Heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method and system

Abstract

The invention belongs to the technical field of heterogeneous multi-agent trajectory tracking control, and particularly discloses a heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method and system. Aiming at the multi-unmanned aerial vehicle-multi-unmanned vehicle trajectory tracking control problem with unknown parameters and external interference, the invention designs an instruction filtering fuzzy adaptive controller based on state constraint so as to track a target position, and utilizes a fuzzy logic theory to process unknown nonlinear terms in a system so as to realize the tracking of the target position. A method of combining an instruction filtering technology and an error compensation mechanism is adopted, the problem of'calculation explosion 'occurring in a traditional backstepping method is solved, the influence of filtering errors is eliminated, and an output state is constrained in a given space based on a barrier Lyapunov function. According to the invention, the control performance of the multi-unmanned aerial vehicle-multi-unmanned vehicle formation system is improved, and meanwhile, it is ensured that the semi-global consistency of the multi-agent system is finally bounded.

Description

Technical Field

[0001] The present invention belongs to the technical field of heterogeneous multi-agent trajectory tracking control, and in particular relates to a heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method and system. Background Art

[0002] In recent years, multi-agent systems have attracted widespread attention from researchers due to their high efficiency, flexibility, and strong stability. Among them, the collaborative control of unmanned systems such as cooperative reconnaissance of multi-rotor drones, cooperative assembly of multiple manipulators, and synchronous control of power grids has become a research hotspot. At present, the research on control methods of multi-unmanned systems mainly focuses on homogeneous agents, that is, it is assumed that all agents have the same dynamic and kinematic models. However, homogeneous multi-agent formations have the problem of a single mode, while heterogeneous multi-agent cross-domain collaborative control can achieve functional complementarity of different unmanned systems. For example, a heterogeneous multi-agent formation composed of multiple drones and multiple unmanned vehicles has the characteristics of a wide detection range, strong communication capability, and flexible maneuverability of quad-rotor drones and the advantages of strong load capacity and fast execution speed of unmanned vehicles. It can be applied in air-to-ground collaborative surveillance, prevention and control, and reconnaissance. Compared with homogeneous multi-agent formations, there are significant differences in structure and system parameters between the units of heterogeneous multi-agent systems. Traditional control methods cannot unify agents with different structures and parameters, and cannot provide effective solutions for cross-domain collaborative control of heterogeneous multi-agents. Therefore, the design of collaborative control solutions for heterogeneous multi-agent formations is very complex and challenging.

[0003] At present, in order to make the system obtain better control performance, researchers have proposed many effective control strategies, such as backstepping control, robust control, adaptive control and other advanced control technologies. However, most multi-UAV-multi-UAV collaborative control strategies focus on the two-dimensional position subsystem and altitude subsystem of the multi-UAV-UAV formation when designing, while the attitude angle system unique to the UAV is often ignored. However, in practical applications, such as when performing collaborative reconnaissance and coordinated strike tasks, by maintaining a consistent attitude, the accuracy and real-time nature of information transmission can be ensured, thereby improving the efficiency of task execution. Attitude control can ensure that each member of the formation accurately reaches and maintains the required attitude within a specific time, which is crucial to maintaining the overall stability and formation of the formation. In addition, in flight missions, due to safety needs, workspace limitations and physical conditions, state constraints need to be considered when designing controllers for heterogeneous multi-agent formations. The multi-agent control idea based on state constraints is less used but worth studying.

[0004] In another frontier field, the introduction of a large number of advanced control methods provides more effective solutions for dealing with nonlinear system problems. Among them, the adaptive backstepping method has been successfully applied to the multi-UAV-multi-UAV formation system and has achieved good control effects. However, the disadvantages of the backstepping method are mainly reflected in the fact that some functions of some systems must be linear, and repeated derivation during the design process will cause computational explosion problems. The prior art solves the problem that functions of some systems must be linear by using fuzzy logic systems FLS or neural networks NN to approximate the nonlinear terms of the system. In response to the computational explosion problem, the prior art has proposed the dynamic surface control DSC method to deal with it, and has achieved remarkable results. However, there will be filtering errors when using the dynamic surface control method, and this error cannot be eliminated, which will affect the control effect. Summary of the invention

[0005] The purpose of the present invention is to propose a heterogeneous multi-agent command filtering fuzzy adaptive constraint control method, which fully considers the influence of external interference and unknown parameters, and enables a multi-UAV-multi-UAV formation system to quickly track the desired signal.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] A heterogeneous multi-agent command filtering fuzzy adaptive constraint control method comprises the following steps:

[0008] Step 1. For a UAV-UAV formation system consisting of a leader and multiple followers, i.e., a heterogeneous multi-agent system, a dynamic model of each follower considering external interference and unknown parameters is given, and the dynamic model of each follower is simplified;

[0009] Step 2. For a heterogeneous multi-agent system consisting of multiple drones and multiple unmanned vehicles, transform the heterogeneous drone and unmanned vehicle models into a unified second-order nonlinear model through model conversion;

[0010] Step 3. Design a state-constrained heterogeneous multi-agent command filter fuzzy adaptive controller for the heterogeneous multi-agent system. While ensuring that the formation follows the leader in the OXY plane and altitude, the stability of the formation attitude subsystem is considered to ensure that the UAV flies according to the predetermined attitude and altitude. The output state of the heterogeneous multi-agent system is constrained by introducing the obstacle Lyapunov function to ensure that the position and attitude of each agent remain in the safe area. In the backstepping design process, command filtering is introduced to avoid computational complexity problems and eliminate the influence of filtering errors by constructing an error compensation mechanism.

[0011] Step 4. Use the command filtering fuzzy adaptive controller to realize the tracking control of the multi-UAV-multi-UAV formation system.

