People-unmanned aerial vehicle cluster system based on clustering emergence and buffeting-free fault-tolerant control

Abstract

The invention relates to a manned-unmanned aerial vehicle cluster system based on clustering emergence and buffeting-free fault-tolerant control, and belongs to the technical field of fault tolerance of unmanned aerial vehicle clusters. Comprising a leading unmanned aerial vehicle, a following unmanned aerial vehicle, an observer and a controller, and the manned-unmanned aerial vehicle cluster is divided into a plurality of clusters including the only leading unmanned aerial vehicle and the only following unmanned aerial vehicle; the observer is used for estimating the leader unmanned aerial vehicle information for the following unmanned aerial vehicles which cannot directly obtain the leader unmanned aerial vehicle information in each cluster according to the leader unmanned aerial vehicle information in the cluster obtained by traction nodes in each cluster and by utilizing network interaction information among the unmanned aerial vehicles, and the leader unmanned aerial vehicle information serves as target information of the following unmanned aerial vehicles to carry out multi-target clustering emergence control; and the controller is used for tracking the target information estimated by the observer and carrying out fault-tolerant control on the following unmanned aerial vehicle. According to the method, distributed buffeting-free fault tolerance and multi-target clustering emergence of the manned-unmanned aerial vehicle cluster are realized, and high performance and safety of manned-unmanned aerial vehicle cluster operation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fault tolerance of unmanned aerial vehicle clusters, and in particular to a manned-unmanned aerial vehicle cluster system based on cluster emergence and chatter-free fault-tolerant control. Background Art

[0002] In recent years, drones have been widely used in emergency rescue, aerial photography, plant protection, power inspection, geological exploration, film and television shooting and other fields. Compared with a single drone, a drone cluster completes various tasks through communication and coordination between drones, and has higher application and research value. For the control of drone clusters, manned-drone cluster control is an effective control method, which guides the follower drones to achieve the control target by controlling the leader drone by the operator. This manned-drone cluster control algorithm has strong robustness and can effectively avoid accidents. On the other hand, emergent control is an effective method for dealing with complex systems by adjusting local behaviors to achieve the expected overall behavior. The clustered multi-target emergence of manned-drone clusters is to divide the drone cluster into different clusters, and use the drone controlled by the operator to tow some drones to achieve the synchronization of the postures of drones in the same cluster and different postures of drones in different clusters.

[0003] In addition, the rotor of the drone may experience physical failure, which manifests as excessive or insufficient torque input to the drone, as well as torque reverse failure; the failure may cause the performance of a single drone to degrade, or even cause the drone to crash or even endanger the safe operation of the entire drone cluster. At present, there is still a jitter phenomenon in the fault-tolerant control of physical faults. This jitter phenomenon cannot be ignored in the actual application of drones, so it is necessary to design a jitter-free controller to suppress the occurrence of jitter phenomenon during fault-tolerant control. Summary of the invention

[0004] In view of the above analysis, the present invention aims to disclose a manned-UAV cluster system based on cluster emergence and jitter-free fault-tolerant control; while realizing the cluster emergence of the manned-UAV cluster, it realizes the fault-tolerant control of the UAV rotors and avoids the UAV jitter problem, thereby ensuring the high performance and safety of the manned-UAV cluster.

[0005] The present invention discloses a manned-unmanned aerial vehicle cluster system based on cluster emergence and chatter-free fault-tolerant control, comprising a leading unmanned aerial vehicle, a following unmanned aerial vehicle, an observer and a controller; wherein:

[0006] The manned-unmanned aerial vehicle cluster is divided into multiple clusters, each of which includes a leading drone and multiple follower drones; the leading drone is controlled by a leader, and the follower drones track the leading drone in the cluster;

[0007] The observer is a distributed preset performance observer; it is used to estimate the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the traction node in each cluster and the conversion error information of the cluster synchronization error interacted by the inter-drone communication network, and use the leader drone information as its target information to perform multi-target cluster emergence control;

[0008] The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the following UAV.

