swarm system as described in claim 1, characterized in that, The distributed time-varying formation tracking and control system of the unmanned aerial vehicle swarm system includes: The UAV dynamics model building module is used to build the dynamics model of the UAV. The drone swarm model building module is used to build a drone swarm model for formation control based on the dynamic model of the drone and using an inner and outer loop control architecture; the drone swarm includes a leader drone with unknown input and multiple follower drones. The desired formation configuration acquisition module is used to acquire the desired formation configuration of the UAV cluster; A distributed time-varying formation tracking controller construction module is used to construct a distributed time-varying formation tracking controller for the UAV cluster according to the desired formation configuration. The distributed time-varying formation tracking controller construction module specifically includes: The leader control constraint unit is used to add control constraints to the leader drone in the drone swarm. Where x1 represents the state of the leader drone; u1 represents the control input vector of the leader drone; A directed graph generation unit is used to describe the interaction topology between multiple drones in the drone swarm using algebraic graph theory, and to obtain a directed graph corresponding to the interaction topology of the drone swarm. Desired formation configuration characterization unit, used to utilize time-varying vectors Describe the desired formation configuration; where h i Let be the desired formation vector of the i-th drone in the drone swarm; A distributed time-varying formation tracking controller construction unit is used to construct the formation based on the desired formation vector h of the i-th UAV in the UAV cluster. i A distributed time-varying formation tracking controller is constructed for the aforementioned UAV swarm. Where e i (t) represents the formation tracking error of the i-th UAV in the UAV cluster at time t; w i1 x represents the strength of the interaction between node 1 and node i in the directed graph. i (t) and x j (t) represents the positions of the i-th and j-th UAVs at time t, respectively; h i (t) and h j x(t) represents the expected formation vectors of the i-th and j-th drones at time t, respectively; x1(t) represents the state of the leader drone at time t; N is the number of drones in the drone cluster; u i (t) represents the control input vector of the i-th UAV at time t; K represents the gain matrix; η and ξ are both positive constants; v i (t) represents the time-varying formation tracking compensation input of the i-th UAV at time t; A distributed time-varying formation tracking control module is used to perform distributed time-varying formation tracking control on the UAV cluster according to the distributed time-varying formation tracking controller; The distributed time-varying formation tracking and control module specifically includes: The controller parameter determination unit is used to determine the parameters of the distributed time-varying formation tracking controller, such that... And η≥γ; where γ is the upper bound of the leader drone control input; A distributed time-varying formation tracking control unit is used to perform distributed time-varying formation tracking control on the UAV cluster using the distributed time-varying formation tracking controller after the parameters are determined.

4. The system according to claim 3, characterized in that, The UAV dynamics model establishment module specifically includes: The UAV dynamics model building unit is used to build the dynamics model of the UAV. Where x, y, z represent the UAV's position in space; φ, θ, ψ represent the roll angle, pitch angle, and yaw angle; m represents the UAV's mass; I xx ,I yy ,I zz Let U1, U2, U3, and U4 represent the moments of inertia about the x, y, and z axes, respectively; L represents the distance between the motor shaft and the center of the machine body; g represents the acceleration due to gravity; and U1, U2, U3, and U4 represent the control inputs of the system determined by the angular velocities of the four rotors.

Images