[0012] In addition, based on the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method, the present invention also proposes a corresponding heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control system, and its technical solution is as follows:

[0013] A heterogeneous multi-agent command filtering fuzzy adaptive constraint control system includes the following modules:

[0014] The model building module simplifies the dynamics models of drones and unmanned vehicles for a multi-drone-multi-unmanned vehicle system consisting of a leader and multiple followers. Through transformation, a dynamics model of a multi-drone-multi-unmanned vehicle formation system that takes into account external interference and unknown parameters is constructed.

[0015] The controller design module designs a command filter fuzzy adaptive controller based on state constraints for the multi-UAV-multi-UAV system, so that all signals in the closed-loop system are bounded in finite time and the output state variables are constrained within a given interval;

[0016] The stability analysis module is used to perform stability analysis on the command filter fuzzy adaptive controller based on state constraints. According to the virtual control signal, the command filter fuzzy adaptive controller, the parameter update law and the error compensation signal, the Lyapunov stability theory is used to prove that all signals of the closed-loop system are semi-globally consistent and ultimately bounded, and at the same time, the output state variables of the formation are constrained within a given interval.

[0017] The tracking control module is used to control the multi-UAV-multi-UAV system using a command filtering fuzzy adaptive controller.

[0018] In addition, based on the above-mentioned heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method, the present invention also proposes a computer device; the computer device includes a memory and one or more processors, and the memory stores executable code. When the processor executes the executable code, the steps of the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method as described above are implemented.

[0019] In addition, based on the above-mentioned heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method, the present invention also proposes a computer-readable storage medium, on which a program is stored. When the program is executed by the processor, the steps of the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method as described above are implemented.

[0020] The present invention has the following advantages:

[0021] As described above, the present invention proposes a heterogeneous multi-agent command filtering fuzzy adaptive constraint control method, which adopts a command filtering fuzzy adaptive control scheme to overcome the problems of unknown model parameters and external disturbances, and constructs an error compensation mechanism by introducing command filtering technology, thereby avoiding the "computational complexity" problem and eliminating the influence of filtering errors. In addition, by introducing the obstacle Lyapunov function to constrain the output state of the heterogeneous multi-agent system, it is ensured that the position and posture of each agent remain in a safe area. The method of the present invention fully considers the influence of external interference and unknown parameters, and can enable the multi-UAV-multi-unmanned vehicle formation system to quickly track the desired signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method in an embodiment of the present invention;

[0023] Figure 2 It is a block diagram of the instruction filtering fuzzy adaptive control based on state constraints of the i-th intelligent agent in the present invention;

[0024] Figure 3 It is a simulation communication topology diagram;

[0025] Figure 4 It is a schematic diagram of the expected formation offset for simulation;

[0026] Figure 5 This is a simulation diagram of the formation position and altitude tracking curve after the control method of the present invention is adopted;

[0027] Figure 6 Input simulation diagram for formation position and altitude control after adopting the control method of the present invention;

[0028] Figure 7 This is a simulation diagram of the tracking error of the attitude subsystem of the UAVs in the formation after the control method of the present invention is adopted;

[0029] Figure 8 It is a simulation diagram of attitude control input of unmanned aerial vehicles in a formation after adopting the control method of the present invention;

[0030] Fig. 9 It is a simulation diagram of the position error variables and constraint boundaries of the unmanned vehicles in the formation after adopting the control method of the present invention;

[0031] Fig.10 It is a simulation diagram of error variables and constraint boundaries of UAV 1 in the formation after adopting the control method of the present invention;

[0032] Fig.11 It is a simulation diagram of the position error variables and constraint boundaries of UAV 2 in the formation after adopting the control method of the present invention;

[0033] Fig.12This is a simulation diagram of the attitude error variables and constraint boundaries of UAV 2 in the formation after adopting the control method of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0035] Example 1

[0036] This embodiment 1 describes a state-constrained heterogeneous multi-agent command filtering fuzzy adaptive control method. This method is aimed at the trajectory tracking control problem of multi-UAV-multi-UAV formation with unknown parameters and external interference. A state-constrained heterogeneous multi-agent command filtering fuzzy adaptive controller is designed to achieve the tracking of the target position. The unknown nonlinear terms in the UAV system are processed by fuzzy logic theory. The method combining command filtering technology with error compensation mechanism solves the "computational explosion" problem in the traditional backstepping method and eliminates the influence of filtering errors. By introducing the barrier Lyapunov function to constrain the output state of the heterogeneous multi-agent system, it is ensured that the position and posture of each agent remain in a safe area.

[0037] like Figure 1 As shown, the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method includes the following steps:

[0038] Step 1. For a multi-UAV-multi-UV formation system consisting of a leader and multiple followers, i.e., a heterogeneous multi-agent system, a dynamic model of each follower considering external interference and unknown parameters is given, and the dynamic model of each follower of the UAV-UV is simplified.

[0039] The dynamic model of the quadrotor drone in the drone-unmanned vehicle formation system is shown in formula (1):

[0040]

[0041] Among them, (x i ,y i ,z i ) is the position of the i-th UAV in the ground coordinate system, φ i ,θ i , i Respectively represent the roll angle, pitch angle and yaw angle of the i-th UAV, i = 1, 2, 3, ..., N1, N1 represents the number of UAVs; m i is the mass of the i-th UAV; g is the acceleration due to gravity; (J i,x ,J i,y ,J i,z ) are the rotational inertia of the drone in the x, y, and z directions respectively; Gi,x , G i,y , G i,z , G i,φ , G i,θ , G i,ψ Respectively represent the air resistance coefficient of the i-th UAV in the x direction, y direction, z direction, roll angle, pitch angle and yaw angle; d i,x d i,y d i,z d i,φ d i,θ d i,ψ They represent the unknown external disturbances of the i-th UAV in the x direction, y direction, z direction, roll angle, pitch angle and yaw angle respectively; T i Represents the total thrust of the drone, U i,φ , U i,θ , U i,ψ They represent the control torques for the roll angle, pitch angle and yaw angle of the i-th UAV respectively.