[0009] Further, the traction node is determined according to the communication topology structure between the following drones; wherein,

[0010] The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3)

[0011] Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i -1,…,s; s is the number of leading drones;

[0012] The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

[0013] Furthermore, the leading UAV and the following UAV in the manned-UAV cluster are both rotor UAVs; dynamic models of the attitude system are established for the leading UAV and the following UAV respectively; wherein,

[0014] The dynamic model of the attitude system of the leader UAV is:

[0015]

[0016] in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone belongs; represents the output of the leader drone; It represents the leader’s operational input to the leader UAV; represents a nonlinear term;

[0017] The output of the leader drone does not have mutations; it has constants make Established;

[0018] The dynamic model of the attitude system of the i-th following drone is:

[0019]

[0020] Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocities of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; i,p Represents the output signal; f i,p represents a nonlinear term; Indicates inputs for following drones including fault conditions.

[0021] Furthermore, the control link between the following UAV and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following UAV track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail;

[0022] Then, the actuator is modeled to obtain the output of the actuator including the fault condition for:

[0023]

[0024] Among them, u i,p Represents the normal input signal to the actuator; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p > 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0; r i,p Indicates a bias fault.

[0025] Furthermore, the distributed preset performance observer is a first-order integral structure, which can be expressed as:

[0026]

[0027] Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster;

[0028] Input signal u at time ti The calculation expression is:

[0029]

[0030] Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of , L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j and μ 2j are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering time, δ j satisfy is δ j The estimated value of .

[0031] Furthermore, in the observer input of the i-th following drone, the conversion error i,p ,p=1,2,3The calculation formula is:

[0032]

[0033] Among them, a ij 、a im is the element of the adjacency matrix that follows the UAV cluster communication topology; is the output vector of the leader drone; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones in other clusters and receive information;

[0034] b i ∈{0,1} is the traction gain. When the i-th following drone is the traction node, b i =1, otherwise, b i =0; is the set of neighbor nodes following drone i; ∩ represents the intersection of two sets;

[0035]

[0036] Among them, γ i (t) = [γ i,1 ,γ i,2 ,γ i,3 ] TUsed to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol It means that the elements of two vectors are multiplied respectively, a1 is the set safety constant, and β is the set safety margin;

[0037] Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p , μ 2j,p , the calculation formulas for p=1,2,3 are:

[0038]

[0039] in, The p-th element of .

[0040] Furthermore, the triggering condition for the interaction of the communication network between the following drones is that the change between the conversion error at the current moment and the conversion error at the most recent triggering moment is not less than the triggering threshold;

[0041] The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

[0042] Furthermore, the distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse faults through the preset performance method;

[0043] Then, the distributed preset performance fault-tolerant controller without chattering is expressed as:

[0044]

[0045] Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3.

[0046] Furthermore, the Nussbaum function It is expressed as:

[0047]

[0048] Where σ1 and σ2 are the parameters for adjusting the amplitude and frequency of the Nussbaum function respectively;

[0049] Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows:

[0050]

[0051] In the formula, q i,p is a positive constant gain, The output signal of the drone angular velocity and the first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p is the boundary distance for adjusting the error, l i is a constant gain, a 2i,p is the positive safety constant of the design;

[0052] is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T is the input of the neural network, is the radial basis function vector.

[0053] Furthermore, the calculation formula of the radial basis function vector is:

[0054]

[0055] Among them, M1 is the number of neurons, o m and π m represent the center and width of the neuron, respectively;

[0056] The conversion error z i,p1 , the boundary distance of adjustment error κ i,p , conversion function h(κ i,p The calculation formulas for (t) are:

[0057]

[0058] Among them, e i,p The adjustment error of the UAV output signal;

[0059] The first-order filter is expressed as:

[0060]

[0061] Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is:

[0062]

[0063] In the formula, Ω i,p is an auxiliary variable, and its calculation formula is:

[0064]

[0065] in, is an intermediate variable;

[0066] Adaptive Gain The update law is:

[0067]

[0068] Among them, r i,p1 and r i,p2 is a positive constant gain.