[0042] Since the dynamics of the quadrotor drone can be divided into position dynamics and attitude dynamics, in order to facilitate the design of the formation controller, when designing the position subsystem, the drone position dynamics model is expressed as:

[0043]

[0044] Among them, u xi 、u yi 、u zi As the control input of the drone in the x-axis, y-axis, and z-axis directions, f xi 、f yi 、f zi represents the nonlinear term in the i-th UAV. i,x d i,y and d i,z As an unknown disturbance.

[0045] u xi =(cosφ i sinθ i cosψ i +sinφ i sinψ i )T i .

[0046] u yi =(cosφ i sinθ i sinψ i +sinφ i sinψ i )T i .

[0047] u zi =(cosθ i cosφ i )T i .

[0048]

[0049] The dynamic model of the two-wheel drive unmanned vehicle in the UAV-unmanned vehicle formation system is shown in formula (3):

[0050]

[0051] Among them, p xn and p yn represents the position of the nth unmanned vehicle, v n and w n Respectively represent the linear velocity and angular velocity of the nth unmanned vehicle, θ n is the direction angle of the nth unmanned vehicle.

[0052] Due to the nonholonomic constraints Define the coordinates (x hn ,y hn ) as a reference point, (x n ,y n ) is used as the inertial position of the unmanned vehicle, and the position of the unmanned vehicle is expressed as:

[0053]

[0054] Then, taking the second-order derivative we get:

[0055]

[0056] Among them, u xn and u yn As the new control input of the reference point, f xn and f yn is the lumped nonlinear function, d xn and d yn As the unknown disturbance of the unmanned vehicle, L n Indicates the distance from the inertial position of the unmanned vehicle to the reference point.

[0057]

[0058] Step 2. For a heterogeneous multi-agent system consisting of multiple drones and multiple unmanned vehicles, the heterogeneous drone and unmanned vehicle models are converted into a unified second-order nonlinear model through model conversion.

[0059] Considering a heterogeneous multi-agent formation consisting of N1 drones and N2 unmanned vehicles, although the position subsystem models of the unmanned vehicles and drones have been transformed, there is still the problem of different dimensions. For the three-dimensional drone model, by decoupling the height subsystem of the Z axis, the formation model is only considered in the OXY two-dimensional plane, and the height subsystem of the drone formation is designed separately. Since the attitude subsystem is a state unique to drones, it should also be designed separately when designing the controller.

[0060] According to formula (2) and formula (4), the control model of the UAV-UAV formation is uniformly expressed on the OXY two-dimensional plane as a second-order nonlinear model as shown in formula (5):

[0061]

[0062] Among them, x ij1 Represents the position signal, x ij2 Represents the speed signal. i∈N and satisfies N=N1∪N2.

[0063] When i∈N1, it represents the i-th UAV, and when i∈N2, it represents the i-th unmanned vehicle; j=1,2,3,4,5,6, when j=1, j=2, j=3, j=4, j=5, j=6, they represent the z, x, y, φ, θ, ψ directions respectively.

[0064] Y i,j is the system output, F i,j is the nonlinear unknown term of the system model, d i,j is the external disturbance to the UAV and the unmanned vehicle, where the unmanned vehicle is only subject to external disturbances in the x and y directions, that is, when d i,j represents the external disturbance to the unmanned vehicle, i∈N2, j∈[2,3]; when d i,j When represents the external disturbance to the UAV, i∈N1, j∈[1,6].

[0065] w i represents the input signal, w i =B i u i .

[0066] where u i Indicates control input.

[0067]

[0068] Where I represents a unit vector.

[0069] Step 3. For the UAV-UAV formation system, i.e., the heterogeneous multi-agent system, a heterogeneous multi-agent command filtering fuzzy adaptive controller based on state constraints is designed. While ensuring that the formation follows the leader in the OXY plane and altitude, the stability of the formation attitude subsystem is considered to ensure that the UAV can fly according to the predetermined attitude and altitude; the output state of the heterogeneous multi-agent system is constrained by introducing the obstacle Lyapunov function to ensure that the position and attitude of each agent are kept in the safe area; command filtering is introduced in the backstepping design process, and an error compensation mechanism is constructed to avoid computational complexity problems and eliminate the influence of filtering errors.

[0070] According to graph theory knowledge, graph G is used to represent the communication relationship between each drone.

[0071] Among them, G = (ν, J) is composed of the vertex set ν = {1, 2, ..., N1} and the edge set J∈ν×ν; (k, i)∈J means that node i can receive information from node k. i ={k∈ν:(k,i)∈J,k≠i} is the neighborhood of the i-th node, and the adjacency matrix A of the graph G = [a i,k ]∈R n×n ; a i,k represents the communication relationship coefficient between followers; when node i can receive information from node k, a i,k =1, otherwise a i,k =0; let the diagonal matrix B = diag{b1,b2,...,b i} to represent the information acquisition of each follower to the leader; b i is the communication relationship coefficient between the follower and the leader; when the i-th follower can obtain the leader's information, b i =1, otherwise b i = 0. The given leader signal is continuous and derivable; the external disturbance d i,j is bounded and satisfies |d i,j |≤D i,j , D i,j >0, where D i,j Indicates d i,j The modulus value of .

[0072] Lemma 1: The instruction filter is defined as follows:

[0073]

[0074] in, is the output signal of the command filter, and α1 is the input signal of the command filter; if the input signal α1 satisfies when t≥0 Then for any μ>0, there must exist ω n>0,ξ∈(0,1] such that Stablize.

[0075] in express The initial value of α1(0) represents the initial value of α1. Initial value of representation t represents time, ρ1 and ρ2 are positive numbers.

[0076] Lemma 2: If f(x) is a continuous function defined in a compact set γ, there exists a fuzzy logic system M i,j T S(x), satisfies sup x∈γ |f(x)-M i,j T S(x)|≤ε, where ε is a positive constant, M i,j represents the fuzzy weight vector, and S(x) is the fuzzy basis function.

[0077]

[0078] Where τ=1,2,...,n, n is a constant, l τ 、j τ Represent the width and center vector of the Gaussian function respectively.

[0079] According to Lemma 2 and Young's inequality, we can get:

[0080]

[0081] Based on the multi-UAV-multi-UAV dynamics model shown in formula (5), a state-constrained command filter fuzzy adaptive controller is designed. According to graph theory, the tracking error of the i-th follower is defined as:

[0082]

[0083] Among them, Z i,j,1 , Z i,j,2 represents the tracking error variable, Y i,j represents the distance of the ith agent relative to the leader, Y k,j represents the distance between the kth agent and the ith agent, h k,j represents the offset of the kth agent relative to the ith agent; R j represents the leader signal, h i,j represents the expected formation offset, i = 1, 2, ..., N, j = 1, 2, 3, 4, 5, 6, x ij1,c is the output signal of the command filter, where Instruction filtering is defined as in Lemma 1.

[0084] The compensation tracking error is defined as shown in formula (9):

[0085]

[0086] Among them, v ij1 、v ij2 To compensate for tracking error; is the error compensation signal.

[0087] The designed error compensation signal is as follows:

[0088]

[0089] Among them, k1 and k2 are both positive numbers, and k1=k ij1 , k2=k ij2 , k ij1 , k ij2 represents the control gain, k ij1 , k ij2 are all constant values, Design the following virtual control signal α1 and indirect control signal ω i :

[0090]

[0091] in, Represents x kj1 The derivative of k b Represents the control gain.

[0092] Design parameter update law As shown in formula (14):

[0093]

[0094] Among them, r1>0, h i >0,ρ i,j >0 is the design parameter.

[0095] Due to the underactuated and strongly coupled characteristics of the quadrotor drone, the expected signal R φ , R θ It is obtained by inversely solving the control input of the position subsystem and the desired yaw angle.

[0096] In step 3, R φ , R θ The formula obtained by inversely solving the control input of the position subsystem and the desired yaw angle is as follows:

[0097]

[0098] Among them, wi,1 represents the indirect control signal of the i-th UAV in the z direction, w i,2 represents the indirect control signal of the ith UAV in the x direction, w i,3 Represents the indirect control signal of the y direction of the i-th UAV.

[0099] The control goal of the multi-drone-multi-unmanned vehicle formation is to enable the formation to follow the virtual leader while keeping each agent stable. Since the stability of the altitude subsystem and attitude subsystem of the drone formation needs to be controlled, a quadcopter drone is selected as the leader, and the leader signal is defined as:

[0100] (R1, R2, R3, R4, R5, R6) = (R z ,R x ,R y ,R φ ,R θ ,R ψ );

[0101] Define the formation tracking error E i,j for:

[0102] E i,j =Y i,j -R j (t)-h i,j ;

[0103] in, ( h i,1 ,h i,2 ,h i,3 ,h i,4 ,h i,5 ,h i,6 )=(h i,z ,h i,x ,h i,y ,h i,φ ,h i,θ ,h i,ψ ) is the expected offset of the ith follower relative to the leader; h i,z 、h i,x 、h i,y 、h i,φ 、h i,θ 、h i,ψ are the desired formation offsets of the i-th agent in the x, y, z directions, roll angle, pitch angle, and yaw angle, respectively.

[0104] Finally, the command filter fuzzy adaptive controller based on state constraints is designed as:

[0105]

[0106] Stability analysis is performed on a state-constrained heterogeneous multi-agent command filtering fuzzy adaptive controller.

[0107] According to the designed virtual control signal, heterogeneous multi-agent command filtering fuzzy adaptive controller, parameter update law and error compensation signal, the Lyapunov stability theory is used to prove that the closed-loop system is semi-globally consistently ultimately bounded, and the output state is constrained within a given domain.

[0108] Select the barrier Lyapunov function Taking the derivative of it, we get:

[0109]

[0110] Among them, k b is a constant value, k b Represents the control gain.

[0111] Select the Lyapunov function Taking the derivative of it, we get:

[0112]

[0113] According to Lemma 1, |x ij1,c Substituting -α1|≤μ into formula (18), we get:

[0114]

[0115] Select the Lyapunov function Taking the derivative of it, we get:

[0116]

[0117] Where θ=max{||M i,j || 2}, represents the estimated value of θ, M i,j represents the fuzzy weight vector, Indicates the required accuracy. Select the Lyapunov function C = C1 + C2 and take its derivative to obtain:

[0118]

[0119] Select the Lyapunov function Taking the derivative of it, we get:

[0120] According to Young's inequality, we get:

[0121]

[0122] Substituting formula (23) and formula (24) into formula (22), we obtain:

[0123]

[0124] in:

[0125] a=min{2k v1 ,2k1,2k1-1,2k2,m i};

[0126]

[0127] Integrating formula (25) at (0, t] yields:

[0128] Where V(0) represents the initial value of V; then we get

[0129] because in express The boundary of so

[0130] Because X ij1 ≤|Z ij1 |+|R i,j +h i,j |, and |R i,j +h i,j |≤Y0, so Where X ij1 represents the position of the ith follower, R i,j represents the distance between the leader and the follower, Y0 and k cl Indicates a constant value.

[0131] For v ij2 , From -aV+b, in the interval (0, t], we have:

[0132] V≤(V(0)-b / a)e -at +b / a≤V(0)+b / a.

[0133] So ij2 , All belong to And the system output state variables are constrained to be x ={|X ij1 |≤k cl}, where Ω, Ω xrepresents a set. Therefore, the closed-loop system is semi-globally consistent and ultimately bounded, and the output state variables of the UAV-UAV platooning system are constrained within a given domain.