[0069] The manned-unmanned aerial vehicle cluster system for distributed chatter-free fault-tolerant control disclosed in the present invention can achieve the following beneficial effects:

[0070] 1) The control strategy designed in the present invention does not require prior knowledge of the initial conditions of the drone, and the designed observer is a first-order integral structure, which simplifies the design difficulty and computational complexity of the observer. In addition, the control strategy of the present invention can autonomously set the system convergence time and convergence accuracy as needed, thereby improving the performance of the manned-drone cluster under fault conditions.

[0071] 2) The present invention can realize distributed, jitter-free, fault-tolerant, multi-target clustering emergence control of manned-UAV clusters; the leading UAV influences the traction node to achieve multi-target clustering emergence of the entire UAV cluster.

[0072] 3) The present invention utilizes a preset performance method to suppress the state chattering phenomenon that occurs when the Nussbaum function method solves the reverse fault, ensuring that the output signal of the following drone is a smooth signal, thereby improving the performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0074] Figure 1 The figure is a schematic diagram of the connection components of a manned-unmanned aerial vehicle cluster system for performing distributed chatter-free fault-tolerant control in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0076] One embodiment of the present invention discloses a manned-unmanned aerial vehicle cluster system for distributed chatter-free fault-tolerant control, such as Figure 1 As shown, it includes a leading UAV, a following UAV, an observer and a controller; wherein,

[0077] The manned-unmanned aerial vehicle cluster is divided into multiple clusters, each of which includes a leading drone and multiple follower drones; the leading drone is controlled by a leader, and the follower drones track the leading drone in the cluster;

[0078] The observer is a distributed preset performance observer; it is used to estimate the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the traction node in each cluster and the conversion error information of the cluster synchronization error interacted by the inter-drone communication network, and use the leader drone information as its target information to perform multi-target cluster emergence control;

[0079] The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the follower UAV; including chatter-free fault-tolerant control for reverse faults.

[0080] Specifically, the traction node is determined according to the communication topology structure between the following drones; wherein,

[0081] The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3)

[0082] Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i -1,…,s; s is the number of leading drones;

[0083] The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

[0084] Specifically, the leading UAV and the following UAV in the manned-UAV cluster are both rotor UAVs; dynamic models of the attitude system are established for the leading UAV and the following UAV respectively;

[0085] The dynamic model of the attitude system of the leader UAV is:

[0086]

[0087] in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone belongs; represents the output of the leader drone; It represents the operational input of the leader; represents a nonlinear term;

[0088] The output of the leader drone does not have mutations; it has constants make Establishment; constant The symbol “—” indicates upper limit.

[0089] The dynamic model of the attitude system of the i-th following drone is:

[0090]

[0091] Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocities of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; i,p Represents the output signal; f i,p represents a nonlinear term; Indicates input including fault conditions.

[0092] The control link between the following UAV and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following UAV track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail;

[0093] Then, the actuator is modeled to obtain the output of the actuator including the fault condition for:

[0094]

[0095] Among them, u i,p Represents the normal input signal of the attitude system design of the following drone; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p> 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0; r i,p Indicates a bias fault.

[0096] The nonlinear term expressions in the dynamics models of the attitude systems of the leading UAV and the following UAV are the same; taking the nonlinear term of the following UAV as an example; the nonlinear term f i,p It is expressed as:

[0097]

[0098] Among them, J x , J y , J z Represents the moment of inertia in different directions, J r is the rotor inertia, I1, I2, I3 are the aerodynamic damping coefficients, Ω r is the residual angular velocity of all motors of the rotorcraft. In addition, the topological graph of the N following drones contains a directed spanning tree.

[0099] The distributed preset performance observer in this embodiment is a first-order integral structure, which simplifies the design difficulty and calculation complexity of the observer.