[0134] Step 4. Use the command filtering fuzzy adaptive controller to realize the tracking control of the multi-UAV-multi-UAV formation system.

[0135] Aiming at the control problem of uncertain heterogeneous multi-agent system, a state-constrained command filtering fuzzy adaptive formation control strategy is proposed. Firstly, the fuzzy adaptive technology is used to deal with the uncertain terms in the heterogeneous multi-agent system. The tracking error of each agent in the UAV-UAV formation is defined according to the expected signal and the expected formation offset. The UAV is divided into a position subsystem and an altitude and attitude subsystem. Then, the position subsystem model of the UAV and the position subsystem model of the UAV are transformed into a unified second-order nonlinear model, and the position controller of the UAV-UAV formation is designed. At the same time, since the UAV is a typical under-actuated system, the expected signals of its pitch angle and roll angle can be obtained by inverse analysis according to the control input of the position subsystem and the expected yaw angle. For the unique altitude and attitude subsystem of the UAV, a separate controller is designed. Secondly, the obstacle Lyapunov function is introduced in the position subsystem, altitude subsystem and attitude subsystem respectively to limit the output state of each agent in the heterogeneous multi-agent formation within the predetermined constraint boundary. The tracking control of the multi-UAV-UAV formation system is realized by using the designed state-constrained command filtering fuzzy adaptive controller.

[0136] like Figure 2 It shows the block diagram of the command filtering fuzzy adaptive control of the i-th agent based on state constraints. Figure 2 It mainly includes a position controller, an attitude and altitude controller, and a state constraint. The controller designed by the present invention includes a position controller and an attitude and altitude controller. Due to the different dimensionality of UAVs and unmanned vehicles, the system dimension of UAVs is three-dimensional, and the system dimension of unmanned vehicles is two-dimensional. The heterogeneous multi-agent command filter fuzzy adaptive constraint control method of the present invention decouples the altitude subsystem and only considers the formation model on a two-dimensional plane to design the position controller, and separately designs the attitude and altitude controller of the UAV. The controller design steps used are the same, and can be uniformly expressed as the design of a command filter fuzzy adaptive controller based on state constraints.

[0137] The control block diagram of the i-th QUAV (quad-rotor drone) is as follows Figure 2 This embodiment designs controllers for the position subsystem, attitude and altitude subsystem of the drone respectively, and constrains the output state of the system.

[0138] First, for the attitude subsystem of the quadrotor drone in the formation, the indirect control signal w obtained by the position loop design is i,1 、w i,2 、w i,3 and the desired yaw angle R ψ Inversely solve the expected roll angle R φ and the desired pitch angle R θ Secondly, the desired signal R φ , R θ , R ψ It is used as a reference signal for attitude loop control for the next design step.

[0139] The proposed heterogeneous multi-agent command filtering fuzzy adaptive control method based on state constraints is simulated in a virtual environment to verify the feasibility of the control method proposed in the present invention. In the simulation experiment, the communication topology relationship between the leader QUAV 0 and the followers QUAV 1 (UAV 1), QUAV 2 (UAV 2), UGV 1 (Unmanned Vehicle 1), and UGV 2 (Unmanned Vehicle 2) is shown in the figure below. Figure 3 shown. Figure 3 Represents the desired final formation of the leader and followers.

[0140] The leader (QUAV 0) reference signal is set as follows:

[0141] R z =0.5, R x = sin(2t), R y =cos(2t), R ψ =1 / 5π, d i,j =0.1sin(1 / 3πt).

[0142] The parameters of each agent model are shown in Table 1, where:

[0143] (G i,1 ,G i,2 ,G i,3 ,G i,4 ,G i,5 ,G i,6 )=(G i,z ,G i,x ,G i,y ,G i,φ ,G i,θ ,G i,ψ );

[0144] Table 1 Model parameters

[0145]

[0146] Control parameters related controller design parameters are shown in Table 2:

[0147] Table 2 Control design parameters

[0148]

[0149]

[0150] At the initial moment, the initial height and attitude angles of each UAV are zero. The initial state of the two-dimensional position system of the multi-UAV-multi-UAV formation system and the expected formation offset are defined as x1(0)=[0.5,1.5], x2(0)=[-0.5,0.5], x3(0)=[-0.5,0.5], x4(0)=[0.5,1.5], H1=[0,0.5,0.5,0,0,0], H2=[0,-0.5,-0.5,0,0,0], H3=[-0.5,-0.5], H4=[0.5,0.5]. Among them, x1(0), x2(0), x3(0), and x4(0) represent the initial states of the two-dimensional position systems of followers QUAV 1, QUAV 2, UGV 1, and UGV 2, respectively, and H1, H2, H3, and H4 represent the expected formation offsets of followers QUAV 1, QUAV 2, UGV 1, and UGV 2, respectively.

[0151] The simulation results of the heterogeneous multi-agent command filtering fuzzy adaptive constraint control method are shown in Figures 4 to 12 shown.

[0152] Figure 4 It is the three-dimensional flight trajectory curve of the UAV-UCV formation. In the simulation results, whether in the OXY plane coordinate system or in the Z height coordinate system, the UCV and the UAV can quickly and accurately track the leader signal and complete the requirements according to the expected predetermined trajectory and formation mode. Figure 5 and Figure 7 is the response curve of the tracking error of the formation in the position subsystem and attitude subsystem, Figure 7 Medium E i,4 、E i,5 、E i,6 They represent the tracking errors of the roll angle, pitch angle, and yaw angle of the UAVs in the formation respectively; Figure 6 and Figure 8 It is the control input of the formation. It can be seen from the figure that all followers can track the expected signal within 2s, and the tracking error is kept at a low value, and the tracking effect is good. It can be seen that the multi-UAV-multi-UAV formation system based on the control method of the present invention can still maintain good tracking performance in the presence of external interference and unknown parameter problems. Fig. 9 represents the system error variable of the position of the unmanned vehicle in the formation, where k bi represents the constraint bound, the constraint bound k given by the unmanned vehicle biThe value is 0.065; Fig.10 represents the error variable and constraint bound of UAV 1 in the formation, where k b1 , k b2 , k b3 , k b4 , k b5 , k b6 They represent the constraints of the UAV’s z-axis, x-axis, y-axis, roll angle, pitch angle, and yaw angle, respectively. b1 , k b2 , k b3 , k b4 , k b5 , k b6 They are 0.51, 0.035, 0.035, 0.9, 1.81 and 0.629 respectively. It can be seen that the error variables of the system are all constrained within the given range. Fig.11 and Fig.12 This is a comparison diagram of the error variables of UAV 2 with and without state constraints. Fig.12 Medium 2,4 、v 2,5 、v 2,6 are the error variables of the roll angle, pitch angle, and yaw angle of the UAV, respectively, and k bi Take k respectively b1 =0.035, k b2 =0.081, k b3 =0.02, k b4 =0.28, k b5 =0.55, k b6 =1.01, it can be seen that the output state error of the system under the constraint always remains within the given interval.

[0153] The above simulation results show that the multi-UAV-multi-UAV command filtering fuzzy adaptive control method based on state constraints in the present invention can efficiently track the reference signal and has practical implementation significance.

[0154] Example 2

[0155] This embodiment 2 describes a heterogeneous multi-agent command filtering fuzzy adaptive constraint control system, which is based on the same inventive concept as the heterogeneous multi-agent command filtering fuzzy adaptive constraint control method described in the above embodiment 1.

[0156] A heterogeneous multi-agent command filtering fuzzy adaptive constraint control system includes the following modules:

[0157] The model building module simplifies the dynamics models of drones and unmanned vehicles for a multi-drone-multi-unmanned vehicle system consisting of a leader and multiple followers. Through transformation, a dynamics model of a multi-drone-multi-unmanned vehicle formation system that takes into account external interference and unknown parameters is constructed.

[0158] The controller design module designs a command filter fuzzy adaptive controller based on state constraints for the multi-UAV-multi-UAV system, so that all signals in the closed-loop system are bounded in finite time and the output state variables are constrained within a given interval;

[0159] The stability analysis module is used to perform stability analysis on the command filter fuzzy adaptive controller based on state constraints. Based on the virtual control signal, the command filter fuzzy adaptive controller, the parameter update law and the error compensation signal, the Lyapunov stability theory is used to prove that all signals of the closed-loop system are semi-globally consistent and ultimately bounded, and that the output state variables of the UAV-UAV formation system are constrained within a given interval.

[0160] The tracking control module is used to realize the tracking control of the multi-UAV-multi-UAV formation system by using the command filtering fuzzy adaptive controller.

[0161] It should be noted that in the heterogeneous multi-agent command filtering fuzzy adaptive constraint control system, the implementation process of the functions and effects of each functional module is specifically described in the implementation process of the corresponding steps in the method in the above embodiment 1, and will not be repeated here.

[0162] Example 3

[0163] This embodiment 3 describes a computer device, which is used to implement the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method described in the above embodiment 1.

[0164] Specifically, the computer device includes a memory and one or more processors.

[0165] An executable code is stored in the memory, and when the processor executes the executable code, it is used to implement the steps of the above-mentioned heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method.

[0166] In this embodiment, the computer device is any device or apparatus with data processing capability, which will not be described in detail here.

[0167] Example 4

[0168] This embodiment 4 describes a computer-readable storage medium, which is used to implement the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method described in the above embodiment 1.

[0169] Specifically, the computer-readable storage medium in this embodiment 4 stores a program thereon, which, when executed by a processor, is used to implement the steps of the above-mentioned heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method.

[0170] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card, etc. equipped on the device.

[0171] Of course, the above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiments. It should be noted that all equivalent substitutions and obvious deformation forms made by any technician familiar with the field under the guidance of this specification fall within the essential scope of this specification and should be protected by the present invention.

Claims

1. A heterogeneous multi-agent command filtering fuzzy adaptive constraint control method, characterized in that: The steps include: Step 1. For a UAV-UAV formation system consisting of a leader and multiple followers, i.e., a heterogeneous multi-agent system, a dynamic model of each follower considering external interference and unknown parameters is given, and the dynamic model of each follower is simplified; Step 2. For a heterogeneous multi-agent system consisting of multiple drones and multiple unmanned vehicles, transform the heterogeneous drone and unmanned vehicle models into a unified second-order nonlinear model through model conversion; Step 3. Design a state-constrained heterogeneous multi-agent command filter fuzzy adaptive controller for the heterogeneous multi-agent system. While ensuring that the formation follows the leader in the OXY plane and altitude, the stability of the formation attitude subsystem is considered to ensure that the UAV flies according to the predetermined attitude and altitude. The output state of the heterogeneous multi-agent system is constrained by introducing the obstacle Lyapunov function to ensure that the position and attitude of each agent remain in the safe area. In the backstepping design process, command filtering is introduced to avoid computational complexity problems and eliminate the influence of filtering errors by constructing an error compensation mechanism. Step 4. Use the command filtering fuzzy adaptive controller to realize the tracking control of the multi-UAV-multi-UAV formation system.