[0100] The expression of the distributed preset performance observer is:

[0101]

[0102] Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster;

[0103] The signal u input to the distributed preset performance observer at time t i The calculation expression is:

[0104]

[0105] Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of , L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j =diag{μ 1j,1 ,μ 1j,2 ,μ 1j,3}、μ 2j =[μ 2j,1 ,μ 2j,2 ,μ2j,3 ] T are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering time, ∈ j =[∈ j,1 ,∈ j,2 ,∈ j,3 ] T , δ j =[δ j,1 ,δ j,2 ,δ j,3 ] T satisfy is δ j The subscripts 1, 2, and 3 in the formula represent the roll, pitch, and yaw channels, respectively.

[0106] More specifically, in the observer input of the i-th following drone, the conversion error of the cluster synchronization error ∈ i,p ,p=1,2,3The calculation formula is:

[0107]

[0108] Among them, a ij 、a im is the element of the adjacency matrix that follows the UAV cluster communication topology; is the output vector of the leader drone; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones in other clusters and receive information;

[0109] b i ∈{0,1} is the traction gain, which constitutes the traction matrix B=diag{b1,…,b N}, when the i-th following drone is a towing node, b i =1, otherwise, b i =0; is the set of neighbor nodes of drone i; ∩ represents the intersection of two sets. In addition:

[0110]

[0111]

[0112] Among them, γi (t) = [γ i,1 ,γ i,2 ,γ i,3 ] T Used to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol It means that the elements of two vectors are multiplied respectively, a1 is the set safety constant, and β is the set safety margin;

[0113] The calculation formula of the safety margin is as follows:

[0114]

[0115] Among them, T is the designed convergence time, and ε is the designed convergence accuracy.

[0116] Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p , μ 2j,p , the calculation formulas for p=1,2,3 are:

[0117]

[0118] in, for The p-th element of .

[0119] Specifically, the following drones conduct network interaction between drones through triggering to obtain the conversion error information of the cluster synchronization error; the triggering condition for the communication network interaction between the following drones is that the change between the conversion error at the current moment and the conversion error at the most recent trigger moment is not less than the trigger threshold;

[0120] The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

[0121] More specifically, the event triggering conditions for the communication between drones are:

[0122]

[0123] in, is the difference between the conversion error at the current moment and the conversion error at the most recent trigger moment, η j is a positive constant trigger threshold, sup{} represents the supremum of the variable, min{} represents the minimum value of the set, and λ max () represents the maximum eigenvalue of the matrix.

[0124] When verifying the performance of the observer,

[0125] Define observation error:

[0126]

[0127] Taking the derivative of the observation error, we can obtain:

[0128]

[0129] Furthermore, the compact form of N following drones can be expressed as:

[0130]

[0131] in, ∈=col{∈1,...,∈ N},∈ i =[∈ i,1 ,∈ i,2 ,∈ i,3 ] T ,μ1=diag{μ 11 ,...,μ 1N},μ 1i =diag{μ 1i,1 ,μ 1i,2 ,μ 1i,3},μ2=diag{μ 21 ,...,μ 2N},μ 2i =[μ 2i,1 ,μ 2i,2 ,μ 2i,3 ] T ; Ψ=col{Ψ1,…,Ψ N},Ψ i =[Ψ i,1 ,Ψ i,2 ,Ψ i,3 ] T .

[0132] Based on (9) and (16), we can obtain:

[0133]

[0134] in,

[0135] Taking the derivative of (8), its compact form can be expressed as:

[0136]

[0137] Similarly, the compact form of (7) is:

[0138]

[0139] in,

[0140] Consider a Lyapunov function of the following form:

[0141]

[0142] Taking the derivative of (22) we can get:

[0143]

[0144] Where δ=col{δ1,...,δ N} and c3δ T δ / 2 is a constant.

[0145] Considering the event triggering condition (15), (23) can be expressed as:

[0146]

[0147] in,

[0148] Therefore, V o (t),∈ i,p as well as If is bounded, then the designed observer (5) can estimate the output of the leading UAV and the observation error converges to the preset accuracy within the preset time.