2. The heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method according to claim 1 is characterized in that: The step 1 is specifically as follows: The UAV dynamics model in the UAV-UAV formation system is shown in formula (1): Among them, (x i ,y i ,z i ) is the position of the i-th UAV in the ground coordinate system, φ i ,θ i , i Respectively represent the roll angle, pitch angle and yaw angle of the i-th UAV, i = 1, 2, 3, ..., N1, N1 represents the number of UAVs; m i is the mass of the i-th UAV; g is the acceleration due to gravity; (J i,x ,J i,y ,J i,z ) are the rotational inertia of the drone in the x, y, and z directions respectively; G i,x , G i,y , G i,z , G i,φ , G i,θ , G i,ψ Respectively represent the air resistance coefficient of the i-th UAV in the x direction, y direction, z direction, roll angle, pitch angle and yaw angle; d i,x d i,y d i,z d i,φ d i,θ d i,ψ They represent the unknown external disturbances of the i-th UAV in the x direction, y direction, z direction, roll angle, pitch angle and yaw angle respectively; T i Represents the total thrust of the drone, U i,φ , U i,θ , U i,ψ They represent the control torques of the i-th UAV to control the roll angle, pitch angle and yaw angle respectively; The UAV dynamics model is divided into a position dynamics model and an attitude dynamics model. When designing the position subsystem, the UAV position dynamics model is expressed as: Among them, u xi 、u yi 、u zi As the control input of the drone in the x-axis, y-axis, and z-axis directions, f xi 、f yi 、f zi represents the nonlinear term in the i-th UAV; you xi =(cosφ i sinth i cosψ i +sinφ i sinψ i )T i ; you yi =(cosφ i sinth i sinψ i +sinφ i sinψ i )T i u zi =(cosθ i cosφ i )T i ; The unmanned vehicle dynamics model in the UAV-unmanned vehicle formation system is shown in formula (3): Among them, p xn and p yn represents the position of the nth unmanned vehicle, v n and w n Respectively represent the linear velocity and angular velocity of the nth unmanned vehicle, θ n is the direction angle of the nth unmanned vehicle; Due to the nonholonomic constraints Define the coordinates (x hn ,y hn ) as a reference point, (x n ,y n ) is the inertial position of the unmanned vehicle, and the position of the unmanned vehicle is redefined as: Then, taking the second-order derivative we get: Among them, u xn and u yn As the new control input of the reference point, f xn and f yn is the lumped nonlinear function, d xn and d yn As the unknown disturbance of the unmanned vehicle, L n Indicates the distance from the inertial position of the unmanned vehicle to the reference point; 3. The heterogeneous multi-agent command filtering fuzzy adaptive constraint control method according to claim 2 is characterized in that: The step 2 is specifically as follows: According to formula (2) and formula (4), the control model of the UAV-UAV formation system is uniformly expressed on the OXY two-dimensional plane as a second-order nonlinear model as shown in formula (5): Among them, x ij1 Represents the position signal, x ij2 Indicates speed signal; w i represents the input signal, w i =B i u i ; where u i represents control input; Where I represents a unit vector, i∈N and satisfies N=N1∪N2; When i∈N1, it represents the i-th UAV; when i∈N2, it represents the i-th unmanned vehicle; j=1,2,3,4,5,6, when j=1, j=2, j=3, j=4, j=5, j=6, it represents the z, x, y, φ, θ, ψ directions respectively; F i,j is the nonlinear unknown term of the system model, d i,j The external disturbances to the UAV and the unmanned vehicle.

4. The heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method according to claim 3 is characterized in that: The step 3 is specifically as follows: According to graph theory knowledge, a graph G is used to represent the communication relationship between each drone; Among them, G = (ν, J) is composed of the vertex set ν = {1, 2, ..., N1} and the edge set J∈ν×ν; (k, i)∈J means that node i can receive information from node k; Note B i ={k∈ν:(k,i)∈J,k≠i} is the neighborhood of the i-th node, and the adjacency matrix A of the graph G = [a i,k ]∈R n×n , a i,k represents the communication relationship coefficient between followers; let the diagonal matrix B = diag{b1,b2,...,b i } to characterize the information acquisition of each follower to the leader, b i is the communication relationship coefficient between the follower and the leader; The given leader signal is continuous and differentiable; the external disturbance d i,j is bounded and satisfies |d i,j |≤D i,j , D i,j >0, where D i,j Indicates d i,j The modulus of If f(x) is a continuous function defined in a compact set γ, there exists a fuzzy logic system M i,j T S(x), satisfies sup x∈γ |f(x)-M i,j T S(x)|≤ε, where ε is a positive constant, M i,j is the fuzzy weight vector, S(x) is the fuzzy basis function, Where τ=1,2,...,n, n is a constant, l τ 、j τ Represent the width and center vector of the Gaussian function respectively; According to Young's inequality, we get: Among them, v ij2 represents the compensation tracking error, S i,j represents the basis function vector, ρ i,j represents a constant value greater than zero, Indicates the preset accuracy, Indicates the maximum value of the disturbance modulus; Based on the multi-UAV-multi-UAV dynamics model shown in formula (5), a state-constrained command filter fuzzy adaptive controller is designed. According to graph theory, the tracking error of the ith follower is defined as: Among them, Z ij1 , Z ij2 represents the tracking error variable, Y i,j represents the distance of the ith agent relative to the leader, Y k,j represents the distance between the kth agent and the ith agent, h k,j represents the offset of the kth agent relative to the ith agent; R j Represents the leader signal, h i,j represents the expected formation offset, i = 1, 2, ..., N, j = 1, 2, 3, 4, 5, 6, x ij1,c is the output signal of the command filter, which is defined as follows: in, is the output signal of the command filter; α1 is the input signal of the command filter; If the input signal α1 satisfies at t≥0, Then for any μ>0, there must exist ω n >0,ξ∈(0,1] such that Stablize; in express The initial value of α1(0) represents the initial value of α1. express The initial value of , t represents time, ρ1 and ρ2 are positive numbers; The compensation tracking error is defined as shown in formula (9): Among them, v ij1 、v ij2 To compensate for the tracking error, is the error compensation signal; The designed error compensation signal is as follows: Among them, k1 and k2 are both positive numbers, and k1=k ij1 , k2=k ij2 , k ij1 , k ij2 represents the control gain, k ij1 , k ij2 are all constant values, Design the following virtual control signal α1 and indirect control signal ω i : Among them, x kj2 Represents x kj1 The derivative of k b represents the control gain; Design parameter update law As shown in formula (14): Among them, r1>0, h i >0 is a parameter; Due to the underactuated and strongly coupled characteristics of the drone, the desired signal R φ , R θ Obtained by inverse solution of the control input of the position subsystem and the desired yaw angle; R φ , R θ The formula obtained by inversely solving the control input of the position subsystem and the desired yaw angle is as follows: Among them, w i,1 represents the indirect control signal of the i-th UAV in the z direction, w i,2 represents the indirect control signal of the ith UAV in the x direction, w i,3 Represents the indirect control signal of the y direction of the i-th UAV; Select the drone as the leader and define the leader signal as: (R1,R2,R3,R4,R5,R6)=(R z ,R x ,R y ,R φ ,R θ ,R ψ ); Define the formation tracking error E i,j for: E i,j =Y i,j -R j -h i,j ; Among them, (h i,1 ,h i,2 ,h i,3 ,h i,4 ,h i,5 ,h i,6 )=(h i,z ,h i,x ,h i,y ,h i,φ ,h i,θ ,h i,ψ ) is the expected offset of the ith follower relative to the leader; h i,z 、h i,x 、h i,y 、h i,φ 、h i,θ 、h i,ψ are the desired formation offsets of the ith agent in the x-direction, y-direction, z-direction, roll angle, pitch angle, and yaw angle, respectively; The command filter fuzzy adaptive controller based on state constraints is designed as follows:

5. The heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method according to claim 1 is characterized in that: In the step 3, after the design of the heterogeneous multi-agent command filter fuzzy adaptive controller based on state constraints is completed, the stability analysis of the heterogeneous multi-agent command filter fuzzy adaptive controller with state constraints is further performed, that is, based on the designed virtual control signal, heterogeneous multi-agent command filter fuzzy adaptive controller, parameter update law and error compensation signal, the Lyapunov stability theory is used to prove that the closed-loop system is semi-globally consistent and ultimately bounded, and the output state constraints are within a given field.

6. The heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method according to claim 4 is characterized in that: In step 3, the process of stability analysis of the heterogeneous multi-agent command filtering fuzzy adaptive controller based on state constraints is as follows: Select the barrier Lyapunov function Taking the derivative of it, we get: Select the Lyapunov function The derivative is: |x ij1,c Substituting -α1|≤μ into formula (18), we get: Select the Lyapunov function Taking the derivative of it, we get: Where θ=max{||M i,j || 2 }, represents the estimated value of θ; Select the Lyapunov function C = C1 + C2 and take its derivative to get: Select the Lyapunov function Taking the derivative of it, we get: According to Young's inequality, we get: Substituting formula (23) and formula (24) into formula (22), we obtain: in: a=min{2k v1 ,2k1,2k1-1,2k2,m i }; Integrating formula (25) at (0, t] yields: Where V(0) represents the initial value of V; Then get because in express The boundary of so Because X ij1 ≤|Z ij1 |+|R i,j +h i,j |, and |R i,j +h i,j |≤Y0, so Where X ij1 represents the position of the ith follower, R i,j represents the distance between the leader and the follower, Y0 and k cl represents a constant value; for v ij2 , From -aV+b, in the interval (0, t], we have: V≤(V(0)-b / a)e -at +b / a≤V(0)+b / a; So ij2 , All belong to And the system output state variables are constrained to be x ={|X ij1 |≤k cl }; therefore, the closed-loop system is semi-globally consistently ultimately bounded, and the output state variables of the UAV-UV platooning system are constrained within a given domain.

7. The heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method according to claim 1 is characterized in that: The step 4 is specifically as follows: Firstly, the fuzzy adaptive technology is used to deal with the uncertainties in the heterogeneous multi-agent system. The tracking error of each agent in the UAV-UAV formation is defined according to the expected signal and the expected formation offset. The UAV is divided into a position subsystem and an altitude and attitude subsystem. Then, the position subsystem model of the UAV and the position subsystem model of the UAV are transformed into a unified second-order nonlinear model, and the position controller of the UAV-UAV formation is designed. At the same time, since the UAV is a typical under-actuated system, the expected signals of its pitch angle and roll angle are obtained by inverse solution of the control input of the position subsystem and the expected yaw angle. For the unique altitude and attitude subsystem of the UAV, a separate controller is designed. Secondly, the obstacle Lyapunov function is introduced in the position subsystem, altitude subsystem and attitude subsystem respectively to limit the output state of each agent in the heterogeneous multi-agent formation to the predetermined constraint boundary. The designed state-constrained command filtering fuzzy adaptive controller is used to realize the tracking control of the multi-UAV-UAV formation system.

8. A heterogeneous multi-agent command filtering fuzzy adaptive constraint control system, characterized in that: Includes the following modules: The model building module simplifies the dynamics models of drones and unmanned vehicles for a multi-drone-multi-unmanned vehicle system consisting of a leader and multiple followers. Through transformation, a dynamics model of a multi-drone-multi-unmanned vehicle formation system that takes into account external interference and unknown parameters is constructed. The controller design module designs a command filter fuzzy adaptive controller based on state constraints for the multi-UAV-multi-UAV system, so that all signals in the closed-loop system are bounded in finite time and the output state variables are constrained within a given interval; The stability analysis module is used to perform stability analysis on the command filter fuzzy adaptive controller based on state constraints. Based on the virtual control signal, the command filter fuzzy adaptive controller, the parameter update law and the error compensation signal, the Lyapunov stability theory is used to prove that all signals of the closed-loop system are semi-globally consistent and ultimately bounded, and that the output state variables of the UAV-UAV formation system are constrained within a given interval. The tracking control module is used to realize the tracking control of the multi-UAV-multi-UAV formation system by using the command filtering fuzzy adaptive controller.

9. A computer device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, the steps of the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the heterogeneous multi-agent instruction filtering fuzzy adaptive constraint control method as described in any one of claims 1 to 7 are implemented.

Images