[0149] Specifically, the distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse faults through the preset performance method;

[0150] Then, the distributed preset performance fault-tolerant controller without chattering is expressed as:

[0151]

[0152] Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3.

[0153] More specifically, the Nussbaum function It is expressed as:

[0154]

[0155] Where σ1 and σ2 are parameters for adjusting the amplitude and frequency of the Nussbaum function, respectively.

[0156] Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows:

[0157]

[0158] In the formula, q i,p is a positive constant gain, The output signal of the drone angular velocity and the first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p is the boundary distance for adjusting the error, l i is a constant gain, a 2i,p is the positive safety constant of the design, is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T represents the input of the neural network, is the radial basis function vector, and its calculation formula is:

[0159]

[0160] Among them, M1 is the number of neurons, o m and π m Represent the center and width of the neuron respectively.

[0161] Conversion error z i,p1 , the boundary distance of adjustment error κ i,p , conversion function h(κ i,p The calculation formula of (t)) is:

[0162]

[0163] e i,p =y i,p -ξ i,p (31)

[0164]

[0165]

[0166] Among them, e i,p is the adjustment error of the drone output signal.

[0167] The first-order filter is expressed as:

[0168]

[0169] Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is:

[0170]

[0171] In the formula, Ω i,p is an auxiliary variable, and its calculation formula is:

[0172]

[0173] in, is an intermediate variable.

[0174] Adaptive Gain The update law is

[0175]

[0176] Among them, r i,p1 and r i,p2 is a positive constant gain.

[0177] The above controller is designed based on the backstepping method. First, consider the following form of Lyapunov function:

[0178]

[0179] Combining (1), (30)-(33), (35), taking the derivative of (39) and using Young's inequality, we can obtain:

[0180]

[0181] in,

[0182] Further consider the following Lyapunov function:

[0183]

[0184] in, yes Estimates.

[0185] Combining (25)-(28), the derivative of (41) is expressed as:

[0186]

[0187] Substituting (38) into (42), we obtain the following inequality:

[0188]

[0189] in, and is a constant.

[0190] Integrating both sides of (43), we obtain:

[0191]

[0192] in, is a constant. Then V i,p (t),υ i,p (t) and Therefore, the adjustment error of the UAV converges to the preset accuracy within the preset time, thus achieving fault-tolerant control under reverse faults.

[0193] The stability analysis shows that the chattering-free preset performance fault-tolerant control method of the designed manned-UAV cluster is effective. It can effectively solve the chattering problem caused by the Nussbaum function method under reverse faults and accurately identify the time of the reverse fault and the channel where the fault is located, thereby improving the robustness of the manned-UAV cluster and ensuring the safe and stable operation of the system.

[0194] In summary, this embodiment realizes the clustering emergence of manned-UAV clusters while achieving fault-tolerant control of UAV rotors and avoiding the UAV vibration problem, thereby ensuring the high performance and safety of the manned-UAV clusters.

[0195] In a more preferred solution, the reverse fault of the following drone is judged by setting a fault direction identification index; the fault direction identification index is determined by the sign of the last few sampling values ​​of the Nussbaum function in the controller. When the signs of the last few sampling values ​​are the same, it indicates that there is no reverse fault. When the signs of the last few sampling values ​​are opposite, it indicates that there is a reverse fault. When there is a reverse fault, an alarm is issued. By alarming the reverse fault, the fault direction is identified, and the reverse fault is located.

[0196] It is preferred to use the last two sampling values ​​to determine the fault direction identification index. If there is a sampling value whose sign cannot be determined in the last two sampling values, the sampling value before the last two sampling values ​​is added. The reverse fault is determined by using whether the two sampling values ​​that can be used to determine the sign have opposite signs in three consecutive sampling values, thereby improving the robustness of the reverse fault.

[0197] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control, characterized in that: It includes a leading UAV, a following UAV, an observer and a controller; among which, The manned-unmanned aerial vehicle cluster is divided into multiple clusters, each of which includes a leading drone and multiple follower drones; the leading drone is controlled by a leader, and the follower drones track the leading drone in the cluster; The observer is a distributed preset performance observer; it is used to estimate the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the traction node in each cluster and the conversion error information of the cluster synchronization error interacted by the inter-drone communication network, and use the leader drone information as its target information to perform multi-target cluster emergence control; The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the following UAV.

2. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 1 is characterized in that: The traction node is determined according to the communication topology between the following drones; The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3) Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i -1,…,s; s is the number of leading drones; The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

3. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 2 is characterized in that: The leading UAV and the following UAV in the manned-UAV cluster are both rotor UAVs. The dynamic models of the attitude systems of the leading UAV and the following UAV are established respectively. The dynamic model of the attitude system of the leader UAV is: in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone is located; represents the output of the leader drone; It represents the leader’s operational input to the leader UAV; represents a nonlinear term; The output of the leader drone does not have mutations; it has constants make Established; The dynamic model of the attitude system of the i-th following drone is: Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocities of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; i,p Represents the output signal; f i,p represents a nonlinear term; Indicates inputs for following drones including fault conditions.

4. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 3 is characterized in that: The control link between the following UAV and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following UAV track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail; Then, the actuator is modeled to obtain the output of the actuator including the fault condition for: Among them, u i,p Represents the normal input signal to the actuator; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p > 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0; r i,p Indicates a bias fault.

5. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 4 is characterized in that: The distributed preset performance observer is a first-order integral structure, expressed as: Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster; Input signal u at time t i The calculation expression is: Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of , L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j and μ 2j are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering time, δ j satisfy is δ j The estimated value of .

6. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 5 is characterized in that: In the input of the observer of the i-th following drone, the conversion error ∈ i,p ,p=1,2,3The calculation formula is: Among them, a ij 、a im is the element of the adjacency matrix that follows the UAV cluster communication topology; is the output vector of the leader drone; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones in other clusters and receive information; b i ∈{0,1} is the traction gain. When the i-th following drone is the traction node, b i =1, otherwise, b i =0; is the set of neighbor nodes following drone i; ∩ represents the intersection of two sets; Among them, γ i (t) = [γ i,1 ,γ i,2 ,γ i,3 ] T Used to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol It means that the elements of two vectors are multiplied respectively, a1 is the set safety constant, and β is the set safety margin; Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p , μ 2j,p , the calculation formulas for p=1,2,3 are: in, for The p-th element of .

7. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 6 is characterized in that: The trigger condition for the interaction of the communication network between the following drones is that the change between the conversion error at the current moment and the conversion error at the most recent trigger moment is not less than the trigger threshold; The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

8. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 2 is characterized in that: The distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse faults through the preset performance method; Then, the distributed preset performance fault-tolerant controller without chattering is expressed as: Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3.

9. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 8, characterized in that: Nussbaum function It is expressed as: Where σ1 and σ2 are the parameters for adjusting the amplitude and frequency of the Nussbaum function respectively; Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows: In the formula, q i,p is a positive constant gain, The output signal of the drone angular velocity and the first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p To adjust the error boundary distance, is a constant gain, a 2i,p is the positive safety constant of the design; is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T is the input of the neural network, is the radial basis function vector.

10. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 9, characterized in that: The calculation formula of the radial basis function vector is: Among them, M1 is the number of neurons, o m and π m represent the center and width of the neuron, respectively; The conversion error z i,p1 , the boundary distance of adjustment error κ i,p , conversion function h(κ i,p The calculation formulas for (t) are: Among them, e i,p The adjustment error of the UAV output signal; The first-order filter is expressed as: Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is: In the formula, Ω i,p is an auxiliary variable, and its calculation formula is: in, is an intermediate variable; Adaptive Gain The update law is: Among them, r i,p1 and r i,p2 is a positive constant gain.